diff options
| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2017-01-24 16:09:02 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-01-24 16:23:35 -0800 |
| commit | 4ac5ed3184873d530dc249a74794a55229e85e0f (patch) | |
| tree | 55d5a0eb7f7af59db04980c3055f0a7efef2a2de | |
| parent | 84867aa7353bda7335dd9976fe3ec3d7063255bd (diff) | |
Update generated Python Op docs.
Change: 145481207
54 files changed, 2914 insertions, 1944 deletions
diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md index fbf66370a0..c7d097fda5 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md +++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md @@ -153,7 +153,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Affine.forward(x, name='forward', **condition_kwargs)` {#Affine.forward} +#### `tf.contrib.distributions.bijector.Affine.forward(x, name='forward')` {#Affine.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -162,7 +162,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -198,7 +197,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Affine.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Affine.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Affine.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Affine.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -207,7 +206,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -273,7 +271,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Affine.inverse(y, name='inverse', **condition_kwargs)` {#Affine.inverse} +#### `tf.contrib.distributions.bijector.Affine.inverse(y, name='inverse')` {#Affine.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -282,7 +280,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -299,7 +296,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Affine.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Affine.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Affine.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Affine.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -313,7 +310,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -350,7 +346,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Affine.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Affine.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Affine.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Affine.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -363,7 +359,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -520,7 +515,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.AffineLinearOperator.forward(x, name='forward', **condition_kwargs)` {#AffineLinearOperator.forward} +#### `tf.contrib.distributions.bijector.AffineLinearOperator.forward(x, name='forward')` {#AffineLinearOperator.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -529,7 +524,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -565,7 +559,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.AffineLinearOperator.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#AffineLinearOperator.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.AffineLinearOperator.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#AffineLinearOperator.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -574,7 +568,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -640,7 +633,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse(y, name='inverse', **condition_kwargs)` {#AffineLinearOperator.inverse} +#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse(y, name='inverse')` {#AffineLinearOperator.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -649,7 +642,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -666,7 +658,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#AffineLinearOperator.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#AffineLinearOperator.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -680,7 +672,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -717,7 +708,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#AffineLinearOperator.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#AffineLinearOperator.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -730,7 +721,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1052,7 +1042,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Bijector.forward(x, name='forward', **condition_kwargs)` {#Bijector.forward} +#### `tf.contrib.distributions.bijector.Bijector.forward(x, name='forward')` {#Bijector.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -1061,7 +1051,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1097,7 +1086,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Bijector.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Bijector.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Bijector.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Bijector.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -1106,7 +1095,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1172,7 +1160,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse(y, name='inverse', **condition_kwargs)` {#Bijector.inverse} +#### `tf.contrib.distributions.bijector.Bijector.inverse(y, name='inverse')` {#Bijector.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -1181,7 +1169,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1198,7 +1185,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Bijector.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Bijector.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Bijector.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -1212,7 +1199,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1249,7 +1235,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Bijector.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Bijector.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Bijector.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -1262,7 +1248,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1385,7 +1370,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Chain.forward(x, name='forward', **condition_kwargs)` {#Chain.forward} +#### `tf.contrib.distributions.bijector.Chain.forward(x, name='forward')` {#Chain.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -1394,7 +1379,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1430,7 +1414,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Chain.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Chain.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Chain.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Chain.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -1439,7 +1423,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1505,7 +1488,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Chain.inverse(y, name='inverse', **condition_kwargs)` {#Chain.inverse} +#### `tf.contrib.distributions.bijector.Chain.inverse(y, name='inverse')` {#Chain.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -1514,7 +1497,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1531,7 +1513,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Chain.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Chain.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Chain.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Chain.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -1545,7 +1527,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1582,7 +1563,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Chain.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Chain.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Chain.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Chain.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -1595,7 +1576,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1695,7 +1675,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.forward(x, name='forward', **condition_kwargs)` {#CholeskyOuterProduct.forward} +#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.forward(x, name='forward')` {#CholeskyOuterProduct.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -1704,7 +1684,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1740,7 +1719,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#CholeskyOuterProduct.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#CholeskyOuterProduct.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -1749,7 +1728,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1815,7 +1793,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse(y, name='inverse', **condition_kwargs)` {#CholeskyOuterProduct.inverse} +#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse(y, name='inverse')` {#CholeskyOuterProduct.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -1824,7 +1802,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1841,7 +1818,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#CholeskyOuterProduct.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#CholeskyOuterProduct.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -1855,7 +1832,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1892,7 +1868,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#CholeskyOuterProduct.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#CholeskyOuterProduct.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -1905,7 +1881,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2002,7 +1977,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Exp.forward(x, name='forward', **condition_kwargs)` {#Exp.forward} +#### `tf.contrib.distributions.bijector.Exp.forward(x, name='forward')` {#Exp.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -2011,7 +1986,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2047,7 +2021,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Exp.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Exp.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Exp.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Exp.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -2056,7 +2030,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2122,7 +2095,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Exp.inverse(y, name='inverse', **condition_kwargs)` {#Exp.inverse} +#### `tf.contrib.distributions.bijector.Exp.inverse(y, name='inverse')` {#Exp.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -2131,7 +2104,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2148,7 +2120,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Exp.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Exp.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Exp.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Exp.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -2162,7 +2134,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2199,7 +2170,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Exp.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Exp.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Exp.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Exp.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -2212,7 +2183,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2301,7 +2271,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Identity.forward(x, name='forward', **condition_kwargs)` {#Identity.forward} +#### `tf.contrib.distributions.bijector.Identity.forward(x, name='forward')` {#Identity.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -2310,7 +2280,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2346,7 +2315,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Identity.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Identity.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Identity.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Identity.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -2355,7 +2324,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2421,7 +2389,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Identity.inverse(y, name='inverse', **condition_kwargs)` {#Identity.inverse} +#### `tf.contrib.distributions.bijector.Identity.inverse(y, name='inverse')` {#Identity.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -2430,7 +2398,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2447,7 +2414,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Identity.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Identity.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Identity.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Identity.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -2461,7 +2428,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2498,7 +2464,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Identity.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Identity.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Identity.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Identity.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -2511,7 +2477,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2618,7 +2583,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Inline.forward(x, name='forward', **condition_kwargs)` {#Inline.forward} +#### `tf.contrib.distributions.bijector.Inline.forward(x, name='forward')` {#Inline.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -2627,7 +2592,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2663,7 +2627,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Inline.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Inline.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Inline.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Inline.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -2672,7 +2636,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2738,7 +2701,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Inline.inverse(y, name='inverse', **condition_kwargs)` {#Inline.inverse} +#### `tf.contrib.distributions.bijector.Inline.inverse(y, name='inverse')` {#Inline.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -2747,7 +2710,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2764,7 +2726,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Inline.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Inline.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Inline.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Inline.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -2778,7 +2740,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2815,7 +2776,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Inline.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Inline.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Inline.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Inline.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -2828,7 +2789,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2905,8 +2865,8 @@ efficient if the base bijector implements `_forward_log_det_jacobian`. If used: ```python -y = self.inverse(x, **condition_kwargs) -return -self.inverse_log_det_jacobian(y, **condition_kwargs) +y = self.inverse(x, **kwargs) +return -self.inverse_log_det_jacobian(y, **kwargs) ``` ##### Args: @@ -2934,7 +2894,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Invert.forward(x, name='forward', **condition_kwargs)` {#Invert.forward} +#### `tf.contrib.distributions.bijector.Invert.forward(x, name='forward')` {#Invert.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -2943,7 +2903,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2979,7 +2938,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Invert.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Invert.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Invert.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Invert.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -2988,7 +2947,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3054,7 +3012,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Invert.inverse(y, name='inverse', **condition_kwargs)` {#Invert.inverse} +#### `tf.contrib.distributions.bijector.Invert.inverse(y, name='inverse')` {#Invert.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -3063,7 +3021,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3080,7 +3037,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Invert.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Invert.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Invert.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Invert.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -3094,7 +3051,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3131,7 +3087,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Invert.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Invert.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Invert.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Invert.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -3144,7 +3100,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3237,7 +3192,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.PowerTransform.forward(x, name='forward', **condition_kwargs)` {#PowerTransform.forward} +#### `tf.contrib.distributions.bijector.PowerTransform.forward(x, name='forward')` {#PowerTransform.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -3246,7 +3201,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3282,7 +3236,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.PowerTransform.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#PowerTransform.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.PowerTransform.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#PowerTransform.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -3291,7 +3245,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3357,7 +3310,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.PowerTransform.inverse(y, name='inverse', **condition_kwargs)` {#PowerTransform.inverse} +#### `tf.contrib.distributions.bijector.PowerTransform.inverse(y, name='inverse')` {#PowerTransform.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -3366,7 +3319,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3383,7 +3335,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.PowerTransform.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#PowerTransform.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.PowerTransform.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#PowerTransform.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -3397,7 +3349,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3434,7 +3385,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.PowerTransform.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#PowerTransform.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.PowerTransform.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#PowerTransform.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -3447,7 +3398,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3529,7 +3479,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.SigmoidCentered.forward(x, name='forward', **condition_kwargs)` {#SigmoidCentered.forward} +#### `tf.contrib.distributions.bijector.SigmoidCentered.forward(x, name='forward')` {#SigmoidCentered.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -3538,7 +3488,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3574,7 +3523,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.SigmoidCentered.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#SigmoidCentered.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SigmoidCentered.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#SigmoidCentered.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -3583,7 +3532,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3649,7 +3597,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse(y, name='inverse', **condition_kwargs)` {#SigmoidCentered.inverse} +#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse(y, name='inverse')` {#SigmoidCentered.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -3658,7 +3606,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3675,7 +3622,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#SigmoidCentered.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#SigmoidCentered.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -3689,7 +3636,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3726,7 +3672,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#SigmoidCentered.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#SigmoidCentered.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -3739,7 +3685,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3836,7 +3781,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.SoftmaxCentered.forward(x, name='forward', **condition_kwargs)` {#SoftmaxCentered.forward} +#### `tf.contrib.distributions.bijector.SoftmaxCentered.forward(x, name='forward')` {#SoftmaxCentered.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -3845,7 +3790,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3881,7 +3825,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.SoftmaxCentered.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#SoftmaxCentered.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SoftmaxCentered.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#SoftmaxCentered.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -3890,7 +3834,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3956,7 +3899,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse(y, name='inverse', **condition_kwargs)` {#SoftmaxCentered.inverse} +#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse(y, name='inverse')` {#SoftmaxCentered.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -3965,7 +3908,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3982,7 +3924,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#SoftmaxCentered.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#SoftmaxCentered.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -3996,7 +3938,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4033,7 +3974,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#SoftmaxCentered.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#SoftmaxCentered.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -4046,7 +3987,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4140,7 +4080,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Softplus.forward(x, name='forward', **condition_kwargs)` {#Softplus.forward} +#### `tf.contrib.distributions.bijector.Softplus.forward(x, name='forward')` {#Softplus.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -4149,7 +4089,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4185,7 +4124,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Softplus.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Softplus.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Softplus.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Softplus.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -4194,7 +4133,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4260,7 +4198,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse(y, name='inverse', **condition_kwargs)` {#Softplus.inverse} +#### `tf.contrib.distributions.bijector.Softplus.inverse(y, name='inverse')` {#Softplus.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -4269,7 +4207,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4286,7 +4223,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Softplus.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Softplus.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Softplus.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -4300,7 +4237,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4337,7 +4273,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Softplus.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Softplus.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Softplus.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -4350,7 +4286,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md index 16a96549e0..bd8e9e8a0b 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md +++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md @@ -261,7 +261,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Distribution.cdf(value, name='cdf', **condition_kwargs)` {#Distribution.cdf} +#### `tf.contrib.distributions.Distribution.cdf(value, name='cdf')` {#Distribution.cdf} Cumulative distribution function. @@ -276,7 +276,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -410,7 +409,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Distribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Distribution.log_cdf} +#### `tf.contrib.distributions.Distribution.log_cdf(value, name='log_cdf')` {#Distribution.log_cdf} Log cumulative distribution function. @@ -429,7 +428,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -440,7 +438,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Distribution.log_pdf} +#### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf')` {#Distribution.log_pdf} Log probability density function. @@ -449,7 +447,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -465,7 +462,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Distribution.log_pmf} +#### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf')` {#Distribution.log_pmf} Log probability mass function. @@ -474,7 +471,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -490,7 +486,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob', **condition_kwargs)` {#Distribution.log_prob} +#### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob')` {#Distribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -499,7 +495,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -510,7 +505,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Distribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Distribution.log_survival_function} +#### `tf.contrib.distributions.Distribution.log_survival_function(value, name='log_survival_function')` {#Distribution.log_survival_function} Log survival function. @@ -530,7 +525,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -622,7 +616,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Distribution.pdf(value, name='pdf', **condition_kwargs)` {#Distribution.pdf} +#### `tf.contrib.distributions.Distribution.pdf(value, name='pdf')` {#Distribution.pdf} Probability density function. @@ -631,7 +625,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -647,7 +640,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Distribution.pmf(value, name='pmf', **condition_kwargs)` {#Distribution.pmf} +#### `tf.contrib.distributions.Distribution.pmf(value, name='pmf')` {#Distribution.pmf} Probability mass function. @@ -656,7 +649,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -672,7 +664,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Distribution.prob(value, name='prob', **condition_kwargs)` {#Distribution.prob} +#### `tf.contrib.distributions.Distribution.prob(value, name='prob')` {#Distribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -681,7 +673,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -707,7 +698,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Distribution.sample} +#### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample')` {#Distribution.sample} Generate samples of the specified shape. @@ -720,7 +711,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -737,7 +727,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Distribution.survival_function(value, name='survival_function', **condition_kwargs)` {#Distribution.survival_function} +#### `tf.contrib.distributions.Distribution.survival_function(value, name='survival_function')` {#Distribution.survival_function} Survival function. @@ -754,7 +744,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -921,7 +910,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Binomial.cdf(value, name='cdf', **condition_kwargs)` {#Binomial.cdf} +#### `tf.contrib.distributions.Binomial.cdf(value, name='cdf')` {#Binomial.cdf} Cumulative distribution function. @@ -936,7 +925,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1070,7 +1058,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Binomial.log_cdf} +#### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf')` {#Binomial.log_cdf} Log cumulative distribution function. @@ -1089,7 +1077,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1100,7 +1087,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Binomial.log_pdf} +#### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf')` {#Binomial.log_pdf} Log probability density function. @@ -1109,7 +1096,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1125,7 +1111,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Binomial.log_pmf} +#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf')` {#Binomial.log_pmf} Log probability mass function. @@ -1134,7 +1120,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1150,7 +1135,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob', **condition_kwargs)` {#Binomial.log_prob} +#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob')` {#Binomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -1172,7 +1157,6 @@ values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1183,7 +1167,7 @@ values. - - - -#### `tf.contrib.distributions.Binomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Binomial.log_survival_function} +#### `tf.contrib.distributions.Binomial.log_survival_function(value, name='log_survival_function')` {#Binomial.log_survival_function} Log survival function. @@ -1203,7 +1187,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1322,7 +1305,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf', **condition_kwargs)` {#Binomial.pdf} +#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf')` {#Binomial.pdf} Probability density function. @@ -1331,7 +1314,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1347,7 +1329,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf', **condition_kwargs)` {#Binomial.pmf} +#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf')` {#Binomial.pmf} Probability mass function. @@ -1356,7 +1338,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1372,7 +1353,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Binomial.prob(value, name='prob', **condition_kwargs)` {#Binomial.prob} +#### `tf.contrib.distributions.Binomial.prob(value, name='prob')` {#Binomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -1394,7 +1375,6 @@ values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1420,7 +1400,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Binomial.sample} +#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample')` {#Binomial.sample} Generate samples of the specified shape. @@ -1433,7 +1413,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1450,7 +1429,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Binomial.survival_function(value, name='survival_function', **condition_kwargs)` {#Binomial.survival_function} +#### `tf.contrib.distributions.Binomial.survival_function(value, name='survival_function')` {#Binomial.survival_function} Survival function. @@ -1467,7 +1446,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1574,7 +1552,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Bernoulli.cdf(value, name='cdf', **condition_kwargs)` {#Bernoulli.cdf} +#### `tf.contrib.distributions.Bernoulli.cdf(value, name='cdf')` {#Bernoulli.cdf} Cumulative distribution function. @@ -1589,7 +1567,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1723,7 +1700,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Bernoulli.log_cdf} +#### `tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf')` {#Bernoulli.log_cdf} Log cumulative distribution function. @@ -1742,7 +1719,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1753,7 +1729,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Bernoulli.log_pdf} +#### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf')` {#Bernoulli.log_pdf} Log probability density function. @@ -1762,7 +1738,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1778,7 +1753,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Bernoulli.log_pmf} +#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf')` {#Bernoulli.log_pmf} Log probability mass function. @@ -1787,7 +1762,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1803,7 +1777,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob', **condition_kwargs)` {#Bernoulli.log_prob} +#### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob')` {#Bernoulli.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -1812,7 +1786,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1823,7 +1796,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Bernoulli.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Bernoulli.log_survival_function} +#### `tf.contrib.distributions.Bernoulli.log_survival_function(value, name='log_survival_function')` {#Bernoulli.log_survival_function} Log survival function. @@ -1843,7 +1816,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1953,7 +1925,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf', **condition_kwargs)` {#Bernoulli.pdf} +#### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf')` {#Bernoulli.pdf} Probability density function. @@ -1962,7 +1934,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -1978,7 +1949,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf', **condition_kwargs)` {#Bernoulli.pmf} +#### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf')` {#Bernoulli.pmf} Probability mass function. @@ -1987,7 +1958,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2003,7 +1973,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Bernoulli.prob(value, name='prob', **condition_kwargs)` {#Bernoulli.prob} +#### `tf.contrib.distributions.Bernoulli.prob(value, name='prob')` {#Bernoulli.prob} Probability density/mass function (depending on `is_continuous`). @@ -2012,7 +1982,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2045,7 +2014,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Bernoulli.sample} +#### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample')` {#Bernoulli.sample} Generate samples of the specified shape. @@ -2058,7 +2027,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2075,7 +2043,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Bernoulli.survival_function(value, name='survival_function', **condition_kwargs)` {#Bernoulli.survival_function} +#### `tf.contrib.distributions.Bernoulli.survival_function(value, name='survival_function')` {#Bernoulli.survival_function} Survival function. @@ -2092,7 +2060,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2170,7 +2137,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.cdf(value, name='cdf', **condition_kwargs)` {#BernoulliWithSigmoidP.cdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.cdf(value, name='cdf')` {#BernoulliWithSigmoidP.cdf} Cumulative distribution function. @@ -2185,7 +2152,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2319,7 +2285,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_cdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf')` {#BernoulliWithSigmoidP.log_cdf} Log cumulative distribution function. @@ -2338,7 +2304,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2349,7 +2314,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_pdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf')` {#BernoulliWithSigmoidP.log_pdf} Log probability density function. @@ -2358,7 +2323,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2374,7 +2338,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_pmf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf')` {#BernoulliWithSigmoidP.log_pmf} Log probability mass function. @@ -2383,7 +2347,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2399,7 +2362,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob', **condition_kwargs)` {#BernoulliWithSigmoidP.log_prob} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob')` {#BernoulliWithSigmoidP.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -2408,7 +2371,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2419,7 +2381,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#BernoulliWithSigmoidP.log_survival_function} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function')` {#BernoulliWithSigmoidP.log_survival_function} Log survival function. @@ -2439,7 +2401,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2549,7 +2510,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf', **condition_kwargs)` {#BernoulliWithSigmoidP.pdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf')` {#BernoulliWithSigmoidP.pdf} Probability density function. @@ -2558,7 +2519,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2574,7 +2534,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf', **condition_kwargs)` {#BernoulliWithSigmoidP.pmf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf')` {#BernoulliWithSigmoidP.pmf} Probability mass function. @@ -2583,7 +2543,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2599,7 +2558,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob', **condition_kwargs)` {#BernoulliWithSigmoidP.prob} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob')` {#BernoulliWithSigmoidP.prob} Probability density/mass function (depending on `is_continuous`). @@ -2608,7 +2567,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2641,7 +2599,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#BernoulliWithSigmoidP.sample} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample')` {#BernoulliWithSigmoidP.sample} Generate samples of the specified shape. @@ -2654,7 +2612,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2671,7 +2628,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function', **condition_kwargs)` {#BernoulliWithSigmoidP.survival_function} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function')` {#BernoulliWithSigmoidP.survival_function} Survival function. @@ -2688,7 +2645,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -2876,7 +2832,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Beta.cdf(value, name='cdf', **condition_kwargs)` {#Beta.cdf} +#### `tf.contrib.distributions.Beta.cdf(value, name='cdf')` {#Beta.cdf} Cumulative distribution function. @@ -2891,7 +2847,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3025,7 +2980,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Beta.log_cdf} +#### `tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf')` {#Beta.log_cdf} Log cumulative distribution function. @@ -3052,7 +3007,6 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3063,7 +3017,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Beta.log_pdf} +#### `tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf')` {#Beta.log_pdf} Log probability density function. @@ -3072,7 +3026,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3088,7 +3041,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Beta.log_pmf} +#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf')` {#Beta.log_pmf} Log probability mass function. @@ -3097,7 +3050,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3113,7 +3065,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob', **condition_kwargs)` {#Beta.log_prob} +#### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob')` {#Beta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -3122,7 +3074,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3133,7 +3084,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Beta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Beta.log_survival_function} +#### `tf.contrib.distributions.Beta.log_survival_function(value, name='log_survival_function')` {#Beta.log_survival_function} Log survival function. @@ -3153,7 +3104,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3252,7 +3202,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Beta.pdf(value, name='pdf', **condition_kwargs)` {#Beta.pdf} +#### `tf.contrib.distributions.Beta.pdf(value, name='pdf')` {#Beta.pdf} Probability density function. @@ -3261,7 +3211,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3277,7 +3226,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Beta.pmf(value, name='pmf', **condition_kwargs)` {#Beta.pmf} +#### `tf.contrib.distributions.Beta.pmf(value, name='pmf')` {#Beta.pmf} Probability mass function. @@ -3286,7 +3235,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3302,7 +3250,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Beta.prob(value, name='prob', **condition_kwargs)` {#Beta.prob} +#### `tf.contrib.distributions.Beta.prob(value, name='prob')` {#Beta.prob} Probability density/mass function (depending on `is_continuous`). @@ -3319,7 +3267,6 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3345,7 +3292,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Beta.sample} +#### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample')` {#Beta.sample} Generate samples of the specified shape. @@ -3358,7 +3305,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3375,7 +3321,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Beta.survival_function(value, name='survival_function', **condition_kwargs)` {#Beta.survival_function} +#### `tf.contrib.distributions.Beta.survival_function(value, name='survival_function')` {#Beta.survival_function} Survival function. @@ -3392,7 +3338,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3491,7 +3436,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.cdf(value, name='cdf', **condition_kwargs)` {#BetaWithSoftplusAB.cdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.cdf(value, name='cdf')` {#BetaWithSoftplusAB.cdf} Cumulative distribution function. @@ -3506,7 +3451,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3640,7 +3584,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf', **condition_kwargs)` {#BetaWithSoftplusAB.log_cdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf')` {#BetaWithSoftplusAB.log_cdf} Log cumulative distribution function. @@ -3667,7 +3611,6 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3678,7 +3621,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pdf(value, name='log_pdf', **condition_kwargs)` {#BetaWithSoftplusAB.log_pdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pdf(value, name='log_pdf')` {#BetaWithSoftplusAB.log_pdf} Log probability density function. @@ -3687,7 +3630,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3703,7 +3645,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf', **condition_kwargs)` {#BetaWithSoftplusAB.log_pmf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf')` {#BetaWithSoftplusAB.log_pmf} Log probability mass function. @@ -3712,7 +3654,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3728,7 +3669,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob', **condition_kwargs)` {#BetaWithSoftplusAB.log_prob} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob')` {#BetaWithSoftplusAB.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -3737,7 +3678,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3748,7 +3688,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#BetaWithSoftplusAB.log_survival_function} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function')` {#BetaWithSoftplusAB.log_survival_function} Log survival function. @@ -3768,7 +3708,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3867,7 +3806,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf', **condition_kwargs)` {#BetaWithSoftplusAB.pdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf')` {#BetaWithSoftplusAB.pdf} Probability density function. @@ -3876,7 +3815,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3892,7 +3830,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf', **condition_kwargs)` {#BetaWithSoftplusAB.pmf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf')` {#BetaWithSoftplusAB.pmf} Probability mass function. @@ -3901,7 +3839,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3917,7 +3854,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob', **condition_kwargs)` {#BetaWithSoftplusAB.prob} +#### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob')` {#BetaWithSoftplusAB.prob} Probability density/mass function (depending on `is_continuous`). @@ -3934,7 +3871,6 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3960,7 +3896,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#BetaWithSoftplusAB.sample} +#### `tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample')` {#BetaWithSoftplusAB.sample} Generate samples of the specified shape. @@ -3973,7 +3909,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -3990,7 +3925,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function', **condition_kwargs)` {#BetaWithSoftplusAB.survival_function} +#### `tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function')` {#BetaWithSoftplusAB.survival_function} Survival function. @@ -4007,7 +3942,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4145,7 +4079,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Categorical.cdf(value, name='cdf', **condition_kwargs)` {#Categorical.cdf} +#### `tf.contrib.distributions.Categorical.cdf(value, name='cdf')` {#Categorical.cdf} Cumulative distribution function. @@ -4160,7 +4094,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4294,7 +4227,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Categorical.log_cdf} +#### `tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf')` {#Categorical.log_cdf} Log cumulative distribution function. @@ -4313,7 +4246,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4324,7 +4256,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Categorical.log_pdf} +#### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf')` {#Categorical.log_pdf} Log probability density function. @@ -4333,7 +4265,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4349,7 +4280,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Categorical.log_pmf} +#### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf')` {#Categorical.log_pmf} Log probability mass function. @@ -4358,7 +4289,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4374,7 +4304,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob', **condition_kwargs)` {#Categorical.log_prob} +#### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob')` {#Categorical.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -4383,7 +4313,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4394,7 +4323,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Categorical.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Categorical.log_survival_function} +#### `tf.contrib.distributions.Categorical.log_survival_function(value, name='log_survival_function')` {#Categorical.log_survival_function} Log survival function. @@ -4414,7 +4343,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4529,7 +4457,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Categorical.pdf(value, name='pdf', **condition_kwargs)` {#Categorical.pdf} +#### `tf.contrib.distributions.Categorical.pdf(value, name='pdf')` {#Categorical.pdf} Probability density function. @@ -4538,7 +4466,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4554,7 +4481,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Categorical.pmf(value, name='pmf', **condition_kwargs)` {#Categorical.pmf} +#### `tf.contrib.distributions.Categorical.pmf(value, name='pmf')` {#Categorical.pmf} Probability mass function. @@ -4563,7 +4490,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4579,7 +4505,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Categorical.prob(value, name='prob', **condition_kwargs)` {#Categorical.prob} +#### `tf.contrib.distributions.Categorical.prob(value, name='prob')` {#Categorical.prob} Probability density/mass function (depending on `is_continuous`). @@ -4588,7 +4514,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4614,7 +4539,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Categorical.sample} +#### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample')` {#Categorical.sample} Generate samples of the specified shape. @@ -4627,7 +4552,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4644,7 +4568,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Categorical.survival_function(value, name='survival_function', **condition_kwargs)` {#Categorical.survival_function} +#### `tf.contrib.distributions.Categorical.survival_function(value, name='survival_function')` {#Categorical.survival_function} Survival function. @@ -4661,7 +4585,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4775,7 +4698,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Chi2.cdf(value, name='cdf', **condition_kwargs)` {#Chi2.cdf} +#### `tf.contrib.distributions.Chi2.cdf(value, name='cdf')` {#Chi2.cdf} Cumulative distribution function. @@ -4790,7 +4713,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4942,7 +4864,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Chi2.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Chi2.log_cdf} +#### `tf.contrib.distributions.Chi2.log_cdf(value, name='log_cdf')` {#Chi2.log_cdf} Log cumulative distribution function. @@ -4961,7 +4883,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4972,7 +4893,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Chi2.log_pdf} +#### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf')` {#Chi2.log_pdf} Log probability density function. @@ -4981,7 +4902,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -4997,7 +4917,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Chi2.log_pmf} +#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf')` {#Chi2.log_pmf} Log probability mass function. @@ -5006,7 +4926,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5022,7 +4941,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob', **condition_kwargs)` {#Chi2.log_prob} +#### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob')` {#Chi2.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -5031,7 +4950,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5042,7 +4960,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Chi2.log_survival_function} +#### `tf.contrib.distributions.Chi2.log_survival_function(value, name='log_survival_function')` {#Chi2.log_survival_function} Log survival function. @@ -5062,7 +4980,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5160,7 +5077,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf', **condition_kwargs)` {#Chi2.pdf} +#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf')` {#Chi2.pdf} Probability density function. @@ -5169,7 +5086,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5185,7 +5101,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf', **condition_kwargs)` {#Chi2.pmf} +#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf')` {#Chi2.pmf} Probability mass function. @@ -5194,7 +5110,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5210,7 +5125,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Chi2.prob(value, name='prob', **condition_kwargs)` {#Chi2.prob} +#### `tf.contrib.distributions.Chi2.prob(value, name='prob')` {#Chi2.prob} Probability density/mass function (depending on `is_continuous`). @@ -5219,7 +5134,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5245,7 +5159,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Chi2.sample} +#### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample')` {#Chi2.sample} Generate samples of the specified shape. @@ -5258,7 +5172,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5275,7 +5188,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Chi2.survival_function(value, name='survival_function', **condition_kwargs)` {#Chi2.survival_function} +#### `tf.contrib.distributions.Chi2.survival_function(value, name='survival_function')` {#Chi2.survival_function} Survival function. @@ -5292,7 +5205,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5384,7 +5296,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.cdf(value, name='cdf', **condition_kwargs)` {#Chi2WithAbsDf.cdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.cdf(value, name='cdf')` {#Chi2WithAbsDf.cdf} Cumulative distribution function. @@ -5399,7 +5311,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5551,7 +5462,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Chi2WithAbsDf.log_cdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_cdf(value, name='log_cdf')` {#Chi2WithAbsDf.log_cdf} Log cumulative distribution function. @@ -5570,7 +5481,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5581,7 +5491,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Chi2WithAbsDf.log_pdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf')` {#Chi2WithAbsDf.log_pdf} Log probability density function. @@ -5590,7 +5500,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5606,7 +5515,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Chi2WithAbsDf.log_pmf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf')` {#Chi2WithAbsDf.log_pmf} Log probability mass function. @@ -5615,7 +5524,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5631,7 +5539,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob', **condition_kwargs)` {#Chi2WithAbsDf.log_prob} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob')` {#Chi2WithAbsDf.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -5640,7 +5548,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5651,7 +5558,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Chi2WithAbsDf.log_survival_function} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_survival_function(value, name='log_survival_function')` {#Chi2WithAbsDf.log_survival_function} Log survival function. @@ -5671,7 +5578,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5769,7 +5675,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf', **condition_kwargs)` {#Chi2WithAbsDf.pdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf')` {#Chi2WithAbsDf.pdf} Probability density function. @@ -5778,7 +5684,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5794,7 +5699,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf', **condition_kwargs)` {#Chi2WithAbsDf.pmf} +#### `tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf')` {#Chi2WithAbsDf.pmf} Probability mass function. @@ -5803,7 +5708,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5819,7 +5723,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob', **condition_kwargs)` {#Chi2WithAbsDf.prob} +#### `tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob')` {#Chi2WithAbsDf.prob} Probability density/mass function (depending on `is_continuous`). @@ -5828,7 +5732,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5854,7 +5757,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Chi2WithAbsDf.sample} +#### `tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample')` {#Chi2WithAbsDf.sample} Generate samples of the specified shape. @@ -5867,7 +5770,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -5884,7 +5786,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function', **condition_kwargs)` {#Chi2WithAbsDf.survival_function} +#### `tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function')` {#Chi2WithAbsDf.survival_function} Survival function. @@ -5901,7 +5803,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6015,7 +5916,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Exponential.cdf(value, name='cdf', **condition_kwargs)` {#Exponential.cdf} +#### `tf.contrib.distributions.Exponential.cdf(value, name='cdf')` {#Exponential.cdf} Cumulative distribution function. @@ -6030,7 +5931,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6182,7 +6082,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Exponential.log_cdf} +#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf')` {#Exponential.log_cdf} Log cumulative distribution function. @@ -6201,7 +6101,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6212,7 +6111,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Exponential.log_pdf} +#### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf')` {#Exponential.log_pdf} Log probability density function. @@ -6221,7 +6120,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6237,7 +6135,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Exponential.log_pmf} +#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')` {#Exponential.log_pmf} Log probability mass function. @@ -6246,7 +6144,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6262,7 +6159,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob', **condition_kwargs)` {#Exponential.log_prob} +#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob')` {#Exponential.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -6271,7 +6168,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6282,7 +6178,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Exponential.log_survival_function} +#### `tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function')` {#Exponential.log_survival_function} Log survival function. @@ -6302,7 +6198,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6400,7 +6295,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf', **condition_kwargs)` {#Exponential.pdf} +#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf')` {#Exponential.pdf} Probability density function. @@ -6409,7 +6304,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6425,7 +6319,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf', **condition_kwargs)` {#Exponential.pmf} +#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf')` {#Exponential.pmf} Probability mass function. @@ -6434,7 +6328,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6450,7 +6343,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Exponential.prob(value, name='prob', **condition_kwargs)` {#Exponential.prob} +#### `tf.contrib.distributions.Exponential.prob(value, name='prob')` {#Exponential.prob} Probability density/mass function (depending on `is_continuous`). @@ -6459,7 +6352,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6485,7 +6377,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Exponential.sample} +#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample')` {#Exponential.sample} Generate samples of the specified shape. @@ -6498,7 +6390,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6515,7 +6406,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Exponential.survival_function(value, name='survival_function', **condition_kwargs)` {#Exponential.survival_function} +#### `tf.contrib.distributions.Exponential.survival_function(value, name='survival_function')` {#Exponential.survival_function} Survival function. @@ -6532,7 +6423,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6624,7 +6514,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.cdf(value, name='cdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.cdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.cdf(value, name='cdf')` {#ExponentialWithSoftplusLam.cdf} Cumulative distribution function. @@ -6639,7 +6529,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6791,7 +6680,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_cdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf')` {#ExponentialWithSoftplusLam.log_cdf} Log cumulative distribution function. @@ -6810,7 +6699,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6821,7 +6709,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_pdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf')` {#ExponentialWithSoftplusLam.log_pdf} Log probability density function. @@ -6830,7 +6718,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6846,7 +6733,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_pmf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf')` {#ExponentialWithSoftplusLam.log_pmf} Log probability mass function. @@ -6855,7 +6742,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6871,7 +6757,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_prob} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob')` {#ExponentialWithSoftplusLam.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -6880,7 +6766,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -6891,7 +6776,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_survival_function} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_survival_function(value, name='log_survival_function')` {#ExponentialWithSoftplusLam.log_survival_function} Log survival function. @@ -6911,7 +6796,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7009,7 +6893,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.pdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf')` {#ExponentialWithSoftplusLam.pdf} Probability density function. @@ -7018,7 +6902,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7034,7 +6917,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf', **condition_kwargs)` {#ExponentialWithSoftplusLam.pmf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf')` {#ExponentialWithSoftplusLam.pmf} Probability mass function. @@ -7043,7 +6926,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7059,7 +6941,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob', **condition_kwargs)` {#ExponentialWithSoftplusLam.prob} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob')` {#ExponentialWithSoftplusLam.prob} Probability density/mass function (depending on `is_continuous`). @@ -7068,7 +6950,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7094,7 +6975,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#ExponentialWithSoftplusLam.sample} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample(sample_shape=(), seed=None, name='sample')` {#ExponentialWithSoftplusLam.sample} Generate samples of the specified shape. @@ -7107,7 +6988,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7124,7 +7004,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.survival_function(value, name='survival_function', **condition_kwargs)` {#ExponentialWithSoftplusLam.survival_function} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.survival_function(value, name='survival_function')` {#ExponentialWithSoftplusLam.survival_function} Survival function. @@ -7141,7 +7021,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7282,7 +7161,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Gamma.cdf(value, name='cdf', **condition_kwargs)` {#Gamma.cdf} +#### `tf.contrib.distributions.Gamma.cdf(value, name='cdf')` {#Gamma.cdf} Cumulative distribution function. @@ -7297,7 +7176,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7442,7 +7320,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Gamma.log_cdf} +#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf')` {#Gamma.log_cdf} Log cumulative distribution function. @@ -7461,7 +7339,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7472,7 +7349,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Gamma.log_pdf} +#### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf')` {#Gamma.log_pdf} Log probability density function. @@ -7481,7 +7358,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7497,7 +7373,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Gamma.log_pmf} +#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')` {#Gamma.log_pmf} Log probability mass function. @@ -7506,7 +7382,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7522,7 +7397,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob', **condition_kwargs)` {#Gamma.log_prob} +#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob')` {#Gamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -7531,7 +7406,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7542,7 +7416,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Gamma.log_survival_function} +#### `tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function')` {#Gamma.log_survival_function} Log survival function. @@ -7562,7 +7436,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7660,7 +7533,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf', **condition_kwargs)` {#Gamma.pdf} +#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf')` {#Gamma.pdf} Probability density function. @@ -7669,7 +7542,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7685,7 +7557,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf', **condition_kwargs)` {#Gamma.pmf} +#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf')` {#Gamma.pmf} Probability mass function. @@ -7694,7 +7566,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7710,7 +7581,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Gamma.prob(value, name='prob', **condition_kwargs)` {#Gamma.prob} +#### `tf.contrib.distributions.Gamma.prob(value, name='prob')` {#Gamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -7719,7 +7590,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7745,7 +7615,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Gamma.sample} +#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample')` {#Gamma.sample} Generate samples of the specified shape. @@ -7758,7 +7628,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7775,7 +7644,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Gamma.survival_function(value, name='survival_function', **condition_kwargs)` {#Gamma.survival_function} +#### `tf.contrib.distributions.Gamma.survival_function(value, name='survival_function')` {#Gamma.survival_function} Survival function. @@ -7792,7 +7661,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -7884,7 +7752,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.cdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf')` {#GammaWithSoftplusAlphaBeta.cdf} Cumulative distribution function. @@ -7899,7 +7767,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8044,7 +7911,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_cdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')` {#GammaWithSoftplusAlphaBeta.log_cdf} Log cumulative distribution function. @@ -8063,7 +7930,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8074,7 +7940,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_pdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')` {#GammaWithSoftplusAlphaBeta.log_pdf} Log probability density function. @@ -8083,7 +7949,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8099,7 +7964,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_pmf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#GammaWithSoftplusAlphaBeta.log_pmf} Log probability mass function. @@ -8108,7 +7973,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8124,7 +7988,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_prob} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#GammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -8133,7 +7997,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8144,7 +8007,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_survival_function} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')` {#GammaWithSoftplusAlphaBeta.log_survival_function} Log survival function. @@ -8164,7 +8027,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8262,7 +8124,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.pdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#GammaWithSoftplusAlphaBeta.pdf} Probability density function. @@ -8271,7 +8133,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8287,7 +8148,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.pmf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#GammaWithSoftplusAlphaBeta.pmf} Probability mass function. @@ -8296,7 +8157,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8312,7 +8172,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.prob} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#GammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -8321,7 +8181,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8347,7 +8206,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.sample} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')` {#GammaWithSoftplusAlphaBeta.sample} Generate samples of the specified shape. @@ -8360,7 +8219,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8377,7 +8235,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.survival_function} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')` {#GammaWithSoftplusAlphaBeta.survival_function} Survival function. @@ -8394,7 +8252,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8531,7 +8388,7 @@ Scale parameter. - - - -#### `tf.contrib.distributions.InverseGamma.cdf(value, name='cdf', **condition_kwargs)` {#InverseGamma.cdf} +#### `tf.contrib.distributions.InverseGamma.cdf(value, name='cdf')` {#InverseGamma.cdf} Cumulative distribution function. @@ -8546,7 +8403,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8691,7 +8547,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#InverseGamma.log_cdf} +#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf')` {#InverseGamma.log_cdf} Log cumulative distribution function. @@ -8710,7 +8566,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8721,7 +8576,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#InverseGamma.log_pdf} +#### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf')` {#InverseGamma.log_pdf} Log probability density function. @@ -8730,7 +8585,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8746,7 +8600,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#InverseGamma.log_pmf} +#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')` {#InverseGamma.log_pmf} Log probability mass function. @@ -8755,7 +8609,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8771,7 +8624,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob', **condition_kwargs)` {#InverseGamma.log_prob} +#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob')` {#InverseGamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -8780,7 +8633,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8791,7 +8643,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#InverseGamma.log_survival_function} +#### `tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function')` {#InverseGamma.log_survival_function} Log survival function. @@ -8811,7 +8663,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8913,7 +8764,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf', **condition_kwargs)` {#InverseGamma.pdf} +#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')` {#InverseGamma.pdf} Probability density function. @@ -8922,7 +8773,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8938,7 +8788,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf', **condition_kwargs)` {#InverseGamma.pmf} +#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')` {#InverseGamma.pmf} Probability mass function. @@ -8947,7 +8797,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8963,7 +8812,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob', **condition_kwargs)` {#InverseGamma.prob} +#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob')` {#InverseGamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -8972,7 +8821,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -8998,7 +8846,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#InverseGamma.sample} +#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample')` {#InverseGamma.sample} Generate samples of the specified shape. @@ -9011,7 +8859,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9028,7 +8875,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function', **condition_kwargs)` {#InverseGamma.survival_function} +#### `tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function')` {#InverseGamma.survival_function} Survival function. @@ -9045,7 +8892,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9143,7 +8989,7 @@ Scale parameter. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.cdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf')` {#InverseGammaWithSoftplusAlphaBeta.cdf} Cumulative distribution function. @@ -9158,7 +9004,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9303,7 +9148,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_cdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')` {#InverseGammaWithSoftplusAlphaBeta.log_cdf} Log cumulative distribution function. @@ -9322,7 +9167,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9333,7 +9177,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_pdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')` {#InverseGammaWithSoftplusAlphaBeta.log_pdf} Log probability density function. @@ -9342,7 +9186,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9358,7 +9201,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} Log probability mass function. @@ -9367,7 +9210,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9383,7 +9225,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_prob} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#InverseGammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -9392,7 +9234,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9403,7 +9244,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_survival_function} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')` {#InverseGammaWithSoftplusAlphaBeta.log_survival_function} Log survival function. @@ -9423,7 +9264,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9525,7 +9365,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.pdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#InverseGammaWithSoftplusAlphaBeta.pdf} Probability density function. @@ -9534,7 +9374,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9550,7 +9389,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.pmf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#InverseGammaWithSoftplusAlphaBeta.pmf} Probability mass function. @@ -9559,7 +9398,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9575,7 +9413,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.prob} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#InverseGammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -9584,7 +9422,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9610,7 +9447,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.sample} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')` {#InverseGammaWithSoftplusAlphaBeta.sample} Generate samples of the specified shape. @@ -9623,7 +9460,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9640,7 +9476,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.survival_function} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')` {#InverseGammaWithSoftplusAlphaBeta.survival_function} Survival function. @@ -9657,7 +9493,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9774,7 +9609,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Laplace.cdf(value, name='cdf', **condition_kwargs)` {#Laplace.cdf} +#### `tf.contrib.distributions.Laplace.cdf(value, name='cdf')` {#Laplace.cdf} Cumulative distribution function. @@ -9789,7 +9624,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9930,7 +9764,7 @@ Distribution parameter for the location. - - - -#### `tf.contrib.distributions.Laplace.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Laplace.log_cdf} +#### `tf.contrib.distributions.Laplace.log_cdf(value, name='log_cdf')` {#Laplace.log_cdf} Log cumulative distribution function. @@ -9949,7 +9783,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9960,7 +9793,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Laplace.log_pdf} +#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf')` {#Laplace.log_pdf} Log probability density function. @@ -9969,7 +9802,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -9985,7 +9817,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Laplace.log_pmf} +#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf} Log probability mass function. @@ -9994,7 +9826,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10010,7 +9841,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob', **condition_kwargs)` {#Laplace.log_prob} +#### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob')` {#Laplace.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -10019,7 +9850,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10030,7 +9860,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Laplace.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Laplace.log_survival_function} +#### `tf.contrib.distributions.Laplace.log_survival_function(value, name='log_survival_function')` {#Laplace.log_survival_function} Log survival function. @@ -10050,7 +9880,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10142,7 +9971,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf', **condition_kwargs)` {#Laplace.pdf} +#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf')` {#Laplace.pdf} Probability density function. @@ -10151,7 +9980,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10167,7 +9995,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf', **condition_kwargs)` {#Laplace.pmf} +#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf')` {#Laplace.pmf} Probability mass function. @@ -10176,7 +10004,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10192,7 +10019,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Laplace.prob(value, name='prob', **condition_kwargs)` {#Laplace.prob} +#### `tf.contrib.distributions.Laplace.prob(value, name='prob')` {#Laplace.prob} Probability density/mass function (depending on `is_continuous`). @@ -10201,7 +10028,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10227,7 +10053,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Laplace.sample} +#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample')` {#Laplace.sample} Generate samples of the specified shape. @@ -10240,7 +10066,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10264,7 +10089,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Laplace.survival_function(value, name='survival_function', **condition_kwargs)` {#Laplace.survival_function} +#### `tf.contrib.distributions.Laplace.survival_function(value, name='survival_function')` {#Laplace.survival_function} Survival function. @@ -10281,7 +10106,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10359,7 +10183,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.cdf(value, name='cdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.cdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.cdf(value, name='cdf')` {#LaplaceWithSoftplusScale.cdf} Cumulative distribution function. @@ -10374,7 +10198,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10515,7 +10338,7 @@ Distribution parameter for the location. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_cdf(value, name='log_cdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_cdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_cdf(value, name='log_cdf')` {#LaplaceWithSoftplusScale.log_cdf} Log cumulative distribution function. @@ -10534,7 +10357,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10545,7 +10367,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_pdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf')` {#LaplaceWithSoftplusScale.log_pdf} Log probability density function. @@ -10554,7 +10376,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10570,7 +10391,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_pmf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf')` {#LaplaceWithSoftplusScale.log_pmf} Log probability mass function. @@ -10579,7 +10400,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10595,7 +10415,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_prob} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob')` {#LaplaceWithSoftplusScale.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -10604,7 +10424,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10615,7 +10434,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_survival_function} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_survival_function(value, name='log_survival_function')` {#LaplaceWithSoftplusScale.log_survival_function} Log survival function. @@ -10635,7 +10454,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10727,7 +10545,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.pdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf')` {#LaplaceWithSoftplusScale.pdf} Probability density function. @@ -10736,7 +10554,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10752,7 +10569,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf', **condition_kwargs)` {#LaplaceWithSoftplusScale.pmf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf')` {#LaplaceWithSoftplusScale.pmf} Probability mass function. @@ -10761,7 +10578,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10777,7 +10593,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob', **condition_kwargs)` {#LaplaceWithSoftplusScale.prob} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob')` {#LaplaceWithSoftplusScale.prob} Probability density/mass function (depending on `is_continuous`). @@ -10786,7 +10602,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10812,7 +10627,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#LaplaceWithSoftplusScale.sample} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample(sample_shape=(), seed=None, name='sample')` {#LaplaceWithSoftplusScale.sample} Generate samples of the specified shape. @@ -10825,7 +10640,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -10849,7 +10663,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.survival_function(value, name='survival_function', **condition_kwargs)` {#LaplaceWithSoftplusScale.survival_function} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.survival_function(value, name='survival_function')` {#LaplaceWithSoftplusScale.survival_function} Survival function. @@ -10866,7 +10680,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11008,7 +10821,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Normal.cdf(value, name='cdf', **condition_kwargs)` {#Normal.cdf} +#### `tf.contrib.distributions.Normal.cdf(value, name='cdf')` {#Normal.cdf} Cumulative distribution function. @@ -11023,7 +10836,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11157,7 +10969,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Normal.log_cdf} +#### `tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf')` {#Normal.log_cdf} Log cumulative distribution function. @@ -11176,7 +10988,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11187,7 +10998,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Normal.log_pdf} +#### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf')` {#Normal.log_pdf} Log probability density function. @@ -11196,7 +11007,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11212,7 +11022,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Normal.log_pmf} +#### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf')` {#Normal.log_pmf} Log probability mass function. @@ -11221,7 +11031,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11237,7 +11046,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob', **condition_kwargs)` {#Normal.log_prob} +#### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob')` {#Normal.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -11246,7 +11055,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11257,7 +11065,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Normal.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Normal.log_survival_function} +#### `tf.contrib.distributions.Normal.log_survival_function(value, name='log_survival_function')` {#Normal.log_survival_function} Log survival function. @@ -11277,7 +11085,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11376,7 +11183,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Normal.pdf(value, name='pdf', **condition_kwargs)` {#Normal.pdf} +#### `tf.contrib.distributions.Normal.pdf(value, name='pdf')` {#Normal.pdf} Probability density function. @@ -11385,7 +11192,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11401,7 +11207,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Normal.pmf(value, name='pmf', **condition_kwargs)` {#Normal.pmf} +#### `tf.contrib.distributions.Normal.pmf(value, name='pmf')` {#Normal.pmf} Probability mass function. @@ -11410,7 +11216,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11426,7 +11231,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Normal.prob(value, name='prob', **condition_kwargs)` {#Normal.prob} +#### `tf.contrib.distributions.Normal.prob(value, name='prob')` {#Normal.prob} Probability density/mass function (depending on `is_continuous`). @@ -11435,7 +11240,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11461,7 +11265,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Normal.sample} +#### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample')` {#Normal.sample} Generate samples of the specified shape. @@ -11474,7 +11278,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11498,7 +11301,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Normal.survival_function(value, name='survival_function', **condition_kwargs)` {#Normal.survival_function} +#### `tf.contrib.distributions.Normal.survival_function(value, name='survival_function')` {#Normal.survival_function} Survival function. @@ -11515,7 +11318,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11593,7 +11395,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.cdf(value, name='cdf', **condition_kwargs)` {#NormalWithSoftplusSigma.cdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.cdf(value, name='cdf')` {#NormalWithSoftplusSigma.cdf} Cumulative distribution function. @@ -11608,7 +11410,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11742,7 +11543,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_cdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_cdf(value, name='log_cdf')` {#NormalWithSoftplusSigma.log_cdf} Log cumulative distribution function. @@ -11761,7 +11562,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11772,7 +11572,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_pdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pdf(value, name='log_pdf')` {#NormalWithSoftplusSigma.log_pdf} Log probability density function. @@ -11781,7 +11581,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11797,7 +11596,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_pmf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pmf(value, name='log_pmf')` {#NormalWithSoftplusSigma.log_pmf} Log probability mass function. @@ -11806,7 +11605,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11822,7 +11620,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_prob(value, name='log_prob', **condition_kwargs)` {#NormalWithSoftplusSigma.log_prob} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_prob(value, name='log_prob')` {#NormalWithSoftplusSigma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -11831,7 +11629,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11842,7 +11639,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#NormalWithSoftplusSigma.log_survival_function} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_survival_function(value, name='log_survival_function')` {#NormalWithSoftplusSigma.log_survival_function} Log survival function. @@ -11862,7 +11659,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11961,7 +11757,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.pdf(value, name='pdf', **condition_kwargs)` {#NormalWithSoftplusSigma.pdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.pdf(value, name='pdf')` {#NormalWithSoftplusSigma.pdf} Probability density function. @@ -11970,7 +11766,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -11986,7 +11781,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.pmf(value, name='pmf', **condition_kwargs)` {#NormalWithSoftplusSigma.pmf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.pmf(value, name='pmf')` {#NormalWithSoftplusSigma.pmf} Probability mass function. @@ -11995,7 +11790,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12011,7 +11805,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.prob(value, name='prob', **condition_kwargs)` {#NormalWithSoftplusSigma.prob} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.prob(value, name='prob')` {#NormalWithSoftplusSigma.prob} Probability density/mass function (depending on `is_continuous`). @@ -12020,7 +11814,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12046,7 +11839,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#NormalWithSoftplusSigma.sample} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample(sample_shape=(), seed=None, name='sample')` {#NormalWithSoftplusSigma.sample} Generate samples of the specified shape. @@ -12059,7 +11852,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12083,7 +11875,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.survival_function(value, name='survival_function', **condition_kwargs)` {#NormalWithSoftplusSigma.survival_function} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.survival_function(value, name='survival_function')` {#NormalWithSoftplusSigma.survival_function} Survival function. @@ -12100,7 +11892,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12202,7 +11993,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Poisson.cdf(value, name='cdf', **condition_kwargs)` {#Poisson.cdf} +#### `tf.contrib.distributions.Poisson.cdf(value, name='cdf')` {#Poisson.cdf} Cumulative distribution function. @@ -12217,7 +12008,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12358,7 +12148,7 @@ Rate parameter. - - - -#### `tf.contrib.distributions.Poisson.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Poisson.log_cdf} +#### `tf.contrib.distributions.Poisson.log_cdf(value, name='log_cdf')` {#Poisson.log_cdf} Log cumulative distribution function. @@ -12377,7 +12167,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12388,7 +12177,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Poisson.log_pdf} +#### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf')` {#Poisson.log_pdf} Log probability density function. @@ -12397,7 +12186,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12413,7 +12201,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Poisson.log_pmf} +#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf')` {#Poisson.log_pmf} Log probability mass function. @@ -12422,7 +12210,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12438,7 +12225,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob', **condition_kwargs)` {#Poisson.log_prob} +#### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob')` {#Poisson.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -12454,7 +12241,6 @@ legal if it is non-negative and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12465,7 +12251,7 @@ legal if it is non-negative and its components are equal to integer values. - - - -#### `tf.contrib.distributions.Poisson.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Poisson.log_survival_function} +#### `tf.contrib.distributions.Poisson.log_survival_function(value, name='log_survival_function')` {#Poisson.log_survival_function} Log survival function. @@ -12485,7 +12271,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12583,7 +12368,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf', **condition_kwargs)` {#Poisson.pdf} +#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf')` {#Poisson.pdf} Probability density function. @@ -12592,7 +12377,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12608,7 +12392,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf', **condition_kwargs)` {#Poisson.pmf} +#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf')` {#Poisson.pmf} Probability mass function. @@ -12617,7 +12401,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12633,7 +12416,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Poisson.prob(value, name='prob', **condition_kwargs)` {#Poisson.prob} +#### `tf.contrib.distributions.Poisson.prob(value, name='prob')` {#Poisson.prob} Probability density/mass function (depending on `is_continuous`). @@ -12649,7 +12432,6 @@ legal if it is non-negative and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12675,7 +12457,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Poisson.sample} +#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample')` {#Poisson.sample} Generate samples of the specified shape. @@ -12688,7 +12470,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12705,7 +12486,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Poisson.survival_function(value, name='survival_function', **condition_kwargs)` {#Poisson.survival_function} +#### `tf.contrib.distributions.Poisson.survival_function(value, name='survival_function')` {#Poisson.survival_function} Survival function. @@ -12722,7 +12503,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -12883,7 +12663,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.StudentT.cdf(value, name='cdf', **condition_kwargs)` {#StudentT.cdf} +#### `tf.contrib.distributions.StudentT.cdf(value, name='cdf')` {#StudentT.cdf} Cumulative distribution function. @@ -12898,7 +12678,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13039,7 +12818,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf', **condition_kwargs)` {#StudentT.log_cdf} +#### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf')` {#StudentT.log_cdf} Log cumulative distribution function. @@ -13058,7 +12837,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13069,7 +12847,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf', **condition_kwargs)` {#StudentT.log_pdf} +#### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf')` {#StudentT.log_pdf} Log probability density function. @@ -13078,7 +12856,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13094,7 +12871,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf', **condition_kwargs)` {#StudentT.log_pmf} +#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf')` {#StudentT.log_pmf} Log probability mass function. @@ -13103,7 +12880,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13119,7 +12895,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob', **condition_kwargs)` {#StudentT.log_prob} +#### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob')` {#StudentT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -13128,7 +12904,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13139,7 +12914,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentT.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#StudentT.log_survival_function} +#### `tf.contrib.distributions.StudentT.log_survival_function(value, name='log_survival_function')` {#StudentT.log_survival_function} Log survival function. @@ -13159,7 +12934,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13264,7 +13038,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.StudentT.pdf(value, name='pdf', **condition_kwargs)` {#StudentT.pdf} +#### `tf.contrib.distributions.StudentT.pdf(value, name='pdf')` {#StudentT.pdf} Probability density function. @@ -13273,7 +13047,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13289,7 +13062,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.StudentT.pmf(value, name='pmf', **condition_kwargs)` {#StudentT.pmf} +#### `tf.contrib.distributions.StudentT.pmf(value, name='pmf')` {#StudentT.pmf} Probability mass function. @@ -13298,7 +13071,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13314,7 +13086,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.StudentT.prob(value, name='prob', **condition_kwargs)` {#StudentT.prob} +#### `tf.contrib.distributions.StudentT.prob(value, name='prob')` {#StudentT.prob} Probability density/mass function (depending on `is_continuous`). @@ -13323,7 +13095,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13349,7 +13120,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#StudentT.sample} +#### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample')` {#StudentT.sample} Generate samples of the specified shape. @@ -13362,7 +13133,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13386,7 +13156,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.StudentT.survival_function(value, name='survival_function', **condition_kwargs)` {#StudentT.survival_function} +#### `tf.contrib.distributions.StudentT.survival_function(value, name='survival_function')` {#StudentT.survival_function} Survival function. @@ -13403,7 +13173,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13491,7 +13260,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.cdf(value, name='cdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.cdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.cdf(value, name='cdf')` {#StudentTWithAbsDfSoftplusSigma.cdf} Cumulative distribution function. @@ -13506,7 +13275,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13647,7 +13415,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_cdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_cdf(value, name='log_cdf')` {#StudentTWithAbsDfSoftplusSigma.log_cdf} Log cumulative distribution function. @@ -13666,7 +13434,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13677,7 +13444,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_pdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pdf(value, name='log_pdf')` {#StudentTWithAbsDfSoftplusSigma.log_pdf} Log probability density function. @@ -13686,7 +13453,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13702,7 +13468,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_pmf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pmf(value, name='log_pmf')` {#StudentTWithAbsDfSoftplusSigma.log_pmf} Log probability mass function. @@ -13711,7 +13477,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13727,7 +13492,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob(value, name='log_prob', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_prob} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob(value, name='log_prob')` {#StudentTWithAbsDfSoftplusSigma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -13736,7 +13501,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13747,7 +13511,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_survival_function} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_survival_function(value, name='log_survival_function')` {#StudentTWithAbsDfSoftplusSigma.log_survival_function} Log survival function. @@ -13767,7 +13531,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13872,7 +13635,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pdf(value, name='pdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.pdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pdf(value, name='pdf')` {#StudentTWithAbsDfSoftplusSigma.pdf} Probability density function. @@ -13881,7 +13644,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13897,7 +13659,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pmf(value, name='pmf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.pmf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pmf(value, name='pmf')` {#StudentTWithAbsDfSoftplusSigma.pmf} Probability mass function. @@ -13906,7 +13668,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13922,7 +13683,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.prob(value, name='prob', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.prob} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.prob(value, name='prob')` {#StudentTWithAbsDfSoftplusSigma.prob} Probability density/mass function (depending on `is_continuous`). @@ -13931,7 +13692,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13957,7 +13717,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.sample} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample(sample_shape=(), seed=None, name='sample')` {#StudentTWithAbsDfSoftplusSigma.sample} Generate samples of the specified shape. @@ -13970,7 +13730,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -13994,7 +13753,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.survival_function(value, name='survival_function', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.survival_function} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.survival_function(value, name='survival_function')` {#StudentTWithAbsDfSoftplusSigma.survival_function} Survival function. @@ -14011,7 +13770,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14155,7 +13913,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Uniform.cdf(value, name='cdf', **condition_kwargs)` {#Uniform.cdf} +#### `tf.contrib.distributions.Uniform.cdf(value, name='cdf')` {#Uniform.cdf} Cumulative distribution function. @@ -14170,7 +13928,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14304,7 +14061,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Uniform.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Uniform.log_cdf} +#### `tf.contrib.distributions.Uniform.log_cdf(value, name='log_cdf')` {#Uniform.log_cdf} Log cumulative distribution function. @@ -14323,7 +14080,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14334,7 +14090,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Uniform.log_pdf} +#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf')` {#Uniform.log_pdf} Log probability density function. @@ -14343,7 +14099,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14359,7 +14114,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Uniform.log_pmf} +#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf')` {#Uniform.log_pmf} Log probability mass function. @@ -14368,7 +14123,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14384,7 +14138,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob', **condition_kwargs)` {#Uniform.log_prob} +#### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob')` {#Uniform.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -14393,7 +14147,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14404,7 +14157,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Uniform.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Uniform.log_survival_function} +#### `tf.contrib.distributions.Uniform.log_survival_function(value, name='log_survival_function')` {#Uniform.log_survival_function} Log survival function. @@ -14424,7 +14177,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14516,7 +14268,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Uniform.pdf(value, name='pdf', **condition_kwargs)` {#Uniform.pdf} +#### `tf.contrib.distributions.Uniform.pdf(value, name='pdf')` {#Uniform.pdf} Probability density function. @@ -14525,7 +14277,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14541,7 +14292,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Uniform.pmf(value, name='pmf', **condition_kwargs)` {#Uniform.pmf} +#### `tf.contrib.distributions.Uniform.pmf(value, name='pmf')` {#Uniform.pmf} Probability mass function. @@ -14550,7 +14301,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14566,7 +14316,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Uniform.prob(value, name='prob', **condition_kwargs)` {#Uniform.prob} +#### `tf.contrib.distributions.Uniform.prob(value, name='prob')` {#Uniform.prob} Probability density/mass function (depending on `is_continuous`). @@ -14575,7 +14325,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14608,7 +14357,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Uniform.sample} +#### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample')` {#Uniform.sample} Generate samples of the specified shape. @@ -14621,7 +14370,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14638,7 +14386,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Uniform.survival_function(value, name='survival_function', **condition_kwargs)` {#Uniform.survival_function} +#### `tf.contrib.distributions.Uniform.survival_function(value, name='survival_function')` {#Uniform.survival_function} Survival function. @@ -14655,7 +14403,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14808,7 +14555,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiag.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.cdf(value, name='cdf')` {#MultivariateNormalDiag.cdf} Cumulative distribution function. @@ -14823,7 +14570,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14957,7 +14703,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiag.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiag.log_cdf} Log cumulative distribution function. @@ -14976,7 +14722,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -14987,7 +14732,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiag.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiag.log_pdf} Log probability density function. @@ -14996,7 +14741,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15012,7 +14756,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiag.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiag.log_pmf} Log probability mass function. @@ -15021,7 +14765,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15037,7 +14780,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiag.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob')` {#MultivariateNormalDiag.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -15062,7 +14805,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15080,7 +14822,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiag.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiag.log_survival_function} Log survival function. @@ -15100,7 +14842,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15199,7 +14940,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiag.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf')` {#MultivariateNormalDiag.pdf} Probability density function. @@ -15208,7 +14949,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15224,7 +14964,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiag.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf')` {#MultivariateNormalDiag.pmf} Probability mass function. @@ -15233,7 +14973,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15249,7 +14988,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiag.prob} +#### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob')` {#MultivariateNormalDiag.prob} Probability density/mass function (depending on `is_continuous`). @@ -15274,7 +15013,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15300,7 +15038,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiag.sample} +#### `tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiag.sample} Generate samples of the specified shape. @@ -15313,7 +15051,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15344,7 +15081,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiag.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiag.survival_function(value, name='survival_function')` {#MultivariateNormalDiag.survival_function} Survival function. @@ -15361,7 +15098,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15500,7 +15236,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalFull.cdf} +#### `tf.contrib.distributions.MultivariateNormalFull.cdf(value, name='cdf')` {#MultivariateNormalFull.cdf} Cumulative distribution function. @@ -15515,7 +15251,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15649,7 +15384,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalFull.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_cdf(value, name='log_cdf')` {#MultivariateNormalFull.log_cdf} Log cumulative distribution function. @@ -15668,7 +15403,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15679,7 +15413,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalFull.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf')` {#MultivariateNormalFull.log_pdf} Log probability density function. @@ -15688,7 +15422,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15704,7 +15437,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalFull.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf')` {#MultivariateNormalFull.log_pmf} Log probability mass function. @@ -15713,7 +15446,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15729,7 +15461,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalFull.log_prob} +#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob')` {#MultivariateNormalFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -15754,7 +15486,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15772,7 +15503,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalFull.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalFull.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalFull.log_survival_function} Log survival function. @@ -15792,7 +15523,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15891,7 +15621,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalFull.pdf} +#### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf')` {#MultivariateNormalFull.pdf} Probability density function. @@ -15900,7 +15630,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15916,7 +15645,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalFull.pmf} +#### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf')` {#MultivariateNormalFull.pmf} Probability mass function. @@ -15925,7 +15654,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15941,7 +15669,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalFull.prob} +#### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob')` {#MultivariateNormalFull.prob} Probability density/mass function (depending on `is_continuous`). @@ -15966,7 +15694,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -15992,7 +15719,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalFull.sample} +#### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalFull.sample} Generate samples of the specified shape. @@ -16005,7 +15732,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16036,7 +15762,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalFull.survival_function} +#### `tf.contrib.distributions.MultivariateNormalFull.survival_function(value, name='survival_function')` {#MultivariateNormalFull.survival_function} Survival function. @@ -16053,7 +15779,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16201,7 +15926,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalCholesky.cdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.cdf(value, name='cdf')` {#MultivariateNormalCholesky.cdf} Cumulative distribution function. @@ -16216,7 +15941,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16350,7 +16074,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalCholesky.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf')` {#MultivariateNormalCholesky.log_cdf} Log cumulative distribution function. @@ -16369,7 +16093,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16380,7 +16103,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalCholesky.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf')` {#MultivariateNormalCholesky.log_pdf} Log probability density function. @@ -16389,7 +16112,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16405,7 +16127,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalCholesky.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf')` {#MultivariateNormalCholesky.log_pmf} Log probability mass function. @@ -16414,7 +16136,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16430,7 +16151,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalCholesky.log_prob} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob')` {#MultivariateNormalCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -16455,7 +16176,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16473,7 +16193,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalCholesky.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalCholesky.log_survival_function} Log survival function. @@ -16493,7 +16213,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16592,7 +16311,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalCholesky.pdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf')` {#MultivariateNormalCholesky.pdf} Probability density function. @@ -16601,7 +16320,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16617,7 +16335,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalCholesky.pmf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf')` {#MultivariateNormalCholesky.pmf} Probability mass function. @@ -16626,7 +16344,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16642,7 +16359,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalCholesky.prob} +#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob')` {#MultivariateNormalCholesky.prob} Probability density/mass function (depending on `is_continuous`). @@ -16667,7 +16384,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16693,7 +16409,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalCholesky.sample} +#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalCholesky.sample} Generate samples of the specified shape. @@ -16706,7 +16422,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16737,7 +16452,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalCholesky.survival_function} +#### `tf.contrib.distributions.MultivariateNormalCholesky.survival_function(value, name='survival_function')` {#MultivariateNormalCholesky.survival_function} Survival function. @@ -16754,7 +16469,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -16928,7 +16642,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.cdf(value, name='cdf')` {#MultivariateNormalDiagPlusVDVT.cdf} Cumulative distribution function. @@ -16943,7 +16657,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17077,7 +16790,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiagPlusVDVT.log_cdf} Log cumulative distribution function. @@ -17096,7 +16809,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17107,7 +16819,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagPlusVDVT.log_pdf} Log probability density function. @@ -17116,7 +16828,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17132,7 +16843,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagPlusVDVT.log_pmf} Log probability mass function. @@ -17141,7 +16852,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17157,7 +16867,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob')` {#MultivariateNormalDiagPlusVDVT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -17182,7 +16892,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17200,7 +16909,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiagPlusVDVT.log_survival_function} Log survival function. @@ -17220,7 +16929,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17319,7 +17027,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf')` {#MultivariateNormalDiagPlusVDVT.pdf} Probability density function. @@ -17328,7 +17036,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17344,7 +17051,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf')` {#MultivariateNormalDiagPlusVDVT.pmf} Probability mass function. @@ -17353,7 +17060,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17369,7 +17075,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.prob} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob')` {#MultivariateNormalDiagPlusVDVT.prob} Probability density/mass function (depending on `is_continuous`). @@ -17394,7 +17100,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17420,7 +17125,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.sample} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiagPlusVDVT.sample} Generate samples of the specified shape. @@ -17433,7 +17138,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17464,7 +17168,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function')` {#MultivariateNormalDiagPlusVDVT.survival_function} Survival function. @@ -17481,7 +17185,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17559,7 +17262,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.cdf(value, name='cdf')` {#MultivariateNormalDiagWithSoftplusStDev.cdf} Cumulative distribution function. @@ -17574,7 +17277,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17708,7 +17410,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiagWithSoftplusStDev.log_cdf} Log cumulative distribution function. @@ -17727,7 +17429,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17738,7 +17439,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pdf} Log probability density function. @@ -17747,7 +17448,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17763,7 +17463,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pmf} Log probability mass function. @@ -17772,7 +17472,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17788,7 +17487,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob')` {#MultivariateNormalDiagWithSoftplusStDev.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -17813,7 +17512,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17831,7 +17529,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiagWithSoftplusStDev.log_survival_function} Log survival function. @@ -17851,7 +17549,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17950,7 +17647,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf')` {#MultivariateNormalDiagWithSoftplusStDev.pdf} Probability density function. @@ -17959,7 +17656,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -17975,7 +17671,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf')` {#MultivariateNormalDiagWithSoftplusStDev.pmf} Probability mass function. @@ -17984,7 +17680,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18000,7 +17695,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.prob} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob')` {#MultivariateNormalDiagWithSoftplusStDev.prob} Probability density/mass function (depending on `is_continuous`). @@ -18025,7 +17720,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18051,7 +17745,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.sample} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiagWithSoftplusStDev.sample} Generate samples of the specified shape. @@ -18064,7 +17758,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18095,7 +17788,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.survival_function(value, name='survival_function')` {#MultivariateNormalDiagWithSoftplusStDev.survival_function} Survival function. @@ -18112,7 +17805,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18294,7 +17986,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Dirichlet.cdf(value, name='cdf', **condition_kwargs)` {#Dirichlet.cdf} +#### `tf.contrib.distributions.Dirichlet.cdf(value, name='cdf')` {#Dirichlet.cdf} Cumulative distribution function. @@ -18309,7 +18001,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18443,7 +18134,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Dirichlet.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Dirichlet.log_cdf} +#### `tf.contrib.distributions.Dirichlet.log_cdf(value, name='log_cdf')` {#Dirichlet.log_cdf} Log cumulative distribution function. @@ -18462,7 +18153,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18473,7 +18163,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Dirichlet.log_pdf} +#### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf')` {#Dirichlet.log_pdf} Log probability density function. @@ -18482,7 +18172,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18498,7 +18187,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Dirichlet.log_pmf} +#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf')` {#Dirichlet.log_pmf} Log probability mass function. @@ -18507,7 +18196,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18523,7 +18211,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob', **condition_kwargs)` {#Dirichlet.log_prob} +#### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob')` {#Dirichlet.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -18540,7 +18228,6 @@ in `self.alpha`. `x` is only legal if it sums up to one. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18551,7 +18238,7 @@ in `self.alpha`. `x` is only legal if it sums up to one. - - - -#### `tf.contrib.distributions.Dirichlet.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Dirichlet.log_survival_function} +#### `tf.contrib.distributions.Dirichlet.log_survival_function(value, name='log_survival_function')` {#Dirichlet.log_survival_function} Log survival function. @@ -18571,7 +18258,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18670,7 +18356,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf', **condition_kwargs)` {#Dirichlet.pdf} +#### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf')` {#Dirichlet.pdf} Probability density function. @@ -18679,7 +18365,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18695,7 +18380,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf', **condition_kwargs)` {#Dirichlet.pmf} +#### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf')` {#Dirichlet.pmf} Probability mass function. @@ -18704,7 +18389,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18720,7 +18404,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Dirichlet.prob(value, name='prob', **condition_kwargs)` {#Dirichlet.prob} +#### `tf.contrib.distributions.Dirichlet.prob(value, name='prob')` {#Dirichlet.prob} Probability density/mass function (depending on `is_continuous`). @@ -18737,7 +18421,6 @@ in `self.alpha`. `x` is only legal if it sums up to one. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18763,7 +18446,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Dirichlet.sample} +#### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample')` {#Dirichlet.sample} Generate samples of the specified shape. @@ -18776,7 +18459,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -18793,7 +18475,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Dirichlet.survival_function(value, name='survival_function', **condition_kwargs)` {#Dirichlet.survival_function} +#### `tf.contrib.distributions.Dirichlet.survival_function(value, name='survival_function')` {#Dirichlet.survival_function} Survival function. @@ -18810,7 +18492,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19001,7 +18682,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf', **condition_kwargs)` {#DirichletMultinomial.cdf} +#### `tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf')` {#DirichletMultinomial.cdf} Cumulative distribution function. @@ -19016,7 +18697,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19150,7 +18830,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#DirichletMultinomial.log_cdf} +#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf')` {#DirichletMultinomial.log_cdf} Log cumulative distribution function. @@ -19169,7 +18849,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19180,7 +18859,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#DirichletMultinomial.log_pdf} +#### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf')` {#DirichletMultinomial.log_pdf} Log probability density function. @@ -19189,7 +18868,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19205,7 +18883,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#DirichletMultinomial.log_pmf} +#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf')` {#DirichletMultinomial.log_pmf} Log probability mass function. @@ -19214,7 +18892,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19230,7 +18907,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob', **condition_kwargs)` {#DirichletMultinomial.log_prob} +#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob')` {#DirichletMultinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -19254,7 +18931,6 @@ it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19265,7 +18941,7 @@ it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#DirichletMultinomial.log_survival_function} +#### `tf.contrib.distributions.DirichletMultinomial.log_survival_function(value, name='log_survival_function')` {#DirichletMultinomial.log_survival_function} Log survival function. @@ -19285,7 +18961,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19384,7 +19059,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf', **condition_kwargs)` {#DirichletMultinomial.pdf} +#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf')` {#DirichletMultinomial.pdf} Probability density function. @@ -19393,7 +19068,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19409,7 +19083,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf', **condition_kwargs)` {#DirichletMultinomial.pmf} +#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf')` {#DirichletMultinomial.pmf} Probability mass function. @@ -19418,7 +19092,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19434,7 +19107,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob', **condition_kwargs)` {#DirichletMultinomial.prob} +#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob')` {#DirichletMultinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -19458,7 +19131,6 @@ it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19484,7 +19156,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#DirichletMultinomial.sample} +#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample')` {#DirichletMultinomial.sample} Generate samples of the specified shape. @@ -19497,7 +19169,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19514,7 +19185,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.DirichletMultinomial.survival_function(value, name='survival_function', **condition_kwargs)` {#DirichletMultinomial.survival_function} +#### `tf.contrib.distributions.DirichletMultinomial.survival_function(value, name='survival_function')` {#DirichletMultinomial.survival_function} Survival function. @@ -19531,7 +19202,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19723,7 +19393,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Multinomial.cdf(value, name='cdf', **condition_kwargs)` {#Multinomial.cdf} +#### `tf.contrib.distributions.Multinomial.cdf(value, name='cdf')` {#Multinomial.cdf} Cumulative distribution function. @@ -19738,7 +19408,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19872,7 +19541,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Multinomial.log_cdf} +#### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf')` {#Multinomial.log_cdf} Log cumulative distribution function. @@ -19891,7 +19560,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19902,7 +19570,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Multinomial.log_pdf} +#### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf')` {#Multinomial.log_pdf} Log probability density function. @@ -19911,7 +19579,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19927,7 +19594,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Multinomial.log_pmf} +#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf')` {#Multinomial.log_pmf} Log probability mass function. @@ -19936,7 +19603,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19952,7 +19618,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob', **condition_kwargs)` {#Multinomial.log_prob} +#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob')` {#Multinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -19976,7 +19642,6 @@ if it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -19987,7 +19652,7 @@ if it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Multinomial.log_survival_function} +#### `tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function')` {#Multinomial.log_survival_function} Log survival function. @@ -20007,7 +19672,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20122,7 +19786,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf', **condition_kwargs)` {#Multinomial.pdf} +#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf')` {#Multinomial.pdf} Probability density function. @@ -20131,7 +19795,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20147,7 +19810,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf', **condition_kwargs)` {#Multinomial.pmf} +#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf')` {#Multinomial.pmf} Probability mass function. @@ -20156,7 +19819,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20172,7 +19834,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Multinomial.prob(value, name='prob', **condition_kwargs)` {#Multinomial.prob} +#### `tf.contrib.distributions.Multinomial.prob(value, name='prob')` {#Multinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -20196,7 +19858,6 @@ if it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20222,7 +19883,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Multinomial.sample} +#### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample')` {#Multinomial.sample} Generate samples of the specified shape. @@ -20235,7 +19896,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20252,7 +19912,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Multinomial.survival_function(value, name='survival_function', **condition_kwargs)` {#Multinomial.survival_function} +#### `tf.contrib.distributions.Multinomial.survival_function(value, name='survival_function')` {#Multinomial.survival_function} Survival function. @@ -20269,7 +19929,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20426,7 +20085,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.WishartCholesky.cdf(value, name='cdf', **condition_kwargs)` {#WishartCholesky.cdf} +#### `tf.contrib.distributions.WishartCholesky.cdf(value, name='cdf')` {#WishartCholesky.cdf} Cumulative distribution function. @@ -20441,7 +20100,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20596,7 +20254,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf', **condition_kwargs)` {#WishartCholesky.log_cdf} +#### `tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf')` {#WishartCholesky.log_cdf} Log cumulative distribution function. @@ -20615,7 +20273,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20633,7 +20290,7 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf', **condition_kwargs)` {#WishartCholesky.log_pdf} +#### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf')` {#WishartCholesky.log_pdf} Log probability density function. @@ -20642,7 +20299,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20658,7 +20314,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf', **condition_kwargs)` {#WishartCholesky.log_pmf} +#### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf')` {#WishartCholesky.log_pmf} Log probability mass function. @@ -20667,7 +20323,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20683,7 +20338,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob', **condition_kwargs)` {#WishartCholesky.log_prob} +#### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob')` {#WishartCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -20692,7 +20347,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20703,7 +20357,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartCholesky.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#WishartCholesky.log_survival_function} +#### `tf.contrib.distributions.WishartCholesky.log_survival_function(value, name='log_survival_function')` {#WishartCholesky.log_survival_function} Log survival function. @@ -20723,7 +20377,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20822,7 +20475,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf', **condition_kwargs)` {#WishartCholesky.pdf} +#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf')` {#WishartCholesky.pdf} Probability density function. @@ -20831,7 +20484,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20847,7 +20499,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf', **condition_kwargs)` {#WishartCholesky.pmf} +#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf')` {#WishartCholesky.pmf} Probability mass function. @@ -20856,7 +20508,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20872,7 +20523,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob', **condition_kwargs)` {#WishartCholesky.prob} +#### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob')` {#WishartCholesky.prob} Probability density/mass function (depending on `is_continuous`). @@ -20881,7 +20532,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20907,7 +20557,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#WishartCholesky.sample} +#### `tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample')` {#WishartCholesky.sample} Generate samples of the specified shape. @@ -20920,7 +20570,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -20951,7 +20600,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.WishartCholesky.survival_function(value, name='survival_function', **condition_kwargs)` {#WishartCholesky.survival_function} +#### `tf.contrib.distributions.WishartCholesky.survival_function(value, name='survival_function')` {#WishartCholesky.survival_function} Survival function. @@ -20968,7 +20617,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21121,7 +20769,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.WishartFull.cdf(value, name='cdf', **condition_kwargs)` {#WishartFull.cdf} +#### `tf.contrib.distributions.WishartFull.cdf(value, name='cdf')` {#WishartFull.cdf} Cumulative distribution function. @@ -21136,7 +20784,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21291,7 +20938,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf', **condition_kwargs)` {#WishartFull.log_cdf} +#### `tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf')` {#WishartFull.log_cdf} Log cumulative distribution function. @@ -21310,7 +20957,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21328,7 +20974,7 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf', **condition_kwargs)` {#WishartFull.log_pdf} +#### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf')` {#WishartFull.log_pdf} Log probability density function. @@ -21337,7 +20983,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21353,7 +20998,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf', **condition_kwargs)` {#WishartFull.log_pmf} +#### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf')` {#WishartFull.log_pmf} Log probability mass function. @@ -21362,7 +21007,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21378,7 +21022,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob', **condition_kwargs)` {#WishartFull.log_prob} +#### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob')` {#WishartFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -21387,7 +21031,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21398,7 +21041,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartFull.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#WishartFull.log_survival_function} +#### `tf.contrib.distributions.WishartFull.log_survival_function(value, name='log_survival_function')` {#WishartFull.log_survival_function} Log survival function. @@ -21418,7 +21061,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21517,7 +21159,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf', **condition_kwargs)` {#WishartFull.pdf} +#### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf')` {#WishartFull.pdf} Probability density function. @@ -21526,7 +21168,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21542,7 +21183,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf', **condition_kwargs)` {#WishartFull.pmf} +#### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf')` {#WishartFull.pmf} Probability mass function. @@ -21551,7 +21192,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21567,7 +21207,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.WishartFull.prob(value, name='prob', **condition_kwargs)` {#WishartFull.prob} +#### `tf.contrib.distributions.WishartFull.prob(value, name='prob')` {#WishartFull.prob} Probability density/mass function (depending on `is_continuous`). @@ -21576,7 +21216,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21602,7 +21241,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#WishartFull.sample} +#### `tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample')` {#WishartFull.sample} Generate samples of the specified shape. @@ -21615,7 +21254,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21646,7 +21284,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.WishartFull.survival_function(value, name='survival_function', **condition_kwargs)` {#WishartFull.survival_function} +#### `tf.contrib.distributions.WishartFull.survival_function(value, name='survival_function')` {#WishartFull.survival_function} Survival function. @@ -21663,7 +21301,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -21960,7 +21597,7 @@ Function transforming x => y. - - - -#### `tf.contrib.distributions.TransformedDistribution.cdf(value, name='cdf', **condition_kwargs)` {#TransformedDistribution.cdf} +#### `tf.contrib.distributions.TransformedDistribution.cdf(value, name='cdf')` {#TransformedDistribution.cdf} Cumulative distribution function. @@ -21970,20 +21607,11 @@ Given random variable `X`, the cumulative distribution function `cdf` is: cdf(x) := P[X <= x] ``` - -Additional documentation from `TransformedDistribution`: - -##### `condition_kwargs`: - -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. - ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22124,7 +21752,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#TransformedDistribution.log_cdf} +#### `tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf')` {#TransformedDistribution.log_cdf} Log cumulative distribution function. @@ -22138,20 +21766,11 @@ Often, a numerical approximation can be used for `log_cdf(x)` that yields a more accurate answer than simply taking the logarithm of the `cdf` when `x << -1`. - -Additional documentation from `TransformedDistribution`: - -##### `condition_kwargs`: - -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. - ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22162,7 +21781,7 @@ Additional documentation from `TransformedDistribution`: - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#TransformedDistribution.log_pdf} +#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf')` {#TransformedDistribution.log_pdf} Log probability density function. @@ -22171,7 +21790,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22187,7 +21805,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#TransformedDistribution.log_pmf} +#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf')` {#TransformedDistribution.log_pmf} Log probability mass function. @@ -22196,7 +21814,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22212,7 +21829,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob', **condition_kwargs)` {#TransformedDistribution.log_prob} +#### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob')` {#TransformedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -22220,22 +21837,16 @@ Log probability density/mass function (depending on `is_continuous`). Additional documentation from `TransformedDistribution`: Implements `(log o p o g^{-1})(y) + (log o abs o det o J o g^{-1})(y)`, - where `g^{-1}` is the inverse of `transform`. - - Also raises a `ValueError` if `inverse` was not provided to the - distribution and `y` was not returned from `sample`. +where `g^{-1}` is the inverse of `transform`. -##### `condition_kwargs`: - -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. +Also raises a `ValueError` if `inverse` was not provided to the +distribution and `y` was not returned from `sample`. ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22246,7 +21857,7 @@ Implements `(log o p o g^{-1})(y) + (log o abs o det o J o g^{-1})(y)`, - - - -#### `tf.contrib.distributions.TransformedDistribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#TransformedDistribution.log_survival_function} +#### `tf.contrib.distributions.TransformedDistribution.log_survival_function(value, name='log_survival_function')` {#TransformedDistribution.log_survival_function} Log survival function. @@ -22261,20 +21872,11 @@ log_survival_function(x) = Log[ P[X > x] ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. - -Additional documentation from `TransformedDistribution`: - -##### `condition_kwargs`: - -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. - ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22366,7 +21968,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf', **condition_kwargs)` {#TransformedDistribution.pdf} +#### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf')` {#TransformedDistribution.pdf} Probability density function. @@ -22375,7 +21977,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22391,7 +21992,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf', **condition_kwargs)` {#TransformedDistribution.pmf} +#### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf')` {#TransformedDistribution.pmf} Probability mass function. @@ -22400,7 +22001,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22416,7 +22016,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob', **condition_kwargs)` {#TransformedDistribution.prob} +#### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob')` {#TransformedDistribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -22424,22 +22024,16 @@ Probability density/mass function (depending on `is_continuous`). Additional documentation from `TransformedDistribution`: Implements `p(g^{-1}(y)) det|J(g^{-1}(y))|`, where `g^{-1}` is the - inverse of `transform`. - - Also raises a `ValueError` if `inverse` was not provided to the - distribution and `y` was not returned from `sample`. +inverse of `transform`. -##### `condition_kwargs`: - -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. +Also raises a `ValueError` if `inverse` was not provided to the +distribution and `y` was not returned from `sample`. ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22465,7 +22059,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#TransformedDistribution.sample} +#### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#TransformedDistribution.sample} Generate samples of the specified shape. @@ -22478,7 +22072,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22495,7 +22088,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.TransformedDistribution.survival_function(value, name='survival_function', **condition_kwargs)` {#TransformedDistribution.survival_function} +#### `tf.contrib.distributions.TransformedDistribution.survival_function(value, name='survival_function')` {#TransformedDistribution.survival_function} Survival function. @@ -22507,20 +22100,11 @@ survival_function(x) = P[X > x] = 1 - cdf(x). ``` - -Additional documentation from `TransformedDistribution`: - -##### `condition_kwargs`: - -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. - ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22677,7 +22261,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.QuantizedDistribution.cdf(value, name='cdf', **condition_kwargs)` {#QuantizedDistribution.cdf} +#### `tf.contrib.distributions.QuantizedDistribution.cdf(value, name='cdf')` {#QuantizedDistribution.cdf} Cumulative distribution function. @@ -22710,7 +22294,6 @@ The base distribution's `cdf` method must be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22851,7 +22434,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#QuantizedDistribution.log_cdf} +#### `tf.contrib.distributions.QuantizedDistribution.log_cdf(value, name='log_cdf')` {#QuantizedDistribution.log_cdf} Log cumulative distribution function. @@ -22888,7 +22471,6 @@ The base distribution's `log_cdf` method must be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22899,7 +22481,7 @@ The base distribution's `log_cdf` method must be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#QuantizedDistribution.log_pdf} +#### `tf.contrib.distributions.QuantizedDistribution.log_pdf(value, name='log_pdf')` {#QuantizedDistribution.log_pdf} Log probability density function. @@ -22908,7 +22490,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22924,7 +22505,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#QuantizedDistribution.log_pmf} +#### `tf.contrib.distributions.QuantizedDistribution.log_pmf(value, name='log_pmf')` {#QuantizedDistribution.log_pmf} Log probability mass function. @@ -22933,7 +22514,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22949,7 +22529,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob', **condition_kwargs)` {#QuantizedDistribution.log_prob} +#### `tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob')` {#QuantizedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -22976,7 +22556,6 @@ must also be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -22987,7 +22566,7 @@ must also be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#QuantizedDistribution.log_survival_function} +#### `tf.contrib.distributions.QuantizedDistribution.log_survival_function(value, name='log_survival_function')` {#QuantizedDistribution.log_survival_function} Log survival function. @@ -23025,7 +22604,6 @@ The base distribution's `log_cdf` method must be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23117,7 +22695,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.pdf(value, name='pdf', **condition_kwargs)` {#QuantizedDistribution.pdf} +#### `tf.contrib.distributions.QuantizedDistribution.pdf(value, name='pdf')` {#QuantizedDistribution.pdf} Probability density function. @@ -23126,7 +22704,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23142,7 +22719,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.pmf(value, name='pmf', **condition_kwargs)` {#QuantizedDistribution.pmf} +#### `tf.contrib.distributions.QuantizedDistribution.pmf(value, name='pmf')` {#QuantizedDistribution.pmf} Probability mass function. @@ -23151,7 +22728,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23167,7 +22743,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob', **condition_kwargs)` {#QuantizedDistribution.prob} +#### `tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob')` {#QuantizedDistribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -23194,7 +22770,6 @@ also be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23220,7 +22795,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#QuantizedDistribution.sample} +#### `tf.contrib.distributions.QuantizedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#QuantizedDistribution.sample} Generate samples of the specified shape. @@ -23233,7 +22808,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23250,7 +22824,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.QuantizedDistribution.survival_function(value, name='survival_function', **condition_kwargs)` {#QuantizedDistribution.survival_function} +#### `tf.contrib.distributions.QuantizedDistribution.survival_function(value, name='survival_function')` {#QuantizedDistribution.survival_function} Survival function. @@ -23285,7 +22859,6 @@ The base distribution's `cdf` method must be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23420,7 +22993,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Mixture.cdf(value, name='cdf', **condition_kwargs)` {#Mixture.cdf} +#### `tf.contrib.distributions.Mixture.cdf(value, name='cdf')` {#Mixture.cdf} Cumulative distribution function. @@ -23435,7 +23008,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23622,7 +23194,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Mixture.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Mixture.log_cdf} +#### `tf.contrib.distributions.Mixture.log_cdf(value, name='log_cdf')` {#Mixture.log_cdf} Log cumulative distribution function. @@ -23641,7 +23213,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23652,7 +23223,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Mixture.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Mixture.log_pdf} +#### `tf.contrib.distributions.Mixture.log_pdf(value, name='log_pdf')` {#Mixture.log_pdf} Log probability density function. @@ -23661,7 +23232,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23677,7 +23247,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Mixture.log_pmf} +#### `tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf')` {#Mixture.log_pmf} Log probability mass function. @@ -23686,7 +23256,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23702,7 +23271,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Mixture.log_prob(value, name='log_prob', **condition_kwargs)` {#Mixture.log_prob} +#### `tf.contrib.distributions.Mixture.log_prob(value, name='log_prob')` {#Mixture.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -23711,7 +23280,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23722,7 +23290,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Mixture.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Mixture.log_survival_function} +#### `tf.contrib.distributions.Mixture.log_survival_function(value, name='log_survival_function')` {#Mixture.log_survival_function} Log survival function. @@ -23742,7 +23310,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23841,7 +23408,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Mixture.pdf(value, name='pdf', **condition_kwargs)` {#Mixture.pdf} +#### `tf.contrib.distributions.Mixture.pdf(value, name='pdf')` {#Mixture.pdf} Probability density function. @@ -23850,7 +23417,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23866,7 +23432,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Mixture.pmf(value, name='pmf', **condition_kwargs)` {#Mixture.pmf} +#### `tf.contrib.distributions.Mixture.pmf(value, name='pmf')` {#Mixture.pmf} Probability mass function. @@ -23875,7 +23441,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23891,7 +23456,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Mixture.prob(value, name='prob', **condition_kwargs)` {#Mixture.prob} +#### `tf.contrib.distributions.Mixture.prob(value, name='prob')` {#Mixture.prob} Probability density/mass function (depending on `is_continuous`). @@ -23900,7 +23465,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23926,7 +23490,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Mixture.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Mixture.sample} +#### `tf.contrib.distributions.Mixture.sample(sample_shape=(), seed=None, name='sample')` {#Mixture.sample} Generate samples of the specified shape. @@ -23939,7 +23503,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -23956,7 +23519,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Mixture.survival_function(value, name='survival_function', **condition_kwargs)` {#Mixture.survival_function} +#### `tf.contrib.distributions.Mixture.survival_function(value, name='survival_function')` {#Mixture.survival_function} Survival function. @@ -23973,7 +23536,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -24230,3 +23792,996 @@ softplus_inverse = log(exp(x) - 1.) `Tensor`. Has the same type/shape as input `x`. + +## Other Functions and Classes +- - - + +### `class tf.contrib.distributions.ConditionalDistribution` {#ConditionalDistribution} + +Distribution that supports intrinsic parameters (local latents). + +Subclasses of this distribution may have additional keyword arguments passed +to their sample-based methods (i.e. `sample`, `log_prob`, etc.). +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.__init__(dtype, is_continuous, reparameterization_type, validate_args, allow_nan_stats, parameters=None, graph_parents=None, name=None)` {#ConditionalDistribution.__init__} + +Constructs the `Distribution`. + +**This is a private method for subclass use.** + +##### Args: + + +* <b>`dtype`</b>: The type of the event samples. `None` implies no type-enforcement. +* <b>`is_continuous`</b>: Python boolean. If `True` this + `Distribution` is continuous over its supported domain. +* <b>`reparameterization_type`</b>: Instance of `ReparameterizationType`. + If `distributions.FULLY_REPARAMETERIZED`, this + `Distribution` can be reparameterized in terms of some standard + distribution with a function whose Jacobian is constant for the support + of the standard distribution. If `distributions.NOT_REPARAMETERIZED`, + then no such reparameterization is available. +* <b>`validate_args`</b>: Python boolean. Whether to validate input with asserts. + If `validate_args` is `False`, and the inputs are invalid, + correct behavior is not guaranteed. +* <b>`allow_nan_stats`</b>: Python boolean. If `False`, raise an + exception if a statistic (e.g., mean, mode) is undefined for any batch + member. If True, batch members with valid parameters leading to + undefined statistics will return `NaN` for this statistic. +* <b>`parameters`</b>: Python dictionary of parameters used to instantiate this + `Distribution`. +* <b>`graph_parents`</b>: Python list of graph prerequisites of this `Distribution`. +* <b>`name`</b>: A name for this distribution. Default: subclass name. + +##### Raises: + + +* <b>`ValueError`</b>: if any member of graph_parents is `None` or not a `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.allow_nan_stats` {#ConditionalDistribution.allow_nan_stats} + +Python boolean describing behavior when a stat is undefined. + +Stats return +/- infinity when it makes sense. E.g., the variance +of a Cauchy distribution is infinity. However, sometimes the +statistic is undefined, e.g., if a distribution's pdf does not achieve a +maximum within the support of the distribution, the mode is undefined. +If the mean is undefined, then by definition the variance is undefined. +E.g. the mean for Student's T for df = 1 is undefined (no clear way to say +it is either + or - infinity), so the variance = E[(X - mean)^2] is also +undefined. + +##### Returns: + + +* <b>`allow_nan_stats`</b>: Python boolean. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.batch_shape(name='batch_shape')` {#ConditionalDistribution.batch_shape} + +Shape of a single sample from a single event index as a 1-D `Tensor`. + +The product of the dimensions of the `batch_shape` is the number of +independent distributions of this kind the instance represents. + +##### Args: + + +* <b>`name`</b>: name to give to the op + +##### Returns: + + +* <b>`batch_shape`</b>: `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.cdf(*args, **kwargs)` {#ConditionalDistribution.cdf} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.copy(**override_parameters_kwargs)` {#ConditionalDistribution.copy} + +Creates a deep copy of the distribution. + +Note: the copy distribution may continue to depend on the original +intialization arguments. + +##### Args: + + +* <b>`**override_parameters_kwargs`</b>: String/value dictionary of initialization + arguments to override with new values. + +##### Returns: + + +* <b>`distribution`</b>: A new instance of `type(self)` intitialized from the union + of self.parameters and override_parameters_kwargs, i.e., + `dict(self.parameters, **override_parameters_kwargs)`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.dtype` {#ConditionalDistribution.dtype} + +The `DType` of `Tensor`s handled by this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.entropy(name='entropy')` {#ConditionalDistribution.entropy} + +Shannon entropy in nats. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.event_shape(name='event_shape')` {#ConditionalDistribution.event_shape} + +Shape of a single sample from a single batch as a 1-D int32 `Tensor`. + +##### Args: + + +* <b>`name`</b>: name to give to the op + +##### Returns: + + +* <b>`event_shape`</b>: `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.get_batch_shape()` {#ConditionalDistribution.get_batch_shape} + +Shape of a single sample from a single event index as a `TensorShape`. + +Same meaning as `batch_shape`. May be only partially defined. + +##### Returns: + + +* <b>`batch_shape`</b>: `TensorShape`, possibly unknown. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.get_event_shape()` {#ConditionalDistribution.get_event_shape} + +Shape of a single sample from a single batch as a `TensorShape`. + +Same meaning as `event_shape`. May be only partially defined. + +##### Returns: + + +* <b>`event_shape`</b>: `TensorShape`, possibly unknown. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.is_continuous` {#ConditionalDistribution.is_continuous} + + + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.is_scalar_batch(name='is_scalar_batch')` {#ConditionalDistribution.is_scalar_batch} + +Indicates that `batch_shape == []`. + +##### Args: + + +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`is_scalar_batch`</b>: `Boolean` `scalar` `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.is_scalar_event(name='is_scalar_event')` {#ConditionalDistribution.is_scalar_event} + +Indicates that `event_shape == []`. + +##### Args: + + +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`is_scalar_event`</b>: `Boolean` `scalar` `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.log_cdf(*args, **kwargs)` {#ConditionalDistribution.log_cdf} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.log_pdf(value, name='log_pdf')` {#ConditionalDistribution.log_pdf} + +Log probability density function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if not `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.log_pmf(value, name='log_pmf')` {#ConditionalDistribution.log_pmf} + +Log probability mass function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.log_prob(*args, **kwargs)` {#ConditionalDistribution.log_prob} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.log_survival_function(*args, **kwargs)` {#ConditionalDistribution.log_survival_function} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.mean(name='mean')` {#ConditionalDistribution.mean} + +Mean. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.mode(name='mode')` {#ConditionalDistribution.mode} + +Mode. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.name` {#ConditionalDistribution.name} + +Name prepended to all ops created by this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#ConditionalDistribution.param_shapes} + +Shapes of parameters given the desired shape of a call to `sample()`. + +This is a class method that describes what key/value arguments are required +to instantiate the given `Distribution` so that a particular shape is +returned for that instance's call to `sample()`. + +Subclasses should override class method `_param_shapes`. + +##### Args: + + +* <b>`sample_shape`</b>: `Tensor` or python list/tuple. Desired shape of a call to + `sample()`. +* <b>`name`</b>: name to prepend ops with. + +##### Returns: + + `dict` of parameter name to `Tensor` shapes. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.param_static_shapes(cls, sample_shape)` {#ConditionalDistribution.param_static_shapes} + +param_shapes with static (i.e. `TensorShape`) shapes. + +This is a class method that describes what key/value arguments are required +to instantiate the given `Distribution` so that a particular shape is +returned for that instance's call to `sample()`. Assumes that +the sample's shape is known statically. + +Subclasses should override class method `_param_shapes` to return +constant-valued tensors when constant values are fed. + +##### Args: + + +* <b>`sample_shape`</b>: `TensorShape` or python list/tuple. Desired shape of a call + to `sample()`. + +##### Returns: + + `dict` of parameter name to `TensorShape`. + +##### Raises: + + +* <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.parameters` {#ConditionalDistribution.parameters} + +Dictionary of parameters used to instantiate this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.pdf(value, name='pdf')` {#ConditionalDistribution.pdf} + +Probability density function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if not `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.pmf(value, name='pmf')` {#ConditionalDistribution.pmf} + +Probability mass function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.prob(*args, **kwargs)` {#ConditionalDistribution.prob} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.reparameterization_type` {#ConditionalDistribution.reparameterization_type} + +Describes how samples from the distribution are reparameterized. + +Currently this is one of the static instances +`distributions.FULLY_REPARAMETERIZED` +or `distributions.NOT_REPARAMETERIZED`. + +##### Returns: + + An instance of `ReparameterizationType`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.sample(*args, **kwargs)` {#ConditionalDistribution.sample} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.stddev(name='stddev')` {#ConditionalDistribution.stddev} + +Standard deviation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.survival_function(*args, **kwargs)` {#ConditionalDistribution.survival_function} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.validate_args` {#ConditionalDistribution.validate_args} + +Python boolean indicated possibly expensive checks are enabled. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.variance(name='variance')` {#ConditionalDistribution.variance} + +Variance. + + + +- - - + +### `class tf.contrib.distributions.ConditionalTransformedDistribution` {#ConditionalTransformedDistribution} + +A TransformedDistribution that allows intrinsic conditioning. +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.__init__(distribution, bijector=None, batch_shape=None, event_shape=None, validate_args=False, name=None)` {#ConditionalTransformedDistribution.__init__} + +Construct a Transformed Distribution. + +##### Args: + + +* <b>`distribution`</b>: The base distribution instance to transform. Typically an + instance of `Distribution`. +* <b>`bijector`</b>: The object responsible for calculating the transformation. + Typically an instance of `Bijector`. `None` means `Identity()`. +* <b>`batch_shape`</b>: `integer` vector `Tensor` which overrides `distribution` + `batch_shape`; valid only if `distribution.is_scalar_batch()`. +* <b>`event_shape`</b>: `integer` vector `Tensor` which overrides `distribution` + `event_shape`; valid only if `distribution.is_scalar_event()`. +* <b>`validate_args`</b>: Python Boolean. Whether to validate input with asserts. + If `validate_args` is `False`, and the inputs are invalid, + correct behavior is not guaranteed. +* <b>`name`</b>: The name for the distribution. Default: + `bijector.name + distribution.name`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.allow_nan_stats` {#ConditionalTransformedDistribution.allow_nan_stats} + +Python boolean describing behavior when a stat is undefined. + +Stats return +/- infinity when it makes sense. E.g., the variance +of a Cauchy distribution is infinity. However, sometimes the +statistic is undefined, e.g., if a distribution's pdf does not achieve a +maximum within the support of the distribution, the mode is undefined. +If the mean is undefined, then by definition the variance is undefined. +E.g. the mean for Student's T for df = 1 is undefined (no clear way to say +it is either + or - infinity), so the variance = E[(X - mean)^2] is also +undefined. + +##### Returns: + + +* <b>`allow_nan_stats`</b>: Python boolean. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.batch_shape(name='batch_shape')` {#ConditionalTransformedDistribution.batch_shape} + +Shape of a single sample from a single event index as a 1-D `Tensor`. + +The product of the dimensions of the `batch_shape` is the number of +independent distributions of this kind the instance represents. + +##### Args: + + +* <b>`name`</b>: name to give to the op + +##### Returns: + + +* <b>`batch_shape`</b>: `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.bijector` {#ConditionalTransformedDistribution.bijector} + +Function transforming x => y. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.cdf(*args, **kwargs)` {#ConditionalTransformedDistribution.cdf} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.copy(**override_parameters_kwargs)` {#ConditionalTransformedDistribution.copy} + +Creates a deep copy of the distribution. + +Note: the copy distribution may continue to depend on the original +intialization arguments. + +##### Args: + + +* <b>`**override_parameters_kwargs`</b>: String/value dictionary of initialization + arguments to override with new values. + +##### Returns: + + +* <b>`distribution`</b>: A new instance of `type(self)` intitialized from the union + of self.parameters and override_parameters_kwargs, i.e., + `dict(self.parameters, **override_parameters_kwargs)`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.distribution` {#ConditionalTransformedDistribution.distribution} + +Base distribution, p(x). + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.dtype` {#ConditionalTransformedDistribution.dtype} + +The `DType` of `Tensor`s handled by this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.entropy(name='entropy')` {#ConditionalTransformedDistribution.entropy} + +Shannon entropy in nats. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.event_shape(name='event_shape')` {#ConditionalTransformedDistribution.event_shape} + +Shape of a single sample from a single batch as a 1-D int32 `Tensor`. + +##### Args: + + +* <b>`name`</b>: name to give to the op + +##### Returns: + + +* <b>`event_shape`</b>: `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.get_batch_shape()` {#ConditionalTransformedDistribution.get_batch_shape} + +Shape of a single sample from a single event index as a `TensorShape`. + +Same meaning as `batch_shape`. May be only partially defined. + +##### Returns: + + +* <b>`batch_shape`</b>: `TensorShape`, possibly unknown. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.get_event_shape()` {#ConditionalTransformedDistribution.get_event_shape} + +Shape of a single sample from a single batch as a `TensorShape`. + +Same meaning as `event_shape`. May be only partially defined. + +##### Returns: + + +* <b>`event_shape`</b>: `TensorShape`, possibly unknown. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.is_continuous` {#ConditionalTransformedDistribution.is_continuous} + + + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.is_scalar_batch(name='is_scalar_batch')` {#ConditionalTransformedDistribution.is_scalar_batch} + +Indicates that `batch_shape == []`. + +##### Args: + + +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`is_scalar_batch`</b>: `Boolean` `scalar` `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.is_scalar_event(name='is_scalar_event')` {#ConditionalTransformedDistribution.is_scalar_event} + +Indicates that `event_shape == []`. + +##### Args: + + +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`is_scalar_event`</b>: `Boolean` `scalar` `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_cdf(*args, **kwargs)` {#ConditionalTransformedDistribution.log_cdf} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_pdf(value, name='log_pdf')` {#ConditionalTransformedDistribution.log_pdf} + +Log probability density function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if not `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_pmf(value, name='log_pmf')` {#ConditionalTransformedDistribution.log_pmf} + +Log probability mass function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_prob(*args, **kwargs)` {#ConditionalTransformedDistribution.log_prob} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_survival_function(*args, **kwargs)` {#ConditionalTransformedDistribution.log_survival_function} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.mean(name='mean')` {#ConditionalTransformedDistribution.mean} + +Mean. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.mode(name='mode')` {#ConditionalTransformedDistribution.mode} + +Mode. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.name` {#ConditionalTransformedDistribution.name} + +Name prepended to all ops created by this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#ConditionalTransformedDistribution.param_shapes} + +Shapes of parameters given the desired shape of a call to `sample()`. + +This is a class method that describes what key/value arguments are required +to instantiate the given `Distribution` so that a particular shape is +returned for that instance's call to `sample()`. + +Subclasses should override class method `_param_shapes`. + +##### Args: + + +* <b>`sample_shape`</b>: `Tensor` or python list/tuple. Desired shape of a call to + `sample()`. +* <b>`name`</b>: name to prepend ops with. + +##### Returns: + + `dict` of parameter name to `Tensor` shapes. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.param_static_shapes(cls, sample_shape)` {#ConditionalTransformedDistribution.param_static_shapes} + +param_shapes with static (i.e. `TensorShape`) shapes. + +This is a class method that describes what key/value arguments are required +to instantiate the given `Distribution` so that a particular shape is +returned for that instance's call to `sample()`. Assumes that +the sample's shape is known statically. + +Subclasses should override class method `_param_shapes` to return +constant-valued tensors when constant values are fed. + +##### Args: + + +* <b>`sample_shape`</b>: `TensorShape` or python list/tuple. Desired shape of a call + to `sample()`. + +##### Returns: + + `dict` of parameter name to `TensorShape`. + +##### Raises: + + +* <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.parameters` {#ConditionalTransformedDistribution.parameters} + +Dictionary of parameters used to instantiate this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.pdf(value, name='pdf')` {#ConditionalTransformedDistribution.pdf} + +Probability density function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if not `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.pmf(value, name='pmf')` {#ConditionalTransformedDistribution.pmf} + +Probability mass function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.prob(*args, **kwargs)` {#ConditionalTransformedDistribution.prob} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.reparameterization_type` {#ConditionalTransformedDistribution.reparameterization_type} + +Describes how samples from the distribution are reparameterized. + +Currently this is one of the static instances +`distributions.FULLY_REPARAMETERIZED` +or `distributions.NOT_REPARAMETERIZED`. + +##### Returns: + + An instance of `ReparameterizationType`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.sample(*args, **kwargs)` {#ConditionalTransformedDistribution.sample} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.stddev(name='stddev')` {#ConditionalTransformedDistribution.stddev} + +Standard deviation. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.survival_function(*args, **kwargs)` {#ConditionalTransformedDistribution.survival_function} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.validate_args` {#ConditionalTransformedDistribution.validate_args} + +Python boolean indicated possibly expensive checks are enabled. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.variance(name='variance')` {#ConditionalTransformedDistribution.variance} + +Variance. + + + diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md index 2287ff8bc3..932a61267d 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md @@ -78,7 +78,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Bernoulli.cdf(value, name='cdf', **condition_kwargs)` {#Bernoulli.cdf} +#### `tf.contrib.distributions.Bernoulli.cdf(value, name='cdf')` {#Bernoulli.cdf} Cumulative distribution function. @@ -93,7 +93,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -227,7 +226,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Bernoulli.log_cdf} +#### `tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf')` {#Bernoulli.log_cdf} Log cumulative distribution function. @@ -246,7 +245,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -257,7 +255,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Bernoulli.log_pdf} +#### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf')` {#Bernoulli.log_pdf} Log probability density function. @@ -266,7 +264,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -282,7 +279,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Bernoulli.log_pmf} +#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf')` {#Bernoulli.log_pmf} Log probability mass function. @@ -291,7 +288,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -307,7 +303,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob', **condition_kwargs)` {#Bernoulli.log_prob} +#### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob')` {#Bernoulli.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -316,7 +312,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -327,7 +322,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Bernoulli.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Bernoulli.log_survival_function} +#### `tf.contrib.distributions.Bernoulli.log_survival_function(value, name='log_survival_function')` {#Bernoulli.log_survival_function} Log survival function. @@ -347,7 +342,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -457,7 +451,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf', **condition_kwargs)` {#Bernoulli.pdf} +#### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf')` {#Bernoulli.pdf} Probability density function. @@ -466,7 +460,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -482,7 +475,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf', **condition_kwargs)` {#Bernoulli.pmf} +#### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf')` {#Bernoulli.pmf} Probability mass function. @@ -491,7 +484,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -507,7 +499,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Bernoulli.prob(value, name='prob', **condition_kwargs)` {#Bernoulli.prob} +#### `tf.contrib.distributions.Bernoulli.prob(value, name='prob')` {#Bernoulli.prob} Probability density/mass function (depending on `is_continuous`). @@ -516,7 +508,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -549,7 +540,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Bernoulli.sample} +#### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample')` {#Bernoulli.sample} Generate samples of the specified shape. @@ -562,7 +553,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -579,7 +569,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Bernoulli.survival_function(value, name='survival_function', **condition_kwargs)` {#Bernoulli.survival_function} +#### `tf.contrib.distributions.Bernoulli.survival_function(value, name='survival_function')` {#Bernoulli.survival_function} Survival function. @@ -596,7 +586,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Chi2WithAbsDf.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Chi2WithAbsDf.md index 6b2b1b92de..c5d0924a6e 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Chi2WithAbsDf.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Chi2WithAbsDf.md @@ -63,7 +63,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.cdf(value, name='cdf', **condition_kwargs)` {#Chi2WithAbsDf.cdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.cdf(value, name='cdf')` {#Chi2WithAbsDf.cdf} Cumulative distribution function. @@ -78,7 +78,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -230,7 +229,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Chi2WithAbsDf.log_cdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_cdf(value, name='log_cdf')` {#Chi2WithAbsDf.log_cdf} Log cumulative distribution function. @@ -249,7 +248,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -260,7 +258,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Chi2WithAbsDf.log_pdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf')` {#Chi2WithAbsDf.log_pdf} Log probability density function. @@ -269,7 +267,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -285,7 +282,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Chi2WithAbsDf.log_pmf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf')` {#Chi2WithAbsDf.log_pmf} Log probability mass function. @@ -294,7 +291,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -310,7 +306,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob', **condition_kwargs)` {#Chi2WithAbsDf.log_prob} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob')` {#Chi2WithAbsDf.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -319,7 +315,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -330,7 +325,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Chi2WithAbsDf.log_survival_function} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_survival_function(value, name='log_survival_function')` {#Chi2WithAbsDf.log_survival_function} Log survival function. @@ -350,7 +345,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -448,7 +442,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf', **condition_kwargs)` {#Chi2WithAbsDf.pdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf')` {#Chi2WithAbsDf.pdf} Probability density function. @@ -457,7 +451,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -473,7 +466,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf', **condition_kwargs)` {#Chi2WithAbsDf.pmf} +#### `tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf')` {#Chi2WithAbsDf.pmf} Probability mass function. @@ -482,7 +475,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -498,7 +490,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob', **condition_kwargs)` {#Chi2WithAbsDf.prob} +#### `tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob')` {#Chi2WithAbsDf.prob} Probability density/mass function (depending on `is_continuous`). @@ -507,7 +499,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -533,7 +524,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Chi2WithAbsDf.sample} +#### `tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample')` {#Chi2WithAbsDf.sample} Generate samples of the specified shape. @@ -546,7 +537,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -563,7 +553,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function', **condition_kwargs)` {#Chi2WithAbsDf.survival_function} +#### `tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function')` {#Chi2WithAbsDf.survival_function} Survival function. @@ -580,7 +570,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md index d2492ecc98..d8de378da7 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md @@ -150,7 +150,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Dirichlet.cdf(value, name='cdf', **condition_kwargs)` {#Dirichlet.cdf} +#### `tf.contrib.distributions.Dirichlet.cdf(value, name='cdf')` {#Dirichlet.cdf} Cumulative distribution function. @@ -165,7 +165,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -299,7 +298,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Dirichlet.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Dirichlet.log_cdf} +#### `tf.contrib.distributions.Dirichlet.log_cdf(value, name='log_cdf')` {#Dirichlet.log_cdf} Log cumulative distribution function. @@ -318,7 +317,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -329,7 +327,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Dirichlet.log_pdf} +#### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf')` {#Dirichlet.log_pdf} Log probability density function. @@ -338,7 +336,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -354,7 +351,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Dirichlet.log_pmf} +#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf')` {#Dirichlet.log_pmf} Log probability mass function. @@ -363,7 +360,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -379,7 +375,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob', **condition_kwargs)` {#Dirichlet.log_prob} +#### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob')` {#Dirichlet.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -396,7 +392,6 @@ in `self.alpha`. `x` is only legal if it sums up to one. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -407,7 +402,7 @@ in `self.alpha`. `x` is only legal if it sums up to one. - - - -#### `tf.contrib.distributions.Dirichlet.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Dirichlet.log_survival_function} +#### `tf.contrib.distributions.Dirichlet.log_survival_function(value, name='log_survival_function')` {#Dirichlet.log_survival_function} Log survival function. @@ -427,7 +422,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -526,7 +520,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf', **condition_kwargs)` {#Dirichlet.pdf} +#### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf')` {#Dirichlet.pdf} Probability density function. @@ -535,7 +529,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -551,7 +544,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf', **condition_kwargs)` {#Dirichlet.pmf} +#### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf')` {#Dirichlet.pmf} Probability mass function. @@ -560,7 +553,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -576,7 +568,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Dirichlet.prob(value, name='prob', **condition_kwargs)` {#Dirichlet.prob} +#### `tf.contrib.distributions.Dirichlet.prob(value, name='prob')` {#Dirichlet.prob} Probability density/mass function (depending on `is_continuous`). @@ -593,7 +585,6 @@ in `self.alpha`. `x` is only legal if it sums up to one. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -619,7 +610,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Dirichlet.sample} +#### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample')` {#Dirichlet.sample} Generate samples of the specified shape. @@ -632,7 +623,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -649,7 +639,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Dirichlet.survival_function(value, name='survival_function', **condition_kwargs)` {#Dirichlet.survival_function} +#### `tf.contrib.distributions.Dirichlet.survival_function(value, name='survival_function')` {#Dirichlet.survival_function} Survival function. @@ -666,7 +656,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md index 382d767f0c..143a3a31c0 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md @@ -191,7 +191,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Distribution.cdf(value, name='cdf', **condition_kwargs)` {#Distribution.cdf} +#### `tf.contrib.distributions.Distribution.cdf(value, name='cdf')` {#Distribution.cdf} Cumulative distribution function. @@ -206,7 +206,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -340,7 +339,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Distribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Distribution.log_cdf} +#### `tf.contrib.distributions.Distribution.log_cdf(value, name='log_cdf')` {#Distribution.log_cdf} Log cumulative distribution function. @@ -359,7 +358,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -370,7 +368,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Distribution.log_pdf} +#### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf')` {#Distribution.log_pdf} Log probability density function. @@ -379,7 +377,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -395,7 +392,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Distribution.log_pmf} +#### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf')` {#Distribution.log_pmf} Log probability mass function. @@ -404,7 +401,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -420,7 +416,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob', **condition_kwargs)` {#Distribution.log_prob} +#### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob')` {#Distribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -429,7 +425,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -440,7 +435,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Distribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Distribution.log_survival_function} +#### `tf.contrib.distributions.Distribution.log_survival_function(value, name='log_survival_function')` {#Distribution.log_survival_function} Log survival function. @@ -460,7 +455,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -552,7 +546,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Distribution.pdf(value, name='pdf', **condition_kwargs)` {#Distribution.pdf} +#### `tf.contrib.distributions.Distribution.pdf(value, name='pdf')` {#Distribution.pdf} Probability density function. @@ -561,7 +555,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -577,7 +570,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Distribution.pmf(value, name='pmf', **condition_kwargs)` {#Distribution.pmf} +#### `tf.contrib.distributions.Distribution.pmf(value, name='pmf')` {#Distribution.pmf} Probability mass function. @@ -586,7 +579,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -602,7 +594,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Distribution.prob(value, name='prob', **condition_kwargs)` {#Distribution.prob} +#### `tf.contrib.distributions.Distribution.prob(value, name='prob')` {#Distribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -611,7 +603,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -637,7 +628,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Distribution.sample} +#### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample')` {#Distribution.sample} Generate samples of the specified shape. @@ -650,7 +641,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -667,7 +657,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Distribution.survival_function(value, name='survival_function', **condition_kwargs)` {#Distribution.survival_function} +#### `tf.contrib.distributions.Distribution.survival_function(value, name='survival_function')` {#Distribution.survival_function} Survival function. @@ -684,7 +674,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md index d794948d51..bd2d22d0a3 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md @@ -119,7 +119,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalCholesky.cdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.cdf(value, name='cdf')` {#MultivariateNormalCholesky.cdf} Cumulative distribution function. @@ -134,7 +134,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -268,7 +267,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalCholesky.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf')` {#MultivariateNormalCholesky.log_cdf} Log cumulative distribution function. @@ -287,7 +286,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -298,7 +296,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalCholesky.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf')` {#MultivariateNormalCholesky.log_pdf} Log probability density function. @@ -307,7 +305,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -323,7 +320,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalCholesky.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf')` {#MultivariateNormalCholesky.log_pmf} Log probability mass function. @@ -332,7 +329,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -348,7 +344,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalCholesky.log_prob} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob')` {#MultivariateNormalCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -373,7 +369,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -391,7 +386,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalCholesky.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalCholesky.log_survival_function} Log survival function. @@ -411,7 +406,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -510,7 +504,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalCholesky.pdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf')` {#MultivariateNormalCholesky.pdf} Probability density function. @@ -519,7 +513,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -535,7 +528,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalCholesky.pmf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf')` {#MultivariateNormalCholesky.pmf} Probability mass function. @@ -544,7 +537,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -560,7 +552,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalCholesky.prob} +#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob')` {#MultivariateNormalCholesky.prob} Probability density/mass function (depending on `is_continuous`). @@ -585,7 +577,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -611,7 +602,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalCholesky.sample} +#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalCholesky.sample} Generate samples of the specified shape. @@ -624,7 +615,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -655,7 +645,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalCholesky.survival_function} +#### `tf.contrib.distributions.MultivariateNormalCholesky.survival_function(value, name='survival_function')` {#MultivariateNormalCholesky.survival_function} Survival function. @@ -672,7 +662,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.bijector.CholeskyOuterProduct.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.bijector.CholeskyOuterProduct.md index d2c24f9c10..3db85660b2 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.bijector.CholeskyOuterProduct.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.bijector.CholeskyOuterProduct.md @@ -44,7 +44,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.forward(x, name='forward', **condition_kwargs)` {#CholeskyOuterProduct.forward} +#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.forward(x, name='forward')` {#CholeskyOuterProduct.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -53,7 +53,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -89,7 +88,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#CholeskyOuterProduct.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#CholeskyOuterProduct.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -98,7 +97,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -164,7 +162,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse(y, name='inverse', **condition_kwargs)` {#CholeskyOuterProduct.inverse} +#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse(y, name='inverse')` {#CholeskyOuterProduct.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -173,7 +171,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -190,7 +187,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#CholeskyOuterProduct.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#CholeskyOuterProduct.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -204,7 +201,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -241,7 +237,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#CholeskyOuterProduct.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.CholeskyOuterProduct.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#CholeskyOuterProduct.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -254,7 +250,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.bijector.SigmoidCentered.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.bijector.SigmoidCentered.md index 15b3e391d1..60f1a6d6d6 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.bijector.SigmoidCentered.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.bijector.SigmoidCentered.md @@ -19,7 +19,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.SigmoidCentered.forward(x, name='forward', **condition_kwargs)` {#SigmoidCentered.forward} +#### `tf.contrib.distributions.bijector.SigmoidCentered.forward(x, name='forward')` {#SigmoidCentered.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -28,7 +28,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -64,7 +63,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.SigmoidCentered.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#SigmoidCentered.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SigmoidCentered.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#SigmoidCentered.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -73,7 +72,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -139,7 +137,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse(y, name='inverse', **condition_kwargs)` {#SigmoidCentered.inverse} +#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse(y, name='inverse')` {#SigmoidCentered.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -148,7 +146,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -165,7 +162,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#SigmoidCentered.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#SigmoidCentered.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -179,7 +176,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -216,7 +212,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#SigmoidCentered.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SigmoidCentered.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#SigmoidCentered.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -229,7 +225,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md index 884d039762..8864fbb22a 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md @@ -119,7 +119,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiag.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.cdf(value, name='cdf')` {#MultivariateNormalDiag.cdf} Cumulative distribution function. @@ -134,7 +134,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -268,7 +267,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiag.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiag.log_cdf} Log cumulative distribution function. @@ -287,7 +286,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -298,7 +296,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiag.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiag.log_pdf} Log probability density function. @@ -307,7 +305,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -323,7 +320,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiag.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiag.log_pmf} Log probability mass function. @@ -332,7 +329,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -348,7 +344,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiag.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob')` {#MultivariateNormalDiag.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -373,7 +369,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -391,7 +386,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiag.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiag.log_survival_function} Log survival function. @@ -411,7 +406,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -510,7 +504,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiag.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf')` {#MultivariateNormalDiag.pdf} Probability density function. @@ -519,7 +513,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -535,7 +528,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiag.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf')` {#MultivariateNormalDiag.pmf} Probability mass function. @@ -544,7 +537,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -560,7 +552,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiag.prob} +#### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob')` {#MultivariateNormalDiag.prob} Probability density/mass function (depending on `is_continuous`). @@ -585,7 +577,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -611,7 +602,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiag.sample} +#### `tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiag.sample} Generate samples of the specified shape. @@ -624,7 +615,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -655,7 +645,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiag.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiag.survival_function(value, name='survival_function')` {#MultivariateNormalDiag.survival_function} Survival function. @@ -672,7 +662,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.QuantizedDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.QuantizedDistribution.md index ef22371fe8..b6d70fd39b 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.QuantizedDistribution.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.QuantizedDistribution.md @@ -128,7 +128,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.QuantizedDistribution.cdf(value, name='cdf', **condition_kwargs)` {#QuantizedDistribution.cdf} +#### `tf.contrib.distributions.QuantizedDistribution.cdf(value, name='cdf')` {#QuantizedDistribution.cdf} Cumulative distribution function. @@ -161,7 +161,6 @@ The base distribution's `cdf` method must be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -302,7 +301,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#QuantizedDistribution.log_cdf} +#### `tf.contrib.distributions.QuantizedDistribution.log_cdf(value, name='log_cdf')` {#QuantizedDistribution.log_cdf} Log cumulative distribution function. @@ -339,7 +338,6 @@ The base distribution's `log_cdf` method must be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -350,7 +348,7 @@ The base distribution's `log_cdf` method must be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#QuantizedDistribution.log_pdf} +#### `tf.contrib.distributions.QuantizedDistribution.log_pdf(value, name='log_pdf')` {#QuantizedDistribution.log_pdf} Log probability density function. @@ -359,7 +357,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -375,7 +372,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#QuantizedDistribution.log_pmf} +#### `tf.contrib.distributions.QuantizedDistribution.log_pmf(value, name='log_pmf')` {#QuantizedDistribution.log_pmf} Log probability mass function. @@ -384,7 +381,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -400,7 +396,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob', **condition_kwargs)` {#QuantizedDistribution.log_prob} +#### `tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob')` {#QuantizedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -427,7 +423,6 @@ must also be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -438,7 +433,7 @@ must also be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#QuantizedDistribution.log_survival_function} +#### `tf.contrib.distributions.QuantizedDistribution.log_survival_function(value, name='log_survival_function')` {#QuantizedDistribution.log_survival_function} Log survival function. @@ -476,7 +471,6 @@ The base distribution's `log_cdf` method must be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -568,7 +562,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.pdf(value, name='pdf', **condition_kwargs)` {#QuantizedDistribution.pdf} +#### `tf.contrib.distributions.QuantizedDistribution.pdf(value, name='pdf')` {#QuantizedDistribution.pdf} Probability density function. @@ -577,7 +571,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -593,7 +586,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.pmf(value, name='pmf', **condition_kwargs)` {#QuantizedDistribution.pmf} +#### `tf.contrib.distributions.QuantizedDistribution.pmf(value, name='pmf')` {#QuantizedDistribution.pmf} Probability mass function. @@ -602,7 +595,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -618,7 +610,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob', **condition_kwargs)` {#QuantizedDistribution.prob} +#### `tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob')` {#QuantizedDistribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -645,7 +637,6 @@ also be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -671,7 +662,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#QuantizedDistribution.sample} +#### `tf.contrib.distributions.QuantizedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#QuantizedDistribution.sample} Generate samples of the specified shape. @@ -684,7 +675,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -701,7 +691,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.QuantizedDistribution.survival_function(value, name='survival_function', **condition_kwargs)` {#QuantizedDistribution.survival_function} +#### `tf.contrib.distributions.QuantizedDistribution.survival_function(value, name='survival_function')` {#QuantizedDistribution.survival_function} Survival function. @@ -736,7 +726,6 @@ The base distribution's `cdf` method must be defined on `y - 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md index 76b3dba597..3a625fddf4 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md @@ -132,7 +132,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.StudentT.cdf(value, name='cdf', **condition_kwargs)` {#StudentT.cdf} +#### `tf.contrib.distributions.StudentT.cdf(value, name='cdf')` {#StudentT.cdf} Cumulative distribution function. @@ -147,7 +147,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -288,7 +287,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf', **condition_kwargs)` {#StudentT.log_cdf} +#### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf')` {#StudentT.log_cdf} Log cumulative distribution function. @@ -307,7 +306,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -318,7 +316,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf', **condition_kwargs)` {#StudentT.log_pdf} +#### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf')` {#StudentT.log_pdf} Log probability density function. @@ -327,7 +325,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -343,7 +340,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf', **condition_kwargs)` {#StudentT.log_pmf} +#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf')` {#StudentT.log_pmf} Log probability mass function. @@ -352,7 +349,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -368,7 +364,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob', **condition_kwargs)` {#StudentT.log_prob} +#### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob')` {#StudentT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -377,7 +373,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -388,7 +383,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentT.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#StudentT.log_survival_function} +#### `tf.contrib.distributions.StudentT.log_survival_function(value, name='log_survival_function')` {#StudentT.log_survival_function} Log survival function. @@ -408,7 +403,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -513,7 +507,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.StudentT.pdf(value, name='pdf', **condition_kwargs)` {#StudentT.pdf} +#### `tf.contrib.distributions.StudentT.pdf(value, name='pdf')` {#StudentT.pdf} Probability density function. @@ -522,7 +516,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -538,7 +531,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.StudentT.pmf(value, name='pmf', **condition_kwargs)` {#StudentT.pmf} +#### `tf.contrib.distributions.StudentT.pmf(value, name='pmf')` {#StudentT.pmf} Probability mass function. @@ -547,7 +540,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -563,7 +555,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.StudentT.prob(value, name='prob', **condition_kwargs)` {#StudentT.prob} +#### `tf.contrib.distributions.StudentT.prob(value, name='prob')` {#StudentT.prob} Probability density/mass function (depending on `is_continuous`). @@ -572,7 +564,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -598,7 +589,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#StudentT.sample} +#### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample')` {#StudentT.sample} Generate samples of the specified shape. @@ -611,7 +602,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -635,7 +625,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.StudentT.survival_function(value, name='survival_function', **condition_kwargs)` {#StudentT.survival_function} +#### `tf.contrib.distributions.StudentT.survival_function(value, name='survival_function')` {#StudentT.survival_function} Survival function. @@ -652,7 +642,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md index 7ed654e32d..ed7cffbae3 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md @@ -206,7 +206,7 @@ Function transforming x => y. - - - -#### `tf.contrib.distributions.TransformedDistribution.cdf(value, name='cdf', **condition_kwargs)` {#TransformedDistribution.cdf} +#### `tf.contrib.distributions.TransformedDistribution.cdf(value, name='cdf')` {#TransformedDistribution.cdf} Cumulative distribution function. @@ -216,20 +216,11 @@ Given random variable `X`, the cumulative distribution function `cdf` is: cdf(x) := P[X <= x] ``` - -Additional documentation from `TransformedDistribution`: - -##### `condition_kwargs`: - -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. - ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -370,7 +361,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#TransformedDistribution.log_cdf} +#### `tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf')` {#TransformedDistribution.log_cdf} Log cumulative distribution function. @@ -384,20 +375,11 @@ Often, a numerical approximation can be used for `log_cdf(x)` that yields a more accurate answer than simply taking the logarithm of the `cdf` when `x << -1`. - -Additional documentation from `TransformedDistribution`: - -##### `condition_kwargs`: - -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. - ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -408,7 +390,7 @@ Additional documentation from `TransformedDistribution`: - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#TransformedDistribution.log_pdf} +#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf')` {#TransformedDistribution.log_pdf} Log probability density function. @@ -417,7 +399,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -433,7 +414,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#TransformedDistribution.log_pmf} +#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf')` {#TransformedDistribution.log_pmf} Log probability mass function. @@ -442,7 +423,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -458,7 +438,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob', **condition_kwargs)` {#TransformedDistribution.log_prob} +#### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob')` {#TransformedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -466,22 +446,16 @@ Log probability density/mass function (depending on `is_continuous`). Additional documentation from `TransformedDistribution`: Implements `(log o p o g^{-1})(y) + (log o abs o det o J o g^{-1})(y)`, - where `g^{-1}` is the inverse of `transform`. - - Also raises a `ValueError` if `inverse` was not provided to the - distribution and `y` was not returned from `sample`. - -##### `condition_kwargs`: +where `g^{-1}` is the inverse of `transform`. -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. +Also raises a `ValueError` if `inverse` was not provided to the +distribution and `y` was not returned from `sample`. ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -492,7 +466,7 @@ Implements `(log o p o g^{-1})(y) + (log o abs o det o J o g^{-1})(y)`, - - - -#### `tf.contrib.distributions.TransformedDistribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#TransformedDistribution.log_survival_function} +#### `tf.contrib.distributions.TransformedDistribution.log_survival_function(value, name='log_survival_function')` {#TransformedDistribution.log_survival_function} Log survival function. @@ -507,20 +481,11 @@ log_survival_function(x) = Log[ P[X > x] ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. - -Additional documentation from `TransformedDistribution`: - -##### `condition_kwargs`: - -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. - ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -612,7 +577,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf', **condition_kwargs)` {#TransformedDistribution.pdf} +#### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf')` {#TransformedDistribution.pdf} Probability density function. @@ -621,7 +586,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -637,7 +601,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf', **condition_kwargs)` {#TransformedDistribution.pmf} +#### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf')` {#TransformedDistribution.pmf} Probability mass function. @@ -646,7 +610,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -662,7 +625,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob', **condition_kwargs)` {#TransformedDistribution.prob} +#### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob')` {#TransformedDistribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -670,22 +633,16 @@ Probability density/mass function (depending on `is_continuous`). Additional documentation from `TransformedDistribution`: Implements `p(g^{-1}(y)) det|J(g^{-1}(y))|`, where `g^{-1}` is the - inverse of `transform`. - - Also raises a `ValueError` if `inverse` was not provided to the - distribution and `y` was not returned from `sample`. +inverse of `transform`. -##### `condition_kwargs`: - -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. +Also raises a `ValueError` if `inverse` was not provided to the +distribution and `y` was not returned from `sample`. ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -711,7 +668,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#TransformedDistribution.sample} +#### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#TransformedDistribution.sample} Generate samples of the specified shape. @@ -724,7 +681,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -741,7 +697,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.TransformedDistribution.survival_function(value, name='survival_function', **condition_kwargs)` {#TransformedDistribution.survival_function} +#### `tf.contrib.distributions.TransformedDistribution.survival_function(value, name='survival_function')` {#TransformedDistribution.survival_function} Survival function. @@ -753,20 +709,11 @@ survival_function(x) = P[X > x] = 1 - cdf(x). ``` - -Additional documentation from `TransformedDistribution`: - -##### `condition_kwargs`: - -* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. - ##### Args: * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md index d10e584f8c..ac90f6b7cd 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md @@ -109,7 +109,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Categorical.cdf(value, name='cdf', **condition_kwargs)` {#Categorical.cdf} +#### `tf.contrib.distributions.Categorical.cdf(value, name='cdf')` {#Categorical.cdf} Cumulative distribution function. @@ -124,7 +124,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -258,7 +257,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Categorical.log_cdf} +#### `tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf')` {#Categorical.log_cdf} Log cumulative distribution function. @@ -277,7 +276,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -288,7 +286,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Categorical.log_pdf} +#### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf')` {#Categorical.log_pdf} Log probability density function. @@ -297,7 +295,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -313,7 +310,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Categorical.log_pmf} +#### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf')` {#Categorical.log_pmf} Log probability mass function. @@ -322,7 +319,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -338,7 +334,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob', **condition_kwargs)` {#Categorical.log_prob} +#### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob')` {#Categorical.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -347,7 +343,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -358,7 +353,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Categorical.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Categorical.log_survival_function} +#### `tf.contrib.distributions.Categorical.log_survival_function(value, name='log_survival_function')` {#Categorical.log_survival_function} Log survival function. @@ -378,7 +373,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -493,7 +487,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Categorical.pdf(value, name='pdf', **condition_kwargs)` {#Categorical.pdf} +#### `tf.contrib.distributions.Categorical.pdf(value, name='pdf')` {#Categorical.pdf} Probability density function. @@ -502,7 +496,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -518,7 +511,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Categorical.pmf(value, name='pmf', **condition_kwargs)` {#Categorical.pmf} +#### `tf.contrib.distributions.Categorical.pmf(value, name='pmf')` {#Categorical.pmf} Probability mass function. @@ -527,7 +520,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -543,7 +535,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Categorical.prob(value, name='prob', **condition_kwargs)` {#Categorical.prob} +#### `tf.contrib.distributions.Categorical.prob(value, name='prob')` {#Categorical.prob} Probability density/mass function (depending on `is_continuous`). @@ -552,7 +544,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -578,7 +569,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Categorical.sample} +#### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample')` {#Categorical.sample} Generate samples of the specified shape. @@ -591,7 +582,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -608,7 +598,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Categorical.survival_function(value, name='survival_function', **condition_kwargs)` {#Categorical.survival_function} +#### `tf.contrib.distributions.Categorical.survival_function(value, name='survival_function')` {#Categorical.survival_function} Survival function. @@ -625,7 +615,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md index 2d55d1ca48..da34aee0f5 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md @@ -85,7 +85,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Chi2.cdf(value, name='cdf', **condition_kwargs)` {#Chi2.cdf} +#### `tf.contrib.distributions.Chi2.cdf(value, name='cdf')` {#Chi2.cdf} Cumulative distribution function. @@ -100,7 +100,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -252,7 +251,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Chi2.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Chi2.log_cdf} +#### `tf.contrib.distributions.Chi2.log_cdf(value, name='log_cdf')` {#Chi2.log_cdf} Log cumulative distribution function. @@ -271,7 +270,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -282,7 +280,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Chi2.log_pdf} +#### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf')` {#Chi2.log_pdf} Log probability density function. @@ -291,7 +289,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -307,7 +304,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Chi2.log_pmf} +#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf')` {#Chi2.log_pmf} Log probability mass function. @@ -316,7 +313,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -332,7 +328,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob', **condition_kwargs)` {#Chi2.log_prob} +#### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob')` {#Chi2.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -341,7 +337,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -352,7 +347,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Chi2.log_survival_function} +#### `tf.contrib.distributions.Chi2.log_survival_function(value, name='log_survival_function')` {#Chi2.log_survival_function} Log survival function. @@ -372,7 +367,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -470,7 +464,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf', **condition_kwargs)` {#Chi2.pdf} +#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf')` {#Chi2.pdf} Probability density function. @@ -479,7 +473,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -495,7 +488,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf', **condition_kwargs)` {#Chi2.pmf} +#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf')` {#Chi2.pmf} Probability mass function. @@ -504,7 +497,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -520,7 +512,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Chi2.prob(value, name='prob', **condition_kwargs)` {#Chi2.prob} +#### `tf.contrib.distributions.Chi2.prob(value, name='prob')` {#Chi2.prob} Probability density/mass function (depending on `is_continuous`). @@ -529,7 +521,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -555,7 +546,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Chi2.sample} +#### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample')` {#Chi2.sample} Generate samples of the specified shape. @@ -568,7 +559,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -585,7 +575,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Chi2.survival_function(value, name='survival_function', **condition_kwargs)` {#Chi2.survival_function} +#### `tf.contrib.distributions.Chi2.survival_function(value, name='survival_function')` {#Chi2.survival_function} Survival function. @@ -602,7 +592,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.ConditionalDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.ConditionalDistribution.md new file mode 100644 index 0000000000..50bf702980 --- /dev/null +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.ConditionalDistribution.md @@ -0,0 +1,483 @@ +Distribution that supports intrinsic parameters (local latents). + +Subclasses of this distribution may have additional keyword arguments passed +to their sample-based methods (i.e. `sample`, `log_prob`, etc.). +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.__init__(dtype, is_continuous, reparameterization_type, validate_args, allow_nan_stats, parameters=None, graph_parents=None, name=None)` {#ConditionalDistribution.__init__} + +Constructs the `Distribution`. + +**This is a private method for subclass use.** + +##### Args: + + +* <b>`dtype`</b>: The type of the event samples. `None` implies no type-enforcement. +* <b>`is_continuous`</b>: Python boolean. If `True` this + `Distribution` is continuous over its supported domain. +* <b>`reparameterization_type`</b>: Instance of `ReparameterizationType`. + If `distributions.FULLY_REPARAMETERIZED`, this + `Distribution` can be reparameterized in terms of some standard + distribution with a function whose Jacobian is constant for the support + of the standard distribution. If `distributions.NOT_REPARAMETERIZED`, + then no such reparameterization is available. +* <b>`validate_args`</b>: Python boolean. Whether to validate input with asserts. + If `validate_args` is `False`, and the inputs are invalid, + correct behavior is not guaranteed. +* <b>`allow_nan_stats`</b>: Python boolean. If `False`, raise an + exception if a statistic (e.g., mean, mode) is undefined for any batch + member. If True, batch members with valid parameters leading to + undefined statistics will return `NaN` for this statistic. +* <b>`parameters`</b>: Python dictionary of parameters used to instantiate this + `Distribution`. +* <b>`graph_parents`</b>: Python list of graph prerequisites of this `Distribution`. +* <b>`name`</b>: A name for this distribution. Default: subclass name. + +##### Raises: + + +* <b>`ValueError`</b>: if any member of graph_parents is `None` or not a `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.allow_nan_stats` {#ConditionalDistribution.allow_nan_stats} + +Python boolean describing behavior when a stat is undefined. + +Stats return +/- infinity when it makes sense. E.g., the variance +of a Cauchy distribution is infinity. However, sometimes the +statistic is undefined, e.g., if a distribution's pdf does not achieve a +maximum within the support of the distribution, the mode is undefined. +If the mean is undefined, then by definition the variance is undefined. +E.g. the mean for Student's T for df = 1 is undefined (no clear way to say +it is either + or - infinity), so the variance = E[(X - mean)^2] is also +undefined. + +##### Returns: + + +* <b>`allow_nan_stats`</b>: Python boolean. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.batch_shape(name='batch_shape')` {#ConditionalDistribution.batch_shape} + +Shape of a single sample from a single event index as a 1-D `Tensor`. + +The product of the dimensions of the `batch_shape` is the number of +independent distributions of this kind the instance represents. + +##### Args: + + +* <b>`name`</b>: name to give to the op + +##### Returns: + + +* <b>`batch_shape`</b>: `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.cdf(*args, **kwargs)` {#ConditionalDistribution.cdf} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.copy(**override_parameters_kwargs)` {#ConditionalDistribution.copy} + +Creates a deep copy of the distribution. + +Note: the copy distribution may continue to depend on the original +intialization arguments. + +##### Args: + + +* <b>`**override_parameters_kwargs`</b>: String/value dictionary of initialization + arguments to override with new values. + +##### Returns: + + +* <b>`distribution`</b>: A new instance of `type(self)` intitialized from the union + of self.parameters and override_parameters_kwargs, i.e., + `dict(self.parameters, **override_parameters_kwargs)`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.dtype` {#ConditionalDistribution.dtype} + +The `DType` of `Tensor`s handled by this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.entropy(name='entropy')` {#ConditionalDistribution.entropy} + +Shannon entropy in nats. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.event_shape(name='event_shape')` {#ConditionalDistribution.event_shape} + +Shape of a single sample from a single batch as a 1-D int32 `Tensor`. + +##### Args: + + +* <b>`name`</b>: name to give to the op + +##### Returns: + + +* <b>`event_shape`</b>: `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.get_batch_shape()` {#ConditionalDistribution.get_batch_shape} + +Shape of a single sample from a single event index as a `TensorShape`. + +Same meaning as `batch_shape`. May be only partially defined. + +##### Returns: + + +* <b>`batch_shape`</b>: `TensorShape`, possibly unknown. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.get_event_shape()` {#ConditionalDistribution.get_event_shape} + +Shape of a single sample from a single batch as a `TensorShape`. + +Same meaning as `event_shape`. May be only partially defined. + +##### Returns: + + +* <b>`event_shape`</b>: `TensorShape`, possibly unknown. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.is_continuous` {#ConditionalDistribution.is_continuous} + + + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.is_scalar_batch(name='is_scalar_batch')` {#ConditionalDistribution.is_scalar_batch} + +Indicates that `batch_shape == []`. + +##### Args: + + +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`is_scalar_batch`</b>: `Boolean` `scalar` `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.is_scalar_event(name='is_scalar_event')` {#ConditionalDistribution.is_scalar_event} + +Indicates that `event_shape == []`. + +##### Args: + + +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`is_scalar_event`</b>: `Boolean` `scalar` `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.log_cdf(*args, **kwargs)` {#ConditionalDistribution.log_cdf} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.log_pdf(value, name='log_pdf')` {#ConditionalDistribution.log_pdf} + +Log probability density function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if not `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.log_pmf(value, name='log_pmf')` {#ConditionalDistribution.log_pmf} + +Log probability mass function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.log_prob(*args, **kwargs)` {#ConditionalDistribution.log_prob} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.log_survival_function(*args, **kwargs)` {#ConditionalDistribution.log_survival_function} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.mean(name='mean')` {#ConditionalDistribution.mean} + +Mean. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.mode(name='mode')` {#ConditionalDistribution.mode} + +Mode. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.name` {#ConditionalDistribution.name} + +Name prepended to all ops created by this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#ConditionalDistribution.param_shapes} + +Shapes of parameters given the desired shape of a call to `sample()`. + +This is a class method that describes what key/value arguments are required +to instantiate the given `Distribution` so that a particular shape is +returned for that instance's call to `sample()`. + +Subclasses should override class method `_param_shapes`. + +##### Args: + + +* <b>`sample_shape`</b>: `Tensor` or python list/tuple. Desired shape of a call to + `sample()`. +* <b>`name`</b>: name to prepend ops with. + +##### Returns: + + `dict` of parameter name to `Tensor` shapes. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.param_static_shapes(cls, sample_shape)` {#ConditionalDistribution.param_static_shapes} + +param_shapes with static (i.e. `TensorShape`) shapes. + +This is a class method that describes what key/value arguments are required +to instantiate the given `Distribution` so that a particular shape is +returned for that instance's call to `sample()`. Assumes that +the sample's shape is known statically. + +Subclasses should override class method `_param_shapes` to return +constant-valued tensors when constant values are fed. + +##### Args: + + +* <b>`sample_shape`</b>: `TensorShape` or python list/tuple. Desired shape of a call + to `sample()`. + +##### Returns: + + `dict` of parameter name to `TensorShape`. + +##### Raises: + + +* <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.parameters` {#ConditionalDistribution.parameters} + +Dictionary of parameters used to instantiate this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.pdf(value, name='pdf')` {#ConditionalDistribution.pdf} + +Probability density function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if not `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.pmf(value, name='pmf')` {#ConditionalDistribution.pmf} + +Probability mass function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.prob(*args, **kwargs)` {#ConditionalDistribution.prob} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.reparameterization_type` {#ConditionalDistribution.reparameterization_type} + +Describes how samples from the distribution are reparameterized. + +Currently this is one of the static instances +`distributions.FULLY_REPARAMETERIZED` +or `distributions.NOT_REPARAMETERIZED`. + +##### Returns: + + An instance of `ReparameterizationType`. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.sample(*args, **kwargs)` {#ConditionalDistribution.sample} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.stddev(name='stddev')` {#ConditionalDistribution.stddev} + +Standard deviation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.survival_function(*args, **kwargs)` {#ConditionalDistribution.survival_function} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.validate_args` {#ConditionalDistribution.validate_args} + +Python boolean indicated possibly expensive checks are enabled. + + +- - - + +#### `tf.contrib.distributions.ConditionalDistribution.variance(name='variance')` {#ConditionalDistribution.variance} + +Variance. + + diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md index d64280270c..bdb5f9f7be 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md @@ -105,7 +105,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Uniform.cdf(value, name='cdf', **condition_kwargs)` {#Uniform.cdf} +#### `tf.contrib.distributions.Uniform.cdf(value, name='cdf')` {#Uniform.cdf} Cumulative distribution function. @@ -120,7 +120,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -254,7 +253,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Uniform.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Uniform.log_cdf} +#### `tf.contrib.distributions.Uniform.log_cdf(value, name='log_cdf')` {#Uniform.log_cdf} Log cumulative distribution function. @@ -273,7 +272,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -284,7 +282,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Uniform.log_pdf} +#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf')` {#Uniform.log_pdf} Log probability density function. @@ -293,7 +291,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -309,7 +306,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Uniform.log_pmf} +#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf')` {#Uniform.log_pmf} Log probability mass function. @@ -318,7 +315,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -334,7 +330,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob', **condition_kwargs)` {#Uniform.log_prob} +#### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob')` {#Uniform.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -343,7 +339,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -354,7 +349,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Uniform.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Uniform.log_survival_function} +#### `tf.contrib.distributions.Uniform.log_survival_function(value, name='log_survival_function')` {#Uniform.log_survival_function} Log survival function. @@ -374,7 +369,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -466,7 +460,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Uniform.pdf(value, name='pdf', **condition_kwargs)` {#Uniform.pdf} +#### `tf.contrib.distributions.Uniform.pdf(value, name='pdf')` {#Uniform.pdf} Probability density function. @@ -475,7 +469,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -491,7 +484,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Uniform.pmf(value, name='pmf', **condition_kwargs)` {#Uniform.pmf} +#### `tf.contrib.distributions.Uniform.pmf(value, name='pmf')` {#Uniform.pmf} Probability mass function. @@ -500,7 +493,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -516,7 +508,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Uniform.prob(value, name='prob', **condition_kwargs)` {#Uniform.prob} +#### `tf.contrib.distributions.Uniform.prob(value, name='prob')` {#Uniform.prob} Probability density/mass function (depending on `is_continuous`). @@ -525,7 +517,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -558,7 +549,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Uniform.sample} +#### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample')` {#Uniform.sample} Generate samples of the specified shape. @@ -571,7 +562,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -588,7 +578,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Uniform.survival_function(value, name='survival_function', **condition_kwargs)` {#Uniform.survival_function} +#### `tf.contrib.distributions.Uniform.survival_function(value, name='survival_function')` {#Uniform.survival_function} Survival function. @@ -605,7 +595,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md index fd21cbb869..751b39da28 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md @@ -128,7 +128,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.WishartCholesky.cdf(value, name='cdf', **condition_kwargs)` {#WishartCholesky.cdf} +#### `tf.contrib.distributions.WishartCholesky.cdf(value, name='cdf')` {#WishartCholesky.cdf} Cumulative distribution function. @@ -143,7 +143,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -298,7 +297,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf', **condition_kwargs)` {#WishartCholesky.log_cdf} +#### `tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf')` {#WishartCholesky.log_cdf} Log cumulative distribution function. @@ -317,7 +316,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -335,7 +333,7 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf', **condition_kwargs)` {#WishartCholesky.log_pdf} +#### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf')` {#WishartCholesky.log_pdf} Log probability density function. @@ -344,7 +342,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -360,7 +357,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf', **condition_kwargs)` {#WishartCholesky.log_pmf} +#### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf')` {#WishartCholesky.log_pmf} Log probability mass function. @@ -369,7 +366,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -385,7 +381,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob', **condition_kwargs)` {#WishartCholesky.log_prob} +#### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob')` {#WishartCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -394,7 +390,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -405,7 +400,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartCholesky.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#WishartCholesky.log_survival_function} +#### `tf.contrib.distributions.WishartCholesky.log_survival_function(value, name='log_survival_function')` {#WishartCholesky.log_survival_function} Log survival function. @@ -425,7 +420,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -524,7 +518,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf', **condition_kwargs)` {#WishartCholesky.pdf} +#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf')` {#WishartCholesky.pdf} Probability density function. @@ -533,7 +527,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -549,7 +542,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf', **condition_kwargs)` {#WishartCholesky.pmf} +#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf')` {#WishartCholesky.pmf} Probability mass function. @@ -558,7 +551,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -574,7 +566,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob', **condition_kwargs)` {#WishartCholesky.prob} +#### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob')` {#WishartCholesky.prob} Probability density/mass function (depending on `is_continuous`). @@ -583,7 +575,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -609,7 +600,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#WishartCholesky.sample} +#### `tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample')` {#WishartCholesky.sample} Generate samples of the specified shape. @@ -622,7 +613,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -653,7 +643,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.WishartCholesky.survival_function(value, name='survival_function', **condition_kwargs)` {#WishartCholesky.survival_function} +#### `tf.contrib.distributions.WishartCholesky.survival_function(value, name='survival_function')` {#WishartCholesky.survival_function} Survival function. @@ -670,7 +660,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.bijector.Bijector.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.bijector.Bijector.md index 18fde81fc0..b52554f862 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.bijector.Bijector.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.bijector.Bijector.md @@ -252,7 +252,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Bijector.forward(x, name='forward', **condition_kwargs)` {#Bijector.forward} +#### `tf.contrib.distributions.bijector.Bijector.forward(x, name='forward')` {#Bijector.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -261,7 +261,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -297,7 +296,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Bijector.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Bijector.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Bijector.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Bijector.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -306,7 +305,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -372,7 +370,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse(y, name='inverse', **condition_kwargs)` {#Bijector.inverse} +#### `tf.contrib.distributions.bijector.Bijector.inverse(y, name='inverse')` {#Bijector.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -381,7 +379,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -398,7 +395,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Bijector.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Bijector.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Bijector.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -412,7 +409,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -449,7 +445,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Bijector.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Bijector.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Bijector.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -462,7 +458,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.BetaWithSoftplusAB.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.BetaWithSoftplusAB.md index 0e2b91d622..d2c7e7de98 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.BetaWithSoftplusAB.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.BetaWithSoftplusAB.md @@ -70,7 +70,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.cdf(value, name='cdf', **condition_kwargs)` {#BetaWithSoftplusAB.cdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.cdf(value, name='cdf')` {#BetaWithSoftplusAB.cdf} Cumulative distribution function. @@ -85,7 +85,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -219,7 +218,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf', **condition_kwargs)` {#BetaWithSoftplusAB.log_cdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf')` {#BetaWithSoftplusAB.log_cdf} Log cumulative distribution function. @@ -246,7 +245,6 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -257,7 +255,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pdf(value, name='log_pdf', **condition_kwargs)` {#BetaWithSoftplusAB.log_pdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pdf(value, name='log_pdf')` {#BetaWithSoftplusAB.log_pdf} Log probability density function. @@ -266,7 +264,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -282,7 +279,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf', **condition_kwargs)` {#BetaWithSoftplusAB.log_pmf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf')` {#BetaWithSoftplusAB.log_pmf} Log probability mass function. @@ -291,7 +288,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -307,7 +303,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob', **condition_kwargs)` {#BetaWithSoftplusAB.log_prob} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob')` {#BetaWithSoftplusAB.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -316,7 +312,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -327,7 +322,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#BetaWithSoftplusAB.log_survival_function} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function')` {#BetaWithSoftplusAB.log_survival_function} Log survival function. @@ -347,7 +342,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -446,7 +440,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf', **condition_kwargs)` {#BetaWithSoftplusAB.pdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf')` {#BetaWithSoftplusAB.pdf} Probability density function. @@ -455,7 +449,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -471,7 +464,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf', **condition_kwargs)` {#BetaWithSoftplusAB.pmf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf')` {#BetaWithSoftplusAB.pmf} Probability mass function. @@ -480,7 +473,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -496,7 +488,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob', **condition_kwargs)` {#BetaWithSoftplusAB.prob} +#### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob')` {#BetaWithSoftplusAB.prob} Probability density/mass function (depending on `is_continuous`). @@ -513,7 +505,6 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -539,7 +530,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#BetaWithSoftplusAB.sample} +#### `tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample')` {#BetaWithSoftplusAB.sample} Generate samples of the specified shape. @@ -552,7 +543,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -569,7 +559,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function', **condition_kwargs)` {#BetaWithSoftplusAB.survival_function} +#### `tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function')` {#BetaWithSoftplusAB.survival_function} Survival function. @@ -586,7 +576,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md index 978c603f5c..2a06a25642 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md @@ -135,7 +135,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Binomial.cdf(value, name='cdf', **condition_kwargs)` {#Binomial.cdf} +#### `tf.contrib.distributions.Binomial.cdf(value, name='cdf')` {#Binomial.cdf} Cumulative distribution function. @@ -150,7 +150,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -284,7 +283,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Binomial.log_cdf} +#### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf')` {#Binomial.log_cdf} Log cumulative distribution function. @@ -303,7 +302,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -314,7 +312,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Binomial.log_pdf} +#### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf')` {#Binomial.log_pdf} Log probability density function. @@ -323,7 +321,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -339,7 +336,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Binomial.log_pmf} +#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf')` {#Binomial.log_pmf} Log probability mass function. @@ -348,7 +345,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -364,7 +360,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob', **condition_kwargs)` {#Binomial.log_prob} +#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob')` {#Binomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -386,7 +382,6 @@ values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -397,7 +392,7 @@ values. - - - -#### `tf.contrib.distributions.Binomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Binomial.log_survival_function} +#### `tf.contrib.distributions.Binomial.log_survival_function(value, name='log_survival_function')` {#Binomial.log_survival_function} Log survival function. @@ -417,7 +412,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -536,7 +530,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf', **condition_kwargs)` {#Binomial.pdf} +#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf')` {#Binomial.pdf} Probability density function. @@ -545,7 +539,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -561,7 +554,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf', **condition_kwargs)` {#Binomial.pmf} +#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf')` {#Binomial.pmf} Probability mass function. @@ -570,7 +563,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -586,7 +578,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Binomial.prob(value, name='prob', **condition_kwargs)` {#Binomial.prob} +#### `tf.contrib.distributions.Binomial.prob(value, name='prob')` {#Binomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -608,7 +600,6 @@ values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -634,7 +625,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Binomial.sample} +#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample')` {#Binomial.sample} Generate samples of the specified shape. @@ -647,7 +638,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -664,7 +654,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Binomial.survival_function(value, name='survival_function', **condition_kwargs)` {#Binomial.survival_function} +#### `tf.contrib.distributions.Binomial.survival_function(value, name='survival_function')` {#Binomial.survival_function} Survival function. @@ -681,7 +671,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md index 147d0ee2a4..59f0264c31 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md @@ -162,7 +162,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf', **condition_kwargs)` {#DirichletMultinomial.cdf} +#### `tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf')` {#DirichletMultinomial.cdf} Cumulative distribution function. @@ -177,7 +177,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -311,7 +310,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#DirichletMultinomial.log_cdf} +#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf')` {#DirichletMultinomial.log_cdf} Log cumulative distribution function. @@ -330,7 +329,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -341,7 +339,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#DirichletMultinomial.log_pdf} +#### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf')` {#DirichletMultinomial.log_pdf} Log probability density function. @@ -350,7 +348,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -366,7 +363,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#DirichletMultinomial.log_pmf} +#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf')` {#DirichletMultinomial.log_pmf} Log probability mass function. @@ -375,7 +372,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -391,7 +387,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob', **condition_kwargs)` {#DirichletMultinomial.log_prob} +#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob')` {#DirichletMultinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -415,7 +411,6 @@ it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -426,7 +421,7 @@ it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#DirichletMultinomial.log_survival_function} +#### `tf.contrib.distributions.DirichletMultinomial.log_survival_function(value, name='log_survival_function')` {#DirichletMultinomial.log_survival_function} Log survival function. @@ -446,7 +441,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -545,7 +539,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf', **condition_kwargs)` {#DirichletMultinomial.pdf} +#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf')` {#DirichletMultinomial.pdf} Probability density function. @@ -554,7 +548,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -570,7 +563,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf', **condition_kwargs)` {#DirichletMultinomial.pmf} +#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf')` {#DirichletMultinomial.pmf} Probability mass function. @@ -579,7 +572,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -595,7 +587,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob', **condition_kwargs)` {#DirichletMultinomial.prob} +#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob')` {#DirichletMultinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -619,7 +611,6 @@ it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -645,7 +636,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#DirichletMultinomial.sample} +#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample')` {#DirichletMultinomial.sample} Generate samples of the specified shape. @@ -658,7 +649,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -675,7 +665,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.DirichletMultinomial.survival_function(value, name='survival_function', **condition_kwargs)` {#DirichletMultinomial.survival_function} +#### `tf.contrib.distributions.DirichletMultinomial.survival_function(value, name='survival_function')` {#DirichletMultinomial.survival_function} Survival function. @@ -692,7 +682,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md index 466a667337..faf16c5e7e 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md @@ -85,7 +85,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Exponential.cdf(value, name='cdf', **condition_kwargs)` {#Exponential.cdf} +#### `tf.contrib.distributions.Exponential.cdf(value, name='cdf')` {#Exponential.cdf} Cumulative distribution function. @@ -100,7 +100,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -252,7 +251,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Exponential.log_cdf} +#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf')` {#Exponential.log_cdf} Log cumulative distribution function. @@ -271,7 +270,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -282,7 +280,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Exponential.log_pdf} +#### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf')` {#Exponential.log_pdf} Log probability density function. @@ -291,7 +289,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -307,7 +304,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Exponential.log_pmf} +#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')` {#Exponential.log_pmf} Log probability mass function. @@ -316,7 +313,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -332,7 +328,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob', **condition_kwargs)` {#Exponential.log_prob} +#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob')` {#Exponential.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -341,7 +337,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -352,7 +347,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Exponential.log_survival_function} +#### `tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function')` {#Exponential.log_survival_function} Log survival function. @@ -372,7 +367,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -470,7 +464,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf', **condition_kwargs)` {#Exponential.pdf} +#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf')` {#Exponential.pdf} Probability density function. @@ -479,7 +473,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -495,7 +488,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf', **condition_kwargs)` {#Exponential.pmf} +#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf')` {#Exponential.pmf} Probability mass function. @@ -504,7 +497,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -520,7 +512,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Exponential.prob(value, name='prob', **condition_kwargs)` {#Exponential.prob} +#### `tf.contrib.distributions.Exponential.prob(value, name='prob')` {#Exponential.prob} Probability density/mass function (depending on `is_continuous`). @@ -529,7 +521,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -555,7 +546,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Exponential.sample} +#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample')` {#Exponential.sample} Generate samples of the specified shape. @@ -568,7 +559,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -585,7 +575,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Exponential.survival_function(value, name='survival_function', **condition_kwargs)` {#Exponential.survival_function} +#### `tf.contrib.distributions.Exponential.survival_function(value, name='survival_function')` {#Exponential.survival_function} Survival function. @@ -602,7 +592,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md index 8e02821f77..d28846b58d 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md @@ -112,7 +112,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Gamma.cdf(value, name='cdf', **condition_kwargs)` {#Gamma.cdf} +#### `tf.contrib.distributions.Gamma.cdf(value, name='cdf')` {#Gamma.cdf} Cumulative distribution function. @@ -127,7 +127,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -272,7 +271,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Gamma.log_cdf} +#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf')` {#Gamma.log_cdf} Log cumulative distribution function. @@ -291,7 +290,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -302,7 +300,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Gamma.log_pdf} +#### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf')` {#Gamma.log_pdf} Log probability density function. @@ -311,7 +309,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -327,7 +324,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Gamma.log_pmf} +#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')` {#Gamma.log_pmf} Log probability mass function. @@ -336,7 +333,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -352,7 +348,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob', **condition_kwargs)` {#Gamma.log_prob} +#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob')` {#Gamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -361,7 +357,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -372,7 +367,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Gamma.log_survival_function} +#### `tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function')` {#Gamma.log_survival_function} Log survival function. @@ -392,7 +387,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -490,7 +484,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf', **condition_kwargs)` {#Gamma.pdf} +#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf')` {#Gamma.pdf} Probability density function. @@ -499,7 +493,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -515,7 +508,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf', **condition_kwargs)` {#Gamma.pmf} +#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf')` {#Gamma.pmf} Probability mass function. @@ -524,7 +517,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -540,7 +532,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Gamma.prob(value, name='prob', **condition_kwargs)` {#Gamma.prob} +#### `tf.contrib.distributions.Gamma.prob(value, name='prob')` {#Gamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -549,7 +541,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -575,7 +566,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Gamma.sample} +#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample')` {#Gamma.sample} Generate samples of the specified shape. @@ -588,7 +579,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -605,7 +595,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Gamma.survival_function(value, name='survival_function', **condition_kwargs)` {#Gamma.survival_function} +#### `tf.contrib.distributions.Gamma.survival_function(value, name='survival_function')` {#Gamma.survival_function} Survival function. @@ -622,7 +612,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.GammaWithSoftplusAlphaBeta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.GammaWithSoftplusAlphaBeta.md index 2434d62bb5..6660739883 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.GammaWithSoftplusAlphaBeta.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.GammaWithSoftplusAlphaBeta.md @@ -63,7 +63,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.cdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf')` {#GammaWithSoftplusAlphaBeta.cdf} Cumulative distribution function. @@ -78,7 +78,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -223,7 +222,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_cdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')` {#GammaWithSoftplusAlphaBeta.log_cdf} Log cumulative distribution function. @@ -242,7 +241,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -253,7 +251,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_pdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')` {#GammaWithSoftplusAlphaBeta.log_pdf} Log probability density function. @@ -262,7 +260,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -278,7 +275,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_pmf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#GammaWithSoftplusAlphaBeta.log_pmf} Log probability mass function. @@ -287,7 +284,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -303,7 +299,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_prob} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#GammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -312,7 +308,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -323,7 +318,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_survival_function} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')` {#GammaWithSoftplusAlphaBeta.log_survival_function} Log survival function. @@ -343,7 +338,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -441,7 +435,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.pdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#GammaWithSoftplusAlphaBeta.pdf} Probability density function. @@ -450,7 +444,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -466,7 +459,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.pmf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#GammaWithSoftplusAlphaBeta.pmf} Probability mass function. @@ -475,7 +468,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -491,7 +483,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.prob} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#GammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -500,7 +492,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -526,7 +517,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.sample} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')` {#GammaWithSoftplusAlphaBeta.sample} Generate samples of the specified shape. @@ -539,7 +530,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -556,7 +546,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.survival_function} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')` {#GammaWithSoftplusAlphaBeta.survival_function} Survival function. @@ -573,7 +563,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md index 14dcae61b2..687f8328ce 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md @@ -108,7 +108,7 @@ Scale parameter. - - - -#### `tf.contrib.distributions.InverseGamma.cdf(value, name='cdf', **condition_kwargs)` {#InverseGamma.cdf} +#### `tf.contrib.distributions.InverseGamma.cdf(value, name='cdf')` {#InverseGamma.cdf} Cumulative distribution function. @@ -123,7 +123,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -268,7 +267,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#InverseGamma.log_cdf} +#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf')` {#InverseGamma.log_cdf} Log cumulative distribution function. @@ -287,7 +286,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -298,7 +296,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#InverseGamma.log_pdf} +#### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf')` {#InverseGamma.log_pdf} Log probability density function. @@ -307,7 +305,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -323,7 +320,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#InverseGamma.log_pmf} +#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')` {#InverseGamma.log_pmf} Log probability mass function. @@ -332,7 +329,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -348,7 +344,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob', **condition_kwargs)` {#InverseGamma.log_prob} +#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob')` {#InverseGamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -357,7 +353,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -368,7 +363,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#InverseGamma.log_survival_function} +#### `tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function')` {#InverseGamma.log_survival_function} Log survival function. @@ -388,7 +383,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -490,7 +484,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf', **condition_kwargs)` {#InverseGamma.pdf} +#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')` {#InverseGamma.pdf} Probability density function. @@ -499,7 +493,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -515,7 +508,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf', **condition_kwargs)` {#InverseGamma.pmf} +#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')` {#InverseGamma.pmf} Probability mass function. @@ -524,7 +517,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -540,7 +532,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob', **condition_kwargs)` {#InverseGamma.prob} +#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob')` {#InverseGamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -549,7 +541,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -575,7 +566,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#InverseGamma.sample} +#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample')` {#InverseGamma.sample} Generate samples of the specified shape. @@ -588,7 +579,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -605,7 +595,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function', **condition_kwargs)` {#InverseGamma.survival_function} +#### `tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function')` {#InverseGamma.survival_function} Survival function. @@ -622,7 +612,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.md index c62af7457c..d290950a8c 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.md @@ -63,7 +63,7 @@ Scale parameter. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.cdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf')` {#InverseGammaWithSoftplusAlphaBeta.cdf} Cumulative distribution function. @@ -78,7 +78,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -223,7 +222,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_cdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')` {#InverseGammaWithSoftplusAlphaBeta.log_cdf} Log cumulative distribution function. @@ -242,7 +241,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -253,7 +251,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_pdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')` {#InverseGammaWithSoftplusAlphaBeta.log_pdf} Log probability density function. @@ -262,7 +260,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -278,7 +275,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} Log probability mass function. @@ -287,7 +284,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -303,7 +299,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_prob} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#InverseGammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -312,7 +308,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -323,7 +318,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_survival_function} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')` {#InverseGammaWithSoftplusAlphaBeta.log_survival_function} Log survival function. @@ -343,7 +338,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -445,7 +439,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.pdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#InverseGammaWithSoftplusAlphaBeta.pdf} Probability density function. @@ -454,7 +448,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -470,7 +463,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.pmf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#InverseGammaWithSoftplusAlphaBeta.pmf} Probability mass function. @@ -479,7 +472,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -495,7 +487,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.prob} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#InverseGammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -504,7 +496,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -530,7 +521,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.sample} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')` {#InverseGammaWithSoftplusAlphaBeta.sample} Generate samples of the specified shape. @@ -543,7 +534,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -560,7 +550,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.survival_function} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')` {#InverseGammaWithSoftplusAlphaBeta.survival_function} Survival function. @@ -577,7 +567,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md index b8f33583ed..f183f90eda 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md @@ -145,7 +145,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Multinomial.cdf(value, name='cdf', **condition_kwargs)` {#Multinomial.cdf} +#### `tf.contrib.distributions.Multinomial.cdf(value, name='cdf')` {#Multinomial.cdf} Cumulative distribution function. @@ -160,7 +160,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -294,7 +293,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Multinomial.log_cdf} +#### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf')` {#Multinomial.log_cdf} Log cumulative distribution function. @@ -313,7 +312,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -324,7 +322,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Multinomial.log_pdf} +#### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf')` {#Multinomial.log_pdf} Log probability density function. @@ -333,7 +331,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -349,7 +346,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Multinomial.log_pmf} +#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf')` {#Multinomial.log_pmf} Log probability mass function. @@ -358,7 +355,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -374,7 +370,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob', **condition_kwargs)` {#Multinomial.log_prob} +#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob')` {#Multinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -398,7 +394,6 @@ if it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -409,7 +404,7 @@ if it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Multinomial.log_survival_function} +#### `tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function')` {#Multinomial.log_survival_function} Log survival function. @@ -429,7 +424,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -544,7 +538,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf', **condition_kwargs)` {#Multinomial.pdf} +#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf')` {#Multinomial.pdf} Probability density function. @@ -553,7 +547,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -569,7 +562,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf', **condition_kwargs)` {#Multinomial.pmf} +#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf')` {#Multinomial.pmf} Probability mass function. @@ -578,7 +571,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -594,7 +586,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Multinomial.prob(value, name='prob', **condition_kwargs)` {#Multinomial.prob} +#### `tf.contrib.distributions.Multinomial.prob(value, name='prob')` {#Multinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -618,7 +610,6 @@ if it sums up to `n` and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -644,7 +635,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Multinomial.sample} +#### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample')` {#Multinomial.sample} Generate samples of the specified shape. @@ -657,7 +648,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -674,7 +664,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Multinomial.survival_function(value, name='survival_function', **condition_kwargs)` {#Multinomial.survival_function} +#### `tf.contrib.distributions.Multinomial.survival_function(value, name='survival_function')` {#Multinomial.survival_function} Survival function. @@ -691,7 +681,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md index 40cd917f8e..fe95fe3036 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md @@ -145,7 +145,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.cdf(value, name='cdf')` {#MultivariateNormalDiagPlusVDVT.cdf} Cumulative distribution function. @@ -160,7 +160,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -294,7 +293,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiagPlusVDVT.log_cdf} Log cumulative distribution function. @@ -313,7 +312,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -324,7 +322,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagPlusVDVT.log_pdf} Log probability density function. @@ -333,7 +331,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -349,7 +346,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagPlusVDVT.log_pmf} Log probability mass function. @@ -358,7 +355,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -374,7 +370,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob')` {#MultivariateNormalDiagPlusVDVT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -399,7 +395,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -417,7 +412,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiagPlusVDVT.log_survival_function} Log survival function. @@ -437,7 +432,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -536,7 +530,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf')` {#MultivariateNormalDiagPlusVDVT.pdf} Probability density function. @@ -545,7 +539,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -561,7 +554,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf')` {#MultivariateNormalDiagPlusVDVT.pmf} Probability mass function. @@ -570,7 +563,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -586,7 +578,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.prob} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob')` {#MultivariateNormalDiagPlusVDVT.prob} Probability density/mass function (depending on `is_continuous`). @@ -611,7 +603,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -637,7 +628,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.sample} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiagPlusVDVT.sample} Generate samples of the specified shape. @@ -650,7 +641,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -681,7 +671,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function')` {#MultivariateNormalDiagPlusVDVT.survival_function} Survival function. @@ -698,7 +688,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.BernoulliWithSigmoidP.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.BernoulliWithSigmoidP.md index 3f5d682eef..8f71e904f5 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.BernoulliWithSigmoidP.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.BernoulliWithSigmoidP.md @@ -49,7 +49,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.cdf(value, name='cdf', **condition_kwargs)` {#BernoulliWithSigmoidP.cdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.cdf(value, name='cdf')` {#BernoulliWithSigmoidP.cdf} Cumulative distribution function. @@ -64,7 +64,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -198,7 +197,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_cdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf')` {#BernoulliWithSigmoidP.log_cdf} Log cumulative distribution function. @@ -217,7 +216,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -228,7 +226,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_pdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf')` {#BernoulliWithSigmoidP.log_pdf} Log probability density function. @@ -237,7 +235,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -253,7 +250,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_pmf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf')` {#BernoulliWithSigmoidP.log_pmf} Log probability mass function. @@ -262,7 +259,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -278,7 +274,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob', **condition_kwargs)` {#BernoulliWithSigmoidP.log_prob} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob')` {#BernoulliWithSigmoidP.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -287,7 +283,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -298,7 +293,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#BernoulliWithSigmoidP.log_survival_function} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function')` {#BernoulliWithSigmoidP.log_survival_function} Log survival function. @@ -318,7 +313,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -428,7 +422,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf', **condition_kwargs)` {#BernoulliWithSigmoidP.pdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf')` {#BernoulliWithSigmoidP.pdf} Probability density function. @@ -437,7 +431,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -453,7 +446,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf', **condition_kwargs)` {#BernoulliWithSigmoidP.pmf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf')` {#BernoulliWithSigmoidP.pmf} Probability mass function. @@ -462,7 +455,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -478,7 +470,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob', **condition_kwargs)` {#BernoulliWithSigmoidP.prob} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob')` {#BernoulliWithSigmoidP.prob} Probability density/mass function (depending on `is_continuous`). @@ -487,7 +479,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -520,7 +511,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#BernoulliWithSigmoidP.sample} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample')` {#BernoulliWithSigmoidP.sample} Generate samples of the specified shape. @@ -533,7 +524,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -550,7 +540,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function', **condition_kwargs)` {#BernoulliWithSigmoidP.survival_function} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function')` {#BernoulliWithSigmoidP.survival_function} Survival function. @@ -567,7 +557,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.bijector.Invert.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.bijector.Invert.md index dc51c12ebb..efff14922a 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.bijector.Invert.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.bijector.Invert.md @@ -21,8 +21,8 @@ efficient if the base bijector implements `_forward_log_det_jacobian`. If used: ```python -y = self.inverse(x, **condition_kwargs) -return -self.inverse_log_det_jacobian(y, **condition_kwargs) +y = self.inverse(x, **kwargs) +return -self.inverse_log_det_jacobian(y, **kwargs) ``` ##### Args: @@ -50,7 +50,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Invert.forward(x, name='forward', **condition_kwargs)` {#Invert.forward} +#### `tf.contrib.distributions.bijector.Invert.forward(x, name='forward')` {#Invert.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -59,7 +59,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -95,7 +94,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Invert.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Invert.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Invert.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Invert.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -104,7 +103,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -170,7 +168,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Invert.inverse(y, name='inverse', **condition_kwargs)` {#Invert.inverse} +#### `tf.contrib.distributions.bijector.Invert.inverse(y, name='inverse')` {#Invert.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -179,7 +177,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -196,7 +193,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Invert.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Invert.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Invert.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Invert.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -210,7 +207,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -247,7 +243,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Invert.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Invert.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Invert.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Invert.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -260,7 +256,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.bijector.Softplus.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.bijector.Softplus.md index 33f31ca245..fb5c591a4f 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.bijector.Softplus.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.bijector.Softplus.md @@ -38,7 +38,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Softplus.forward(x, name='forward', **condition_kwargs)` {#Softplus.forward} +#### `tf.contrib.distributions.bijector.Softplus.forward(x, name='forward')` {#Softplus.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -47,7 +47,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -83,7 +82,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Softplus.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Softplus.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Softplus.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Softplus.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -92,7 +91,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -158,7 +156,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse(y, name='inverse', **condition_kwargs)` {#Softplus.inverse} +#### `tf.contrib.distributions.bijector.Softplus.inverse(y, name='inverse')` {#Softplus.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -167,7 +165,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -184,7 +181,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Softplus.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Softplus.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Softplus.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -198,7 +195,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -235,7 +231,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Softplus.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Softplus.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Softplus.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -248,7 +244,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Affine.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Affine.md index ee19f33d47..639c344c9a 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Affine.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Affine.md @@ -127,7 +127,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Affine.forward(x, name='forward', **condition_kwargs)` {#Affine.forward} +#### `tf.contrib.distributions.bijector.Affine.forward(x, name='forward')` {#Affine.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -136,7 +136,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -172,7 +171,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Affine.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Affine.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Affine.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Affine.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -181,7 +180,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -247,7 +245,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Affine.inverse(y, name='inverse', **condition_kwargs)` {#Affine.inverse} +#### `tf.contrib.distributions.bijector.Affine.inverse(y, name='inverse')` {#Affine.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -256,7 +254,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -273,7 +270,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Affine.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Affine.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Affine.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Affine.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -287,7 +284,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -324,7 +320,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Affine.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Affine.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Affine.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Affine.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -337,7 +333,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Chain.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Chain.md index 3e91a0ea7a..eb4f1c49b6 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Chain.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Chain.md @@ -67,7 +67,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Chain.forward(x, name='forward', **condition_kwargs)` {#Chain.forward} +#### `tf.contrib.distributions.bijector.Chain.forward(x, name='forward')` {#Chain.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -76,7 +76,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -112,7 +111,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Chain.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Chain.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Chain.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Chain.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -121,7 +120,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -187,7 +185,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Chain.inverse(y, name='inverse', **condition_kwargs)` {#Chain.inverse} +#### `tf.contrib.distributions.bijector.Chain.inverse(y, name='inverse')` {#Chain.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -196,7 +194,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -213,7 +210,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Chain.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Chain.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Chain.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Chain.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -227,7 +224,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -264,7 +260,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Chain.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Chain.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Chain.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Chain.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -277,7 +273,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Exp.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Exp.md index 589bff9c2d..04991b5273 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Exp.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.bijector.Exp.md @@ -41,7 +41,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Exp.forward(x, name='forward', **condition_kwargs)` {#Exp.forward} +#### `tf.contrib.distributions.bijector.Exp.forward(x, name='forward')` {#Exp.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -50,7 +50,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -86,7 +85,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Exp.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Exp.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Exp.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Exp.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -95,7 +94,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -161,7 +159,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Exp.inverse(y, name='inverse', **condition_kwargs)` {#Exp.inverse} +#### `tf.contrib.distributions.bijector.Exp.inverse(y, name='inverse')` {#Exp.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -170,7 +168,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -187,7 +184,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Exp.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Exp.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Exp.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Exp.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -201,7 +198,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -238,7 +234,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Exp.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Exp.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Exp.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Exp.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -251,7 +247,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md index 6dc8841505..2dc251fddd 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md @@ -159,7 +159,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Beta.cdf(value, name='cdf', **condition_kwargs)` {#Beta.cdf} +#### `tf.contrib.distributions.Beta.cdf(value, name='cdf')` {#Beta.cdf} Cumulative distribution function. @@ -174,7 +174,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -308,7 +307,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Beta.log_cdf} +#### `tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf')` {#Beta.log_cdf} Log cumulative distribution function. @@ -335,7 +334,6 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -346,7 +344,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Beta.log_pdf} +#### `tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf')` {#Beta.log_pdf} Log probability density function. @@ -355,7 +353,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -371,7 +368,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Beta.log_pmf} +#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf')` {#Beta.log_pmf} Log probability mass function. @@ -380,7 +377,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -396,7 +392,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob', **condition_kwargs)` {#Beta.log_prob} +#### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob')` {#Beta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -405,7 +401,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -416,7 +411,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Beta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Beta.log_survival_function} +#### `tf.contrib.distributions.Beta.log_survival_function(value, name='log_survival_function')` {#Beta.log_survival_function} Log survival function. @@ -436,7 +431,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -535,7 +529,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Beta.pdf(value, name='pdf', **condition_kwargs)` {#Beta.pdf} +#### `tf.contrib.distributions.Beta.pdf(value, name='pdf')` {#Beta.pdf} Probability density function. @@ -544,7 +538,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -560,7 +553,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Beta.pmf(value, name='pmf', **condition_kwargs)` {#Beta.pmf} +#### `tf.contrib.distributions.Beta.pmf(value, name='pmf')` {#Beta.pmf} Probability mass function. @@ -569,7 +562,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -585,7 +577,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Beta.prob(value, name='prob', **condition_kwargs)` {#Beta.prob} +#### `tf.contrib.distributions.Beta.prob(value, name='prob')` {#Beta.prob} Probability density/mass function (depending on `is_continuous`). @@ -602,7 +594,6 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -628,7 +619,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Beta.sample} +#### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample')` {#Beta.sample} Generate samples of the specified shape. @@ -641,7 +632,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -658,7 +648,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Beta.survival_function(value, name='survival_function', **condition_kwargs)` {#Beta.survival_function} +#### `tf.contrib.distributions.Beta.survival_function(value, name='survival_function')` {#Beta.survival_function} Survival function. @@ -675,7 +665,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md index 8187c32251..b3e3ac2ff9 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md @@ -82,7 +82,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Laplace.cdf(value, name='cdf', **condition_kwargs)` {#Laplace.cdf} +#### `tf.contrib.distributions.Laplace.cdf(value, name='cdf')` {#Laplace.cdf} Cumulative distribution function. @@ -97,7 +97,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -238,7 +237,7 @@ Distribution parameter for the location. - - - -#### `tf.contrib.distributions.Laplace.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Laplace.log_cdf} +#### `tf.contrib.distributions.Laplace.log_cdf(value, name='log_cdf')` {#Laplace.log_cdf} Log cumulative distribution function. @@ -257,7 +256,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -268,7 +266,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Laplace.log_pdf} +#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf')` {#Laplace.log_pdf} Log probability density function. @@ -277,7 +275,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -293,7 +290,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Laplace.log_pmf} +#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf} Log probability mass function. @@ -302,7 +299,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -318,7 +314,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob', **condition_kwargs)` {#Laplace.log_prob} +#### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob')` {#Laplace.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -327,7 +323,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -338,7 +333,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Laplace.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Laplace.log_survival_function} +#### `tf.contrib.distributions.Laplace.log_survival_function(value, name='log_survival_function')` {#Laplace.log_survival_function} Log survival function. @@ -358,7 +353,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -450,7 +444,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf', **condition_kwargs)` {#Laplace.pdf} +#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf')` {#Laplace.pdf} Probability density function. @@ -459,7 +453,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -475,7 +468,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf', **condition_kwargs)` {#Laplace.pmf} +#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf')` {#Laplace.pmf} Probability mass function. @@ -484,7 +477,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -500,7 +492,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Laplace.prob(value, name='prob', **condition_kwargs)` {#Laplace.prob} +#### `tf.contrib.distributions.Laplace.prob(value, name='prob')` {#Laplace.prob} Probability density/mass function (depending on `is_continuous`). @@ -509,7 +501,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -535,7 +526,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Laplace.sample} +#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample')` {#Laplace.sample} Generate samples of the specified shape. @@ -548,7 +539,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -572,7 +562,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Laplace.survival_function(value, name='survival_function', **condition_kwargs)` {#Laplace.survival_function} +#### `tf.contrib.distributions.Laplace.survival_function(value, name='survival_function')` {#Laplace.survival_function} Survival function. @@ -589,7 +579,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.LaplaceWithSoftplusScale.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.LaplaceWithSoftplusScale.md index 74ac684a7c..6d6139bd4d 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.LaplaceWithSoftplusScale.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.LaplaceWithSoftplusScale.md @@ -49,7 +49,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.cdf(value, name='cdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.cdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.cdf(value, name='cdf')` {#LaplaceWithSoftplusScale.cdf} Cumulative distribution function. @@ -64,7 +64,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -205,7 +204,7 @@ Distribution parameter for the location. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_cdf(value, name='log_cdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_cdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_cdf(value, name='log_cdf')` {#LaplaceWithSoftplusScale.log_cdf} Log cumulative distribution function. @@ -224,7 +223,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -235,7 +233,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_pdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf')` {#LaplaceWithSoftplusScale.log_pdf} Log probability density function. @@ -244,7 +242,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -260,7 +257,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_pmf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf')` {#LaplaceWithSoftplusScale.log_pmf} Log probability mass function. @@ -269,7 +266,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -285,7 +281,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_prob} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob')` {#LaplaceWithSoftplusScale.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -294,7 +290,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -305,7 +300,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_survival_function} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_survival_function(value, name='log_survival_function')` {#LaplaceWithSoftplusScale.log_survival_function} Log survival function. @@ -325,7 +320,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -417,7 +411,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.pdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf')` {#LaplaceWithSoftplusScale.pdf} Probability density function. @@ -426,7 +420,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -442,7 +435,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf', **condition_kwargs)` {#LaplaceWithSoftplusScale.pmf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf')` {#LaplaceWithSoftplusScale.pmf} Probability mass function. @@ -451,7 +444,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -467,7 +459,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob', **condition_kwargs)` {#LaplaceWithSoftplusScale.prob} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob')` {#LaplaceWithSoftplusScale.prob} Probability density/mass function (depending on `is_continuous`). @@ -476,7 +468,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -502,7 +493,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#LaplaceWithSoftplusScale.sample} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample(sample_shape=(), seed=None, name='sample')` {#LaplaceWithSoftplusScale.sample} Generate samples of the specified shape. @@ -515,7 +506,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -539,7 +529,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.survival_function(value, name='survival_function', **condition_kwargs)` {#LaplaceWithSoftplusScale.survival_function} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.survival_function(value, name='survival_function')` {#LaplaceWithSoftplusScale.survival_function} Survival function. @@ -556,7 +546,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.md index a5af6829de..522327a648 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.md @@ -49,7 +49,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.cdf(value, name='cdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.cdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.cdf(value, name='cdf')` {#StudentTWithAbsDfSoftplusSigma.cdf} Cumulative distribution function. @@ -64,7 +64,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -205,7 +204,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_cdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_cdf(value, name='log_cdf')` {#StudentTWithAbsDfSoftplusSigma.log_cdf} Log cumulative distribution function. @@ -224,7 +223,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -235,7 +233,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_pdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pdf(value, name='log_pdf')` {#StudentTWithAbsDfSoftplusSigma.log_pdf} Log probability density function. @@ -244,7 +242,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -260,7 +257,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_pmf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pmf(value, name='log_pmf')` {#StudentTWithAbsDfSoftplusSigma.log_pmf} Log probability mass function. @@ -269,7 +266,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -285,7 +281,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob(value, name='log_prob', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_prob} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob(value, name='log_prob')` {#StudentTWithAbsDfSoftplusSigma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -294,7 +290,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -305,7 +300,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_survival_function} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_survival_function(value, name='log_survival_function')` {#StudentTWithAbsDfSoftplusSigma.log_survival_function} Log survival function. @@ -325,7 +320,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -430,7 +424,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pdf(value, name='pdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.pdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pdf(value, name='pdf')` {#StudentTWithAbsDfSoftplusSigma.pdf} Probability density function. @@ -439,7 +433,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -455,7 +448,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pmf(value, name='pmf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.pmf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pmf(value, name='pmf')` {#StudentTWithAbsDfSoftplusSigma.pmf} Probability mass function. @@ -464,7 +457,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -480,7 +472,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.prob(value, name='prob', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.prob} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.prob(value, name='prob')` {#StudentTWithAbsDfSoftplusSigma.prob} Probability density/mass function (depending on `is_continuous`). @@ -489,7 +481,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -515,7 +506,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.sample} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample(sample_shape=(), seed=None, name='sample')` {#StudentTWithAbsDfSoftplusSigma.sample} Generate samples of the specified shape. @@ -528,7 +519,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -552,7 +542,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.survival_function(value, name='survival_function', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.survival_function} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.survival_function(value, name='survival_function')` {#StudentTWithAbsDfSoftplusSigma.survival_function} Survival function. @@ -569,7 +559,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.bijector.AffineLinearOperator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.bijector.AffineLinearOperator.md index 04ceea12c7..bfa8d4ac6d 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.bijector.AffineLinearOperator.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.bijector.AffineLinearOperator.md @@ -87,7 +87,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.AffineLinearOperator.forward(x, name='forward', **condition_kwargs)` {#AffineLinearOperator.forward} +#### `tf.contrib.distributions.bijector.AffineLinearOperator.forward(x, name='forward')` {#AffineLinearOperator.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -96,7 +96,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -132,7 +131,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.AffineLinearOperator.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#AffineLinearOperator.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.AffineLinearOperator.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#AffineLinearOperator.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -141,7 +140,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -207,7 +205,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse(y, name='inverse', **condition_kwargs)` {#AffineLinearOperator.inverse} +#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse(y, name='inverse')` {#AffineLinearOperator.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -216,7 +214,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -233,7 +230,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#AffineLinearOperator.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#AffineLinearOperator.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -247,7 +244,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -284,7 +280,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#AffineLinearOperator.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.AffineLinearOperator.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#AffineLinearOperator.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -297,7 +293,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.bijector.Identity.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.bijector.Identity.md index aafe8528ad..f1e1465fd3 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.bijector.Identity.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.bijector.Identity.md @@ -26,7 +26,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Identity.forward(x, name='forward', **condition_kwargs)` {#Identity.forward} +#### `tf.contrib.distributions.bijector.Identity.forward(x, name='forward')` {#Identity.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -35,7 +35,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -71,7 +70,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Identity.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Identity.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Identity.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Identity.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -80,7 +79,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -146,7 +144,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Identity.inverse(y, name='inverse', **condition_kwargs)` {#Identity.inverse} +#### `tf.contrib.distributions.bijector.Identity.inverse(y, name='inverse')` {#Identity.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -155,7 +153,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -172,7 +169,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Identity.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Identity.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Identity.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Identity.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -186,7 +183,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -223,7 +219,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Identity.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Identity.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Identity.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Identity.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -236,7 +232,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ConditionalTransformedDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ConditionalTransformedDistribution.md new file mode 100644 index 0000000000..696df0f70c --- /dev/null +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ConditionalTransformedDistribution.md @@ -0,0 +1,498 @@ +A TransformedDistribution that allows intrinsic conditioning. +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.__init__(distribution, bijector=None, batch_shape=None, event_shape=None, validate_args=False, name=None)` {#ConditionalTransformedDistribution.__init__} + +Construct a Transformed Distribution. + +##### Args: + + +* <b>`distribution`</b>: The base distribution instance to transform. Typically an + instance of `Distribution`. +* <b>`bijector`</b>: The object responsible for calculating the transformation. + Typically an instance of `Bijector`. `None` means `Identity()`. +* <b>`batch_shape`</b>: `integer` vector `Tensor` which overrides `distribution` + `batch_shape`; valid only if `distribution.is_scalar_batch()`. +* <b>`event_shape`</b>: `integer` vector `Tensor` which overrides `distribution` + `event_shape`; valid only if `distribution.is_scalar_event()`. +* <b>`validate_args`</b>: Python Boolean. Whether to validate input with asserts. + If `validate_args` is `False`, and the inputs are invalid, + correct behavior is not guaranteed. +* <b>`name`</b>: The name for the distribution. Default: + `bijector.name + distribution.name`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.allow_nan_stats` {#ConditionalTransformedDistribution.allow_nan_stats} + +Python boolean describing behavior when a stat is undefined. + +Stats return +/- infinity when it makes sense. E.g., the variance +of a Cauchy distribution is infinity. However, sometimes the +statistic is undefined, e.g., if a distribution's pdf does not achieve a +maximum within the support of the distribution, the mode is undefined. +If the mean is undefined, then by definition the variance is undefined. +E.g. the mean for Student's T for df = 1 is undefined (no clear way to say +it is either + or - infinity), so the variance = E[(X - mean)^2] is also +undefined. + +##### Returns: + + +* <b>`allow_nan_stats`</b>: Python boolean. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.batch_shape(name='batch_shape')` {#ConditionalTransformedDistribution.batch_shape} + +Shape of a single sample from a single event index as a 1-D `Tensor`. + +The product of the dimensions of the `batch_shape` is the number of +independent distributions of this kind the instance represents. + +##### Args: + + +* <b>`name`</b>: name to give to the op + +##### Returns: + + +* <b>`batch_shape`</b>: `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.bijector` {#ConditionalTransformedDistribution.bijector} + +Function transforming x => y. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.cdf(*args, **kwargs)` {#ConditionalTransformedDistribution.cdf} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.copy(**override_parameters_kwargs)` {#ConditionalTransformedDistribution.copy} + +Creates a deep copy of the distribution. + +Note: the copy distribution may continue to depend on the original +intialization arguments. + +##### Args: + + +* <b>`**override_parameters_kwargs`</b>: String/value dictionary of initialization + arguments to override with new values. + +##### Returns: + + +* <b>`distribution`</b>: A new instance of `type(self)` intitialized from the union + of self.parameters and override_parameters_kwargs, i.e., + `dict(self.parameters, **override_parameters_kwargs)`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.distribution` {#ConditionalTransformedDistribution.distribution} + +Base distribution, p(x). + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.dtype` {#ConditionalTransformedDistribution.dtype} + +The `DType` of `Tensor`s handled by this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.entropy(name='entropy')` {#ConditionalTransformedDistribution.entropy} + +Shannon entropy in nats. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.event_shape(name='event_shape')` {#ConditionalTransformedDistribution.event_shape} + +Shape of a single sample from a single batch as a 1-D int32 `Tensor`. + +##### Args: + + +* <b>`name`</b>: name to give to the op + +##### Returns: + + +* <b>`event_shape`</b>: `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.get_batch_shape()` {#ConditionalTransformedDistribution.get_batch_shape} + +Shape of a single sample from a single event index as a `TensorShape`. + +Same meaning as `batch_shape`. May be only partially defined. + +##### Returns: + + +* <b>`batch_shape`</b>: `TensorShape`, possibly unknown. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.get_event_shape()` {#ConditionalTransformedDistribution.get_event_shape} + +Shape of a single sample from a single batch as a `TensorShape`. + +Same meaning as `event_shape`. May be only partially defined. + +##### Returns: + + +* <b>`event_shape`</b>: `TensorShape`, possibly unknown. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.is_continuous` {#ConditionalTransformedDistribution.is_continuous} + + + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.is_scalar_batch(name='is_scalar_batch')` {#ConditionalTransformedDistribution.is_scalar_batch} + +Indicates that `batch_shape == []`. + +##### Args: + + +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`is_scalar_batch`</b>: `Boolean` `scalar` `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.is_scalar_event(name='is_scalar_event')` {#ConditionalTransformedDistribution.is_scalar_event} + +Indicates that `event_shape == []`. + +##### Args: + + +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`is_scalar_event`</b>: `Boolean` `scalar` `Tensor`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_cdf(*args, **kwargs)` {#ConditionalTransformedDistribution.log_cdf} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_pdf(value, name='log_pdf')` {#ConditionalTransformedDistribution.log_pdf} + +Log probability density function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if not `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_pmf(value, name='log_pmf')` {#ConditionalTransformedDistribution.log_pmf} + +Log probability mass function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_prob(*args, **kwargs)` {#ConditionalTransformedDistribution.log_prob} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.log_survival_function(*args, **kwargs)` {#ConditionalTransformedDistribution.log_survival_function} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.mean(name='mean')` {#ConditionalTransformedDistribution.mean} + +Mean. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.mode(name='mode')` {#ConditionalTransformedDistribution.mode} + +Mode. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.name` {#ConditionalTransformedDistribution.name} + +Name prepended to all ops created by this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#ConditionalTransformedDistribution.param_shapes} + +Shapes of parameters given the desired shape of a call to `sample()`. + +This is a class method that describes what key/value arguments are required +to instantiate the given `Distribution` so that a particular shape is +returned for that instance's call to `sample()`. + +Subclasses should override class method `_param_shapes`. + +##### Args: + + +* <b>`sample_shape`</b>: `Tensor` or python list/tuple. Desired shape of a call to + `sample()`. +* <b>`name`</b>: name to prepend ops with. + +##### Returns: + + `dict` of parameter name to `Tensor` shapes. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.param_static_shapes(cls, sample_shape)` {#ConditionalTransformedDistribution.param_static_shapes} + +param_shapes with static (i.e. `TensorShape`) shapes. + +This is a class method that describes what key/value arguments are required +to instantiate the given `Distribution` so that a particular shape is +returned for that instance's call to `sample()`. Assumes that +the sample's shape is known statically. + +Subclasses should override class method `_param_shapes` to return +constant-valued tensors when constant values are fed. + +##### Args: + + +* <b>`sample_shape`</b>: `TensorShape` or python list/tuple. Desired shape of a call + to `sample()`. + +##### Returns: + + `dict` of parameter name to `TensorShape`. + +##### Raises: + + +* <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.parameters` {#ConditionalTransformedDistribution.parameters} + +Dictionary of parameters used to instantiate this `Distribution`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.pdf(value, name='pdf')` {#ConditionalTransformedDistribution.pdf} + +Probability density function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if not `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.pmf(value, name='pmf')` {#ConditionalTransformedDistribution.pmf} + +Probability mass function. + +##### Args: + + +* <b>`value`</b>: `float` or `double` `Tensor`. +* <b>`name`</b>: The name to give this op. + +##### Returns: + + +* <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with + values of type `self.dtype`. + +##### Raises: + + +* <b>`TypeError`</b>: if `is_continuous`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.prob(*args, **kwargs)` {#ConditionalTransformedDistribution.prob} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.reparameterization_type` {#ConditionalTransformedDistribution.reparameterization_type} + +Describes how samples from the distribution are reparameterized. + +Currently this is one of the static instances +`distributions.FULLY_REPARAMETERIZED` +or `distributions.NOT_REPARAMETERIZED`. + +##### Returns: + + An instance of `ReparameterizationType`. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.sample(*args, **kwargs)` {#ConditionalTransformedDistribution.sample} + +##### `kwargs`: + +* `**condition_kwargs`: Named arguments forwarded to subclass implementation. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.stddev(name='stddev')` {#ConditionalTransformedDistribution.stddev} + +Standard deviation. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.survival_function(*args, **kwargs)` {#ConditionalTransformedDistribution.survival_function} + +Additional documentation from `ConditionalTransformedDistribution`: + +##### `kwargs`: + +* `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.validate_args` {#ConditionalTransformedDistribution.validate_args} + +Python boolean indicated possibly expensive checks are enabled. + + +- - - + +#### `tf.contrib.distributions.ConditionalTransformedDistribution.variance(name='variance')` {#ConditionalTransformedDistribution.variance} + +Variance. + + diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ExponentialWithSoftplusLam.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ExponentialWithSoftplusLam.md index 5c31250fbe..946b42ae37 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ExponentialWithSoftplusLam.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ExponentialWithSoftplusLam.md @@ -63,7 +63,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.cdf(value, name='cdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.cdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.cdf(value, name='cdf')` {#ExponentialWithSoftplusLam.cdf} Cumulative distribution function. @@ -78,7 +78,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -230,7 +229,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_cdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf')` {#ExponentialWithSoftplusLam.log_cdf} Log cumulative distribution function. @@ -249,7 +248,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -260,7 +258,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_pdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf')` {#ExponentialWithSoftplusLam.log_pdf} Log probability density function. @@ -269,7 +267,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -285,7 +282,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_pmf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf')` {#ExponentialWithSoftplusLam.log_pmf} Log probability mass function. @@ -294,7 +291,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -310,7 +306,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_prob} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob')` {#ExponentialWithSoftplusLam.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -319,7 +315,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -330,7 +325,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_survival_function} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_survival_function(value, name='log_survival_function')` {#ExponentialWithSoftplusLam.log_survival_function} Log survival function. @@ -350,7 +345,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -448,7 +442,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.pdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf')` {#ExponentialWithSoftplusLam.pdf} Probability density function. @@ -457,7 +451,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -473,7 +466,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf', **condition_kwargs)` {#ExponentialWithSoftplusLam.pmf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf')` {#ExponentialWithSoftplusLam.pmf} Probability mass function. @@ -482,7 +475,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -498,7 +490,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob', **condition_kwargs)` {#ExponentialWithSoftplusLam.prob} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob')` {#ExponentialWithSoftplusLam.prob} Probability density/mass function (depending on `is_continuous`). @@ -507,7 +499,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -533,7 +524,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#ExponentialWithSoftplusLam.sample} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample(sample_shape=(), seed=None, name='sample')` {#ExponentialWithSoftplusLam.sample} Generate samples of the specified shape. @@ -546,7 +537,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -563,7 +553,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.survival_function(value, name='survival_function', **condition_kwargs)` {#ExponentialWithSoftplusLam.survival_function} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.survival_function(value, name='survival_function')` {#ExponentialWithSoftplusLam.survival_function} Survival function. @@ -580,7 +570,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md index 4762c1245b..6c4918d19c 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md @@ -110,7 +110,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalFull.cdf} +#### `tf.contrib.distributions.MultivariateNormalFull.cdf(value, name='cdf')` {#MultivariateNormalFull.cdf} Cumulative distribution function. @@ -125,7 +125,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -259,7 +258,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalFull.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_cdf(value, name='log_cdf')` {#MultivariateNormalFull.log_cdf} Log cumulative distribution function. @@ -278,7 +277,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -289,7 +287,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalFull.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf')` {#MultivariateNormalFull.log_pdf} Log probability density function. @@ -298,7 +296,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -314,7 +311,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalFull.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf')` {#MultivariateNormalFull.log_pmf} Log probability mass function. @@ -323,7 +320,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -339,7 +335,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalFull.log_prob} +#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob')` {#MultivariateNormalFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -364,7 +360,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -382,7 +377,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalFull.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalFull.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalFull.log_survival_function} Log survival function. @@ -402,7 +397,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -501,7 +495,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalFull.pdf} +#### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf')` {#MultivariateNormalFull.pdf} Probability density function. @@ -510,7 +504,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -526,7 +519,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalFull.pmf} +#### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf')` {#MultivariateNormalFull.pmf} Probability mass function. @@ -535,7 +528,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -551,7 +543,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalFull.prob} +#### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob')` {#MultivariateNormalFull.prob} Probability density/mass function (depending on `is_continuous`). @@ -576,7 +568,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -602,7 +593,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalFull.sample} +#### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalFull.sample} Generate samples of the specified shape. @@ -615,7 +606,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -646,7 +636,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalFull.survival_function} +#### `tf.contrib.distributions.MultivariateNormalFull.survival_function(value, name='survival_function')` {#MultivariateNormalFull.survival_function} Survival function. @@ -663,7 +653,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md index 1f8dc64a1c..91bf21c03c 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md @@ -113,7 +113,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Normal.cdf(value, name='cdf', **condition_kwargs)` {#Normal.cdf} +#### `tf.contrib.distributions.Normal.cdf(value, name='cdf')` {#Normal.cdf} Cumulative distribution function. @@ -128,7 +128,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -262,7 +261,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Normal.log_cdf} +#### `tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf')` {#Normal.log_cdf} Log cumulative distribution function. @@ -281,7 +280,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -292,7 +290,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Normal.log_pdf} +#### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf')` {#Normal.log_pdf} Log probability density function. @@ -301,7 +299,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -317,7 +314,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Normal.log_pmf} +#### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf')` {#Normal.log_pmf} Log probability mass function. @@ -326,7 +323,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -342,7 +338,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob', **condition_kwargs)` {#Normal.log_prob} +#### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob')` {#Normal.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -351,7 +347,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -362,7 +357,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Normal.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Normal.log_survival_function} +#### `tf.contrib.distributions.Normal.log_survival_function(value, name='log_survival_function')` {#Normal.log_survival_function} Log survival function. @@ -382,7 +377,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -481,7 +475,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Normal.pdf(value, name='pdf', **condition_kwargs)` {#Normal.pdf} +#### `tf.contrib.distributions.Normal.pdf(value, name='pdf')` {#Normal.pdf} Probability density function. @@ -490,7 +484,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -506,7 +499,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Normal.pmf(value, name='pmf', **condition_kwargs)` {#Normal.pmf} +#### `tf.contrib.distributions.Normal.pmf(value, name='pmf')` {#Normal.pmf} Probability mass function. @@ -515,7 +508,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -531,7 +523,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Normal.prob(value, name='prob', **condition_kwargs)` {#Normal.prob} +#### `tf.contrib.distributions.Normal.prob(value, name='prob')` {#Normal.prob} Probability density/mass function (depending on `is_continuous`). @@ -540,7 +532,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -566,7 +557,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Normal.sample} +#### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample')` {#Normal.sample} Generate samples of the specified shape. @@ -579,7 +570,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -603,7 +593,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Normal.survival_function(value, name='survival_function', **condition_kwargs)` {#Normal.survival_function} +#### `tf.contrib.distributions.Normal.survival_function(value, name='survival_function')` {#Normal.survival_function} Survival function. @@ -620,7 +610,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.bijector.Inline.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.bijector.Inline.md index 031d12cdab..ee95b51d03 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.bijector.Inline.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.bijector.Inline.md @@ -51,7 +51,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Inline.forward(x, name='forward', **condition_kwargs)` {#Inline.forward} +#### `tf.contrib.distributions.bijector.Inline.forward(x, name='forward')` {#Inline.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -60,7 +60,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -96,7 +95,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Inline.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#Inline.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Inline.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#Inline.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -105,7 +104,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -171,7 +169,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.Inline.inverse(y, name='inverse', **condition_kwargs)` {#Inline.inverse} +#### `tf.contrib.distributions.bijector.Inline.inverse(y, name='inverse')` {#Inline.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -180,7 +178,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -197,7 +194,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Inline.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#Inline.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Inline.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#Inline.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -211,7 +208,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -248,7 +244,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.Inline.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Inline.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Inline.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#Inline.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -261,7 +257,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.bijector.PowerTransform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.bijector.PowerTransform.md index 4a0e2a5b4c..19426933cd 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.bijector.PowerTransform.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.bijector.PowerTransform.md @@ -37,7 +37,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.PowerTransform.forward(x, name='forward', **condition_kwargs)` {#PowerTransform.forward} +#### `tf.contrib.distributions.bijector.PowerTransform.forward(x, name='forward')` {#PowerTransform.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -46,7 +46,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -82,7 +81,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.PowerTransform.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#PowerTransform.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.PowerTransform.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#PowerTransform.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -91,7 +90,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -157,7 +155,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.PowerTransform.inverse(y, name='inverse', **condition_kwargs)` {#PowerTransform.inverse} +#### `tf.contrib.distributions.bijector.PowerTransform.inverse(y, name='inverse')` {#PowerTransform.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -166,7 +164,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -183,7 +180,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.PowerTransform.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#PowerTransform.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.PowerTransform.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#PowerTransform.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -197,7 +194,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -234,7 +230,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.PowerTransform.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#PowerTransform.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.PowerTransform.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#PowerTransform.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -247,7 +243,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Mixture.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Mixture.md index 657f9479bd..4a4f4ee831 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Mixture.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Mixture.md @@ -103,7 +103,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Mixture.cdf(value, name='cdf', **condition_kwargs)` {#Mixture.cdf} +#### `tf.contrib.distributions.Mixture.cdf(value, name='cdf')` {#Mixture.cdf} Cumulative distribution function. @@ -118,7 +118,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -305,7 +304,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.Mixture.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Mixture.log_cdf} +#### `tf.contrib.distributions.Mixture.log_cdf(value, name='log_cdf')` {#Mixture.log_cdf} Log cumulative distribution function. @@ -324,7 +323,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -335,7 +333,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Mixture.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Mixture.log_pdf} +#### `tf.contrib.distributions.Mixture.log_pdf(value, name='log_pdf')` {#Mixture.log_pdf} Log probability density function. @@ -344,7 +342,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -360,7 +357,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Mixture.log_pmf} +#### `tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf')` {#Mixture.log_pmf} Log probability mass function. @@ -369,7 +366,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -385,7 +381,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Mixture.log_prob(value, name='log_prob', **condition_kwargs)` {#Mixture.log_prob} +#### `tf.contrib.distributions.Mixture.log_prob(value, name='log_prob')` {#Mixture.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -394,7 +390,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -405,7 +400,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Mixture.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Mixture.log_survival_function} +#### `tf.contrib.distributions.Mixture.log_survival_function(value, name='log_survival_function')` {#Mixture.log_survival_function} Log survival function. @@ -425,7 +420,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -524,7 +518,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Mixture.pdf(value, name='pdf', **condition_kwargs)` {#Mixture.pdf} +#### `tf.contrib.distributions.Mixture.pdf(value, name='pdf')` {#Mixture.pdf} Probability density function. @@ -533,7 +527,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -549,7 +542,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Mixture.pmf(value, name='pmf', **condition_kwargs)` {#Mixture.pmf} +#### `tf.contrib.distributions.Mixture.pmf(value, name='pmf')` {#Mixture.pmf} Probability mass function. @@ -558,7 +551,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -574,7 +566,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Mixture.prob(value, name='prob', **condition_kwargs)` {#Mixture.prob} +#### `tf.contrib.distributions.Mixture.prob(value, name='prob')` {#Mixture.prob} Probability density/mass function (depending on `is_continuous`). @@ -583,7 +575,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -609,7 +600,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Mixture.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Mixture.sample} +#### `tf.contrib.distributions.Mixture.sample(sample_shape=(), seed=None, name='sample')` {#Mixture.sample} Generate samples of the specified shape. @@ -622,7 +613,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -639,7 +629,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Mixture.survival_function(value, name='survival_function', **condition_kwargs)` {#Mixture.survival_function} +#### `tf.contrib.distributions.Mixture.survival_function(value, name='survival_function')` {#Mixture.survival_function} Survival function. @@ -656,7 +646,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.NormalWithSoftplusSigma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.NormalWithSoftplusSigma.md index 5cdf3f035f..1550fcac87 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.NormalWithSoftplusSigma.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.NormalWithSoftplusSigma.md @@ -49,7 +49,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.cdf(value, name='cdf', **condition_kwargs)` {#NormalWithSoftplusSigma.cdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.cdf(value, name='cdf')` {#NormalWithSoftplusSigma.cdf} Cumulative distribution function. @@ -64,7 +64,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -198,7 +197,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_cdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_cdf(value, name='log_cdf')` {#NormalWithSoftplusSigma.log_cdf} Log cumulative distribution function. @@ -217,7 +216,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -228,7 +226,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_pdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pdf(value, name='log_pdf')` {#NormalWithSoftplusSigma.log_pdf} Log probability density function. @@ -237,7 +235,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -253,7 +250,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_pmf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pmf(value, name='log_pmf')` {#NormalWithSoftplusSigma.log_pmf} Log probability mass function. @@ -262,7 +259,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -278,7 +274,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_prob(value, name='log_prob', **condition_kwargs)` {#NormalWithSoftplusSigma.log_prob} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_prob(value, name='log_prob')` {#NormalWithSoftplusSigma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -287,7 +283,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -298,7 +293,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#NormalWithSoftplusSigma.log_survival_function} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_survival_function(value, name='log_survival_function')` {#NormalWithSoftplusSigma.log_survival_function} Log survival function. @@ -318,7 +313,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -417,7 +411,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.pdf(value, name='pdf', **condition_kwargs)` {#NormalWithSoftplusSigma.pdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.pdf(value, name='pdf')` {#NormalWithSoftplusSigma.pdf} Probability density function. @@ -426,7 +420,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -442,7 +435,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.pmf(value, name='pmf', **condition_kwargs)` {#NormalWithSoftplusSigma.pmf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.pmf(value, name='pmf')` {#NormalWithSoftplusSigma.pmf} Probability mass function. @@ -451,7 +444,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -467,7 +459,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.prob(value, name='prob', **condition_kwargs)` {#NormalWithSoftplusSigma.prob} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.prob(value, name='prob')` {#NormalWithSoftplusSigma.prob} Probability density/mass function (depending on `is_continuous`). @@ -476,7 +468,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -502,7 +493,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#NormalWithSoftplusSigma.sample} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample(sample_shape=(), seed=None, name='sample')` {#NormalWithSoftplusSigma.sample} Generate samples of the specified shape. @@ -515,7 +506,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -539,7 +529,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.survival_function(value, name='survival_function', **condition_kwargs)` {#NormalWithSoftplusSigma.survival_function} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.survival_function(value, name='survival_function')` {#NormalWithSoftplusSigma.survival_function} Survival function. @@ -556,7 +546,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.bijector.SoftmaxCentered.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.bijector.SoftmaxCentered.md index e7403f8417..ae567ac500 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.bijector.SoftmaxCentered.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.bijector.SoftmaxCentered.md @@ -41,7 +41,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.SoftmaxCentered.forward(x, name='forward', **condition_kwargs)` {#SoftmaxCentered.forward} +#### `tf.contrib.distributions.bijector.SoftmaxCentered.forward(x, name='forward')` {#SoftmaxCentered.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -50,7 +50,6 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). * <b>`x`</b>: `Tensor`. The input to the "forward" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -86,7 +85,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.SoftmaxCentered.forward_log_det_jacobian(x, name='forward_log_det_jacobian', **condition_kwargs)` {#SoftmaxCentered.forward_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SoftmaxCentered.forward_log_det_jacobian(x, name='forward_log_det_jacobian')` {#SoftmaxCentered.forward_log_det_jacobian} Returns both the forward_log_det_jacobian. @@ -95,7 +94,6 @@ Returns both the forward_log_det_jacobian. * <b>`x`</b>: `Tensor`. The input to the "forward" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -161,7 +159,7 @@ Returns this `Bijector`'s graph_parents as a Python list. - - - -#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse(y, name='inverse', **condition_kwargs)` {#SoftmaxCentered.inverse} +#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse(y, name='inverse')` {#SoftmaxCentered.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). @@ -170,7 +168,6 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). * <b>`y`</b>: `Tensor`. The input to the "inverse" evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -187,7 +184,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#SoftmaxCentered.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian')` {#SoftmaxCentered.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -201,7 +198,6 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -238,7 +234,7 @@ Shape of a single sample from a single batch as an `int32` 1D `Tensor`. - - - -#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#SoftmaxCentered.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.SoftmaxCentered.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian')` {#SoftmaxCentered.inverse_log_det_jacobian} Returns the (log o det o Jacobian o inverse)(y). @@ -251,7 +247,6 @@ Note that `forward_log_det_jacobian` is the negative of this function. * <b>`y`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.md index a8dd1af055..f199747531 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.md @@ -49,7 +49,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.cdf(value, name='cdf')` {#MultivariateNormalDiagWithSoftplusStDev.cdf} Cumulative distribution function. @@ -64,7 +64,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -198,7 +197,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiagWithSoftplusStDev.log_cdf} Log cumulative distribution function. @@ -217,7 +216,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -228,7 +226,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pdf} Log probability density function. @@ -237,7 +235,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -253,7 +250,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pmf} Log probability mass function. @@ -262,7 +259,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -278,7 +274,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob')` {#MultivariateNormalDiagWithSoftplusStDev.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -303,7 +299,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -321,7 +316,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiagWithSoftplusStDev.log_survival_function} Log survival function. @@ -341,7 +336,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -440,7 +434,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf')` {#MultivariateNormalDiagWithSoftplusStDev.pdf} Probability density function. @@ -449,7 +443,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -465,7 +458,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf')` {#MultivariateNormalDiagWithSoftplusStDev.pmf} Probability mass function. @@ -474,7 +467,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -490,7 +482,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.prob} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob')` {#MultivariateNormalDiagWithSoftplusStDev.prob} Probability density/mass function (depending on `is_continuous`). @@ -515,7 +507,6 @@ or * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -541,7 +532,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.sample} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiagWithSoftplusStDev.sample} Generate samples of the specified shape. @@ -554,7 +545,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -585,7 +575,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.survival_function(value, name='survival_function')` {#MultivariateNormalDiagWithSoftplusStDev.survival_function} Survival function. @@ -602,7 +592,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md index 4a0bb9d824..a54e2bea04 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md @@ -73,7 +73,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Poisson.cdf(value, name='cdf', **condition_kwargs)` {#Poisson.cdf} +#### `tf.contrib.distributions.Poisson.cdf(value, name='cdf')` {#Poisson.cdf} Cumulative distribution function. @@ -88,7 +88,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -229,7 +228,7 @@ Rate parameter. - - - -#### `tf.contrib.distributions.Poisson.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Poisson.log_cdf} +#### `tf.contrib.distributions.Poisson.log_cdf(value, name='log_cdf')` {#Poisson.log_cdf} Log cumulative distribution function. @@ -248,7 +247,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -259,7 +257,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Poisson.log_pdf} +#### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf')` {#Poisson.log_pdf} Log probability density function. @@ -268,7 +266,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -284,7 +281,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Poisson.log_pmf} +#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf')` {#Poisson.log_pmf} Log probability mass function. @@ -293,7 +290,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -309,7 +305,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob', **condition_kwargs)` {#Poisson.log_prob} +#### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob')` {#Poisson.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -325,7 +321,6 @@ legal if it is non-negative and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -336,7 +331,7 @@ legal if it is non-negative and its components are equal to integer values. - - - -#### `tf.contrib.distributions.Poisson.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Poisson.log_survival_function} +#### `tf.contrib.distributions.Poisson.log_survival_function(value, name='log_survival_function')` {#Poisson.log_survival_function} Log survival function. @@ -356,7 +351,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -454,7 +448,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf', **condition_kwargs)` {#Poisson.pdf} +#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf')` {#Poisson.pdf} Probability density function. @@ -463,7 +457,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -479,7 +472,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf', **condition_kwargs)` {#Poisson.pmf} +#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf')` {#Poisson.pmf} Probability mass function. @@ -488,7 +481,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -504,7 +496,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Poisson.prob(value, name='prob', **condition_kwargs)` {#Poisson.prob} +#### `tf.contrib.distributions.Poisson.prob(value, name='prob')` {#Poisson.prob} Probability density/mass function (depending on `is_continuous`). @@ -520,7 +512,6 @@ legal if it is non-negative and its components are equal to integer values. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -546,7 +537,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Poisson.sample} +#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample')` {#Poisson.sample} Generate samples of the specified shape. @@ -559,7 +550,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -576,7 +566,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Poisson.survival_function(value, name='survival_function', **condition_kwargs)` {#Poisson.survival_function} +#### `tf.contrib.distributions.Poisson.survival_function(value, name='survival_function')` {#Poisson.survival_function} Survival function. @@ -593,7 +583,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md index 736eadcdab..11eb2d48be 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md @@ -124,7 +124,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.WishartFull.cdf(value, name='cdf', **condition_kwargs)` {#WishartFull.cdf} +#### `tf.contrib.distributions.WishartFull.cdf(value, name='cdf')` {#WishartFull.cdf} Cumulative distribution function. @@ -139,7 +139,6 @@ cdf(x) := P[X <= x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -294,7 +293,7 @@ Indicates that `event_shape == []`. - - - -#### `tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf', **condition_kwargs)` {#WishartFull.log_cdf} +#### `tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf')` {#WishartFull.log_cdf} Log cumulative distribution function. @@ -313,7 +312,6 @@ a more accurate answer than simply taking the logarithm of the `cdf` when * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -331,7 +329,7 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf', **condition_kwargs)` {#WishartFull.log_pdf} +#### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf')` {#WishartFull.log_pdf} Log probability density function. @@ -340,7 +338,6 @@ Log probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -356,7 +353,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf', **condition_kwargs)` {#WishartFull.log_pmf} +#### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf')` {#WishartFull.log_pmf} Log probability mass function. @@ -365,7 +362,6 @@ Log probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -381,7 +377,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob', **condition_kwargs)` {#WishartFull.log_prob} +#### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob')` {#WishartFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -390,7 +386,6 @@ Log probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -401,7 +396,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartFull.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#WishartFull.log_survival_function} +#### `tf.contrib.distributions.WishartFull.log_survival_function(value, name='log_survival_function')` {#WishartFull.log_survival_function} Log survival function. @@ -421,7 +416,6 @@ survival function, which are more accurate than `1 - cdf(x)` when `x >> 1`. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -520,7 +514,7 @@ Dictionary of parameters used to instantiate this `Distribution`. - - - -#### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf', **condition_kwargs)` {#WishartFull.pdf} +#### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf')` {#WishartFull.pdf} Probability density function. @@ -529,7 +523,6 @@ Probability density function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -545,7 +538,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf', **condition_kwargs)` {#WishartFull.pmf} +#### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf')` {#WishartFull.pmf} Probability mass function. @@ -554,7 +547,6 @@ Probability mass function. * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -570,7 +562,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.WishartFull.prob(value, name='prob', **condition_kwargs)` {#WishartFull.prob} +#### `tf.contrib.distributions.WishartFull.prob(value, name='prob')` {#WishartFull.prob} Probability density/mass function (depending on `is_continuous`). @@ -579,7 +571,6 @@ Probability density/mass function (depending on `is_continuous`). * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -605,7 +596,7 @@ or `distributions.NOT_REPARAMETERIZED`. - - - -#### `tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#WishartFull.sample} +#### `tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample')` {#WishartFull.sample} Generate samples of the specified shape. @@ -618,7 +609,6 @@ sample. * <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples. * <b>`seed`</b>: Python integer seed for RNG * <b>`name`</b>: name to give to the op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: @@ -649,7 +639,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.WishartFull.survival_function(value, name='survival_function', **condition_kwargs)` {#WishartFull.survival_function} +#### `tf.contrib.distributions.WishartFull.survival_function(value, name='survival_function')` {#WishartFull.survival_function} Survival function. @@ -666,7 +656,6 @@ survival_function(x) = P[X > x] * <b>`value`</b>: `float` or `double` `Tensor`. * <b>`name`</b>: The name to give this op. -* <b>`**condition_kwargs`</b>: Named arguments forwarded to subclass implementation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md index cac9bd6833..9dac97a5c9 100644 --- a/tensorflow/g3doc/api_docs/python/index.md +++ b/tensorflow/g3doc/api_docs/python/index.md @@ -745,6 +745,8 @@ * [`Categorical`](../../api_docs/python/contrib.distributions.md#Categorical) * [`Chi2`](../../api_docs/python/contrib.distributions.md#Chi2) * [`Chi2WithAbsDf`](../../api_docs/python/contrib.distributions.md#Chi2WithAbsDf) + * [`ConditionalDistribution`](../../api_docs/python/contrib.distributions.md#ConditionalDistribution) + * [`ConditionalTransformedDistribution`](../../api_docs/python/contrib.distributions.md#ConditionalTransformedDistribution) * [`Dirichlet`](../../api_docs/python/contrib.distributions.md#Dirichlet) * [`DirichletMultinomial`](../../api_docs/python/contrib.distributions.md#DirichletMultinomial) * [`Distribution`](../../api_docs/python/contrib.distributions.md#Distribution) |