diff options
| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2016-10-14 09:53:01 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2016-10-14 11:05:31 -0700 |
| commit | a174e7127ba5e57f942305efea91061d3ea93133 (patch) | |
| tree | 64a4795419c8646c903a79fc8dbe35ac07020489 | |
| parent | a874293581be992398d2396aa79225695f240fc5 (diff) | |
Update generated Python Op docs.
Change: 136173826
53 files changed, 2254 insertions, 1182 deletions
diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md index 226c06c069..e3c58f717c 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md +++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.bijector.md @@ -146,6 +146,38 @@ Subclass Requirements: `inverse_log_det_jacobian` then he or she may also wish to implement these functions to avoid computing the `inverse_log_det_jacobian` or the `inverse`, respectively. + + +Tips for implementing `inverse_log_det_jacobian`: + +- In rare cases it may be easier to compute the Jacobian of the forward + transformation rather than the inverse. The two are equivalent up to sign. + + - Claim: + + Assume `Y=g(X)` is a bijection whose derivative exists and is nonzero + for its domain, i.e., `d/dX g(X)!=0`. Then: + + ```none + (log o det o jacobian o g^{-1})(Y) = -(log o det o jacobian o g)(X) + ``` + + - Proof: + + From the nonzero, differentiability of `g`, the [inverse function + theorem](https://en.wikipedia.org/wiki/Inverse_function_theorem) implies + `g^{-1}` is differentiable in the image of `g`. + Observe that `y = g(x) = g(g^{-1}(y))`. + From the chain rule we have `I = g'(g^{-1}(y))*g^{-1}'(y).` + Since `g` is a bijection and `g`, `g^{-1}` are differentiable, g{-1}' is + non-singular and: + `inv[ g'(g^{-1}(y)) ] = g^{-1}'(y)`. + The claim follows from [properties of determinant]( +https://en.wikipedia.org/wiki/Determinant#Multiplicativity_and_matrix_groups). + +- It is generally preferable to implement the Jacobian of the inverse. Doing + so should have better numerical stability and is likely to share operations + with the `inverse` implementation. - - - #### `tf.contrib.distributions.bijector.Bijector.__init__(batch_ndims=None, event_ndims=None, parameters=None, is_constant_jacobian=False, validate_args=False, dtype=None, name=None)` {#Bijector.__init__} @@ -191,7 +223,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Bijector.forward(x, name='forward')` {#Bijector.forward} +#### `tf.contrib.distributions.bijector.Bijector.forward(x, name='forward', **condition_kwargs)` {#Bijector.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -200,6 +232,7 @@ 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: @@ -215,15 +248,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse(x, name='inverse')` {#Bijector.inverse} +#### `tf.contrib.distributions.bijector.Bijector.inverse(y, name='inverse', **condition_kwargs)` {#Bijector.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -232,7 +266,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -240,7 +274,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#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', **condition_kwargs)` {#Bijector.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -252,8 +286,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -262,7 +297,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -270,20 +305,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#Bijector.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Bijector.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Bijector.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -292,7 +327,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -372,7 +407,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Identity.forward(x, name='forward')` {#Identity.forward} +#### `tf.contrib.distributions.bijector.Identity.forward(x, name='forward', **condition_kwargs)` {#Identity.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -381,6 +416,7 @@ 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: @@ -396,15 +432,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.Identity.inverse(x, name='inverse')` {#Identity.inverse} +#### `tf.contrib.distributions.bijector.Identity.inverse(y, name='inverse', **condition_kwargs)` {#Identity.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -413,7 +450,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -421,7 +458,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Identity.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#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', **condition_kwargs)` {#Identity.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -433,8 +470,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -443,7 +481,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -451,20 +489,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.Identity.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#Identity.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Identity.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Identity.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -473,7 +511,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -566,7 +604,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Inline.forward(x, name='forward')` {#Inline.forward} +#### `tf.contrib.distributions.bijector.Inline.forward(x, name='forward', **condition_kwargs)` {#Inline.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -575,6 +613,7 @@ 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: @@ -590,15 +629,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.Inline.inverse(x, name='inverse')` {#Inline.inverse} +#### `tf.contrib.distributions.bijector.Inline.inverse(y, name='inverse', **condition_kwargs)` {#Inline.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -607,7 +647,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -615,7 +655,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Inline.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#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', **condition_kwargs)` {#Inline.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -627,8 +667,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -637,7 +678,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -645,20 +686,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.Inline.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#Inline.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Inline.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Inline.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -667,7 +708,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -753,7 +794,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Exp.forward(x, name='forward')` {#Exp.forward} +#### `tf.contrib.distributions.bijector.Exp.forward(x, name='forward', **condition_kwargs)` {#Exp.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -762,6 +803,7 @@ 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: @@ -777,15 +819,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.Exp.inverse(x, name='inverse')` {#Exp.inverse} +#### `tf.contrib.distributions.bijector.Exp.inverse(y, name='inverse', **condition_kwargs)` {#Exp.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -794,7 +837,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -802,7 +845,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Exp.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#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', **condition_kwargs)` {#Exp.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -814,8 +857,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -824,7 +868,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -832,20 +876,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.Exp.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#Exp.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Exp.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Exp.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -854,7 +898,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -962,7 +1006,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.forward(x, name='forward')` {#ScaleAndShift.forward} +#### `tf.contrib.distributions.bijector.ScaleAndShift.forward(x, name='forward', **condition_kwargs)` {#ScaleAndShift.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -971,6 +1015,7 @@ 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: @@ -986,15 +1031,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse(x, name='inverse')` {#ScaleAndShift.inverse} +#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse(y, name='inverse', **condition_kwargs)` {#ScaleAndShift.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -1003,7 +1049,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -1011,7 +1057,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#ScaleAndShift.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#ScaleAndShift.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -1023,8 +1069,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -1033,7 +1080,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -1041,20 +1088,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#ScaleAndShift.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#ScaleAndShift.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -1063,7 +1110,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -1171,7 +1218,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Softplus.forward(x, name='forward')` {#Softplus.forward} +#### `tf.contrib.distributions.bijector.Softplus.forward(x, name='forward', **condition_kwargs)` {#Softplus.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -1180,6 +1227,7 @@ 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: @@ -1195,15 +1243,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse(x, name='inverse')` {#Softplus.inverse} +#### `tf.contrib.distributions.bijector.Softplus.inverse(y, name='inverse', **condition_kwargs)` {#Softplus.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -1212,7 +1261,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -1220,7 +1269,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#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', **condition_kwargs)` {#Softplus.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -1232,8 +1281,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -1242,7 +1292,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -1250,20 +1300,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#Softplus.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Softplus.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Softplus.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -1272,7 +1322,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md index 7c059a7de4..f126a3d18a 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md +++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md @@ -23,7 +23,7 @@ A generic probability distribution base class. ### Subclassing -Subclasess are expected to implement a leading-underscore version of the +Subclasses are expected to implement a leading-underscore version of the same-named function. The argument signature should be identical except for the omission of `name="..."`. For example, to enable `log_prob(value, name="log_prob")` a subclass should implement `_log_prob(value)`. @@ -200,7 +200,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Distribution.cdf(value, name='cdf')` {#Distribution.cdf} +#### `tf.contrib.distributions.Distribution.cdf(value, name='cdf', **condition_kwargs)` {#Distribution.cdf} Cumulative distribution function. @@ -215,6 +215,7 @@ 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: @@ -234,7 +235,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Distribution.entropy(name='entropy')` {#Distribution.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -298,7 +299,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Distribution.log_cdf(value, name='log_cdf')` {#Distribution.log_cdf} +#### `tf.contrib.distributions.Distribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Distribution.log_cdf} Log cumulative distribution function. @@ -317,6 +318,7 @@ 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: @@ -327,7 +329,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf')` {#Distribution.log_pdf} +#### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Distribution.log_pdf} Log probability density function. @@ -336,6 +338,7 @@ 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: @@ -351,7 +354,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf')` {#Distribution.log_pmf} +#### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Distribution.log_pmf} Log probability mass function. @@ -360,6 +363,7 @@ 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: @@ -375,7 +379,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob')` {#Distribution.log_prob} +#### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob', **condition_kwargs)` {#Distribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -384,6 +388,7 @@ 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: @@ -394,7 +399,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Distribution.log_survival_function(value, name='log_survival_function')` {#Distribution.log_survival_function} +#### `tf.contrib.distributions.Distribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Distribution.log_survival_function} Log survival function. @@ -414,6 +419,7 @@ 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 +499,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Distribution.pdf(value, name='pdf')` {#Distribution.pdf} +#### `tf.contrib.distributions.Distribution.pdf(value, name='pdf', **condition_kwargs)` {#Distribution.pdf} Probability density function. @@ -502,6 +508,7 @@ 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: @@ -517,7 +524,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Distribution.pmf(value, name='pmf')` {#Distribution.pmf} +#### `tf.contrib.distributions.Distribution.pmf(value, name='pmf', **condition_kwargs)` {#Distribution.pmf} Probability mass function. @@ -526,6 +533,7 @@ 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: @@ -541,7 +549,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Distribution.prob(value, name='prob')` {#Distribution.prob} +#### `tf.contrib.distributions.Distribution.prob(value, name='prob', **condition_kwargs)` {#Distribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -550,6 +558,7 @@ 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: @@ -560,7 +569,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample')` {#Distribution.sample} +#### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Distribution.sample} Generate samples of the specified shape. @@ -573,6 +582,7 @@ 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: @@ -582,7 +592,7 @@ sample. - - - -#### `tf.contrib.distributions.Distribution.sample_n(n, seed=None, name='sample_n')` {#Distribution.sample_n} +#### `tf.contrib.distributions.Distribution.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Distribution.sample_n} Generate `n` samples. @@ -593,6 +603,7 @@ Generate `n` samples. observations to sample. * <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: @@ -614,7 +625,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Distribution.survival_function(value, name='survival_function')` {#Distribution.survival_function} +#### `tf.contrib.distributions.Distribution.survival_function(value, name='survival_function', **condition_kwargs)` {#Distribution.survival_function} Survival function. @@ -631,6 +642,7 @@ 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: @@ -797,7 +809,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Binomial.cdf(value, name='cdf')` {#Binomial.cdf} +#### `tf.contrib.distributions.Binomial.cdf(value, name='cdf', **condition_kwargs)` {#Binomial.cdf} Cumulative distribution function. @@ -812,6 +824,7 @@ 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: @@ -831,7 +844,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Binomial.entropy(name='entropy')` {#Binomial.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -895,7 +908,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf')` {#Binomial.log_cdf} +#### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Binomial.log_cdf} Log cumulative distribution function. @@ -914,6 +927,7 @@ 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: @@ -924,7 +938,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf')` {#Binomial.log_pdf} +#### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Binomial.log_pdf} Log probability density function. @@ -933,6 +947,7 @@ 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: @@ -948,7 +963,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf')` {#Binomial.log_pmf} +#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Binomial.log_pmf} Log probability mass function. @@ -957,6 +972,7 @@ 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: @@ -972,7 +988,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob')` {#Binomial.log_prob} +#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob', **condition_kwargs)` {#Binomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -994,6 +1010,7 @@ 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: @@ -1004,7 +1021,7 @@ values. - - - -#### `tf.contrib.distributions.Binomial.log_survival_function(value, name='log_survival_function')` {#Binomial.log_survival_function} +#### `tf.contrib.distributions.Binomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Binomial.log_survival_function} Log survival function. @@ -1024,6 +1041,7 @@ 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: @@ -1130,7 +1148,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf')` {#Binomial.pdf} +#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf', **condition_kwargs)` {#Binomial.pdf} Probability density function. @@ -1139,6 +1157,7 @@ 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: @@ -1154,7 +1173,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf')` {#Binomial.pmf} +#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf', **condition_kwargs)` {#Binomial.pmf} Probability mass function. @@ -1163,6 +1182,7 @@ 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: @@ -1178,7 +1198,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Binomial.prob(value, name='prob')` {#Binomial.prob} +#### `tf.contrib.distributions.Binomial.prob(value, name='prob', **condition_kwargs)` {#Binomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -1200,6 +1220,7 @@ 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: @@ -1210,7 +1231,7 @@ values. - - - -#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample')` {#Binomial.sample} +#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Binomial.sample} Generate samples of the specified shape. @@ -1223,6 +1244,7 @@ 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: @@ -1232,7 +1254,7 @@ sample. - - - -#### `tf.contrib.distributions.Binomial.sample_n(n, seed=None, name='sample_n')` {#Binomial.sample_n} +#### `tf.contrib.distributions.Binomial.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Binomial.sample_n} Generate `n` samples. @@ -1243,6 +1265,7 @@ Generate `n` samples. observations to sample. * <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: @@ -1264,7 +1287,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Binomial.survival_function(value, name='survival_function')` {#Binomial.survival_function} +#### `tf.contrib.distributions.Binomial.survival_function(value, name='survival_function', **condition_kwargs)` {#Binomial.survival_function} Survival function. @@ -1281,6 +1304,7 @@ 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: @@ -1387,7 +1411,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Bernoulli.cdf(value, name='cdf')` {#Bernoulli.cdf} +#### `tf.contrib.distributions.Bernoulli.cdf(value, name='cdf', **condition_kwargs)` {#Bernoulli.cdf} Cumulative distribution function. @@ -1402,6 +1426,7 @@ 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: @@ -1421,7 +1446,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Bernoulli.entropy(name='entropy')` {#Bernoulli.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -1485,7 +1510,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf')` {#Bernoulli.log_cdf} +#### `tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Bernoulli.log_cdf} Log cumulative distribution function. @@ -1504,6 +1529,7 @@ 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: @@ -1514,7 +1540,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf')` {#Bernoulli.log_pdf} +#### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Bernoulli.log_pdf} Log probability density function. @@ -1523,6 +1549,7 @@ 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: @@ -1538,7 +1565,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf')` {#Bernoulli.log_pmf} +#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Bernoulli.log_pmf} Log probability mass function. @@ -1547,6 +1574,7 @@ 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: @@ -1562,7 +1590,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob')` {#Bernoulli.log_prob} +#### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob', **condition_kwargs)` {#Bernoulli.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -1571,6 +1599,7 @@ 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: @@ -1581,7 +1610,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Bernoulli.log_survival_function(value, name='log_survival_function')` {#Bernoulli.log_survival_function} +#### `tf.contrib.distributions.Bernoulli.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Bernoulli.log_survival_function} Log survival function. @@ -1601,6 +1630,7 @@ 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: @@ -1698,7 +1728,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf')` {#Bernoulli.pdf} +#### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf', **condition_kwargs)` {#Bernoulli.pdf} Probability density function. @@ -1707,6 +1737,7 @@ 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: @@ -1722,7 +1753,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf')` {#Bernoulli.pmf} +#### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf', **condition_kwargs)` {#Bernoulli.pmf} Probability mass function. @@ -1731,6 +1762,7 @@ 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: @@ -1746,7 +1778,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Bernoulli.prob(value, name='prob')` {#Bernoulli.prob} +#### `tf.contrib.distributions.Bernoulli.prob(value, name='prob', **condition_kwargs)` {#Bernoulli.prob} Probability density/mass function (depending on `is_continuous`). @@ -1755,6 +1787,7 @@ 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: @@ -1772,7 +1805,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample')` {#Bernoulli.sample} +#### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Bernoulli.sample} Generate samples of the specified shape. @@ -1785,6 +1818,7 @@ 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: @@ -1794,7 +1828,7 @@ sample. - - - -#### `tf.contrib.distributions.Bernoulli.sample_n(n, seed=None, name='sample_n')` {#Bernoulli.sample_n} +#### `tf.contrib.distributions.Bernoulli.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Bernoulli.sample_n} Generate `n` samples. @@ -1805,6 +1839,7 @@ Generate `n` samples. observations to sample. * <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: @@ -1826,7 +1861,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Bernoulli.survival_function(value, name='survival_function')` {#Bernoulli.survival_function} +#### `tf.contrib.distributions.Bernoulli.survival_function(value, name='survival_function', **condition_kwargs)` {#Bernoulli.survival_function} Survival function. @@ -1843,6 +1878,7 @@ 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: @@ -1920,7 +1956,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.cdf(value, name='cdf')` {#BernoulliWithSigmoidP.cdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.cdf(value, name='cdf', **condition_kwargs)` {#BernoulliWithSigmoidP.cdf} Cumulative distribution function. @@ -1935,6 +1971,7 @@ 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: @@ -1954,7 +1991,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.BernoulliWithSigmoidP.entropy(name='entropy')` {#BernoulliWithSigmoidP.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -2018,7 +2055,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf')` {#BernoulliWithSigmoidP.log_cdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_cdf} Log cumulative distribution function. @@ -2037,6 +2074,7 @@ 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: @@ -2047,7 +2085,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf')` {#BernoulliWithSigmoidP.log_pdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_pdf} Log probability density function. @@ -2056,6 +2094,7 @@ 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: @@ -2071,7 +2110,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf')` {#BernoulliWithSigmoidP.log_pmf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_pmf} Log probability mass function. @@ -2080,6 +2119,7 @@ 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: @@ -2095,7 +2135,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob')` {#BernoulliWithSigmoidP.log_prob} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob', **condition_kwargs)` {#BernoulliWithSigmoidP.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -2104,6 +2144,7 @@ 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: @@ -2114,7 +2155,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function')` {#BernoulliWithSigmoidP.log_survival_function} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#BernoulliWithSigmoidP.log_survival_function} Log survival function. @@ -2134,6 +2175,7 @@ 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: @@ -2231,7 +2273,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf')` {#BernoulliWithSigmoidP.pdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf', **condition_kwargs)` {#BernoulliWithSigmoidP.pdf} Probability density function. @@ -2240,6 +2282,7 @@ 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: @@ -2255,7 +2298,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf')` {#BernoulliWithSigmoidP.pmf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf', **condition_kwargs)` {#BernoulliWithSigmoidP.pmf} Probability mass function. @@ -2264,6 +2307,7 @@ 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: @@ -2279,7 +2323,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob')` {#BernoulliWithSigmoidP.prob} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob', **condition_kwargs)` {#BernoulliWithSigmoidP.prob} Probability density/mass function (depending on `is_continuous`). @@ -2288,6 +2332,7 @@ 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: @@ -2305,7 +2350,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample')` {#BernoulliWithSigmoidP.sample} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#BernoulliWithSigmoidP.sample} Generate samples of the specified shape. @@ -2318,6 +2363,7 @@ 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: @@ -2327,7 +2373,7 @@ sample. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample_n(n, seed=None, name='sample_n')` {#BernoulliWithSigmoidP.sample_n} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#BernoulliWithSigmoidP.sample_n} Generate `n` samples. @@ -2338,6 +2384,7 @@ Generate `n` samples. observations to sample. * <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: @@ -2359,7 +2406,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function')` {#BernoulliWithSigmoidP.survival_function} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function', **condition_kwargs)` {#BernoulliWithSigmoidP.survival_function} Survival function. @@ -2376,6 +2423,7 @@ 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: @@ -2563,7 +2611,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Beta.cdf(value, name='cdf')` {#Beta.cdf} +#### `tf.contrib.distributions.Beta.cdf(value, name='cdf', **condition_kwargs)` {#Beta.cdf} Cumulative distribution function. @@ -2578,6 +2626,7 @@ 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: @@ -2597,7 +2646,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Beta.entropy(name='entropy')` {#Beta.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -2661,7 +2710,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf')` {#Beta.log_cdf} +#### `tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Beta.log_cdf} Log cumulative distribution function. @@ -2688,6 +2737,7 @@ 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: @@ -2698,7 +2748,7 @@ 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')` {#Beta.log_pdf} +#### `tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Beta.log_pdf} Log probability density function. @@ -2707,6 +2757,7 @@ 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: @@ -2722,7 +2773,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf')` {#Beta.log_pmf} +#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Beta.log_pmf} Log probability mass function. @@ -2731,6 +2782,7 @@ 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: @@ -2746,7 +2798,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob')` {#Beta.log_prob} +#### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob', **condition_kwargs)` {#Beta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -2755,6 +2807,7 @@ 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: @@ -2765,7 +2818,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Beta.log_survival_function(value, name='log_survival_function')` {#Beta.log_survival_function} +#### `tf.contrib.distributions.Beta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Beta.log_survival_function} Log survival function. @@ -2785,6 +2838,7 @@ 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: @@ -2871,7 +2925,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Beta.pdf(value, name='pdf')` {#Beta.pdf} +#### `tf.contrib.distributions.Beta.pdf(value, name='pdf', **condition_kwargs)` {#Beta.pdf} Probability density function. @@ -2880,6 +2934,7 @@ 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: @@ -2895,7 +2950,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Beta.pmf(value, name='pmf')` {#Beta.pmf} +#### `tf.contrib.distributions.Beta.pmf(value, name='pmf', **condition_kwargs)` {#Beta.pmf} Probability mass function. @@ -2904,6 +2959,7 @@ 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: @@ -2919,7 +2975,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Beta.prob(value, name='prob')` {#Beta.prob} +#### `tf.contrib.distributions.Beta.prob(value, name='prob', **condition_kwargs)` {#Beta.prob} Probability density/mass function (depending on `is_continuous`). @@ -2936,6 +2992,7 @@ 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: @@ -2946,7 +3003,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample')` {#Beta.sample} +#### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Beta.sample} Generate samples of the specified shape. @@ -2959,6 +3016,7 @@ 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: @@ -2968,7 +3026,7 @@ sample. - - - -#### `tf.contrib.distributions.Beta.sample_n(n, seed=None, name='sample_n')` {#Beta.sample_n} +#### `tf.contrib.distributions.Beta.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Beta.sample_n} Generate `n` samples. @@ -2979,6 +3037,7 @@ Generate `n` samples. observations to sample. * <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: @@ -3000,7 +3059,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Beta.survival_function(value, name='survival_function')` {#Beta.survival_function} +#### `tf.contrib.distributions.Beta.survival_function(value, name='survival_function', **condition_kwargs)` {#Beta.survival_function} Survival function. @@ -3017,6 +3076,7 @@ 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: @@ -3115,7 +3175,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.cdf(value, name='cdf')` {#BetaWithSoftplusAB.cdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.cdf(value, name='cdf', **condition_kwargs)` {#BetaWithSoftplusAB.cdf} Cumulative distribution function. @@ -3130,6 +3190,7 @@ 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: @@ -3149,7 +3210,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.BetaWithSoftplusAB.entropy(name='entropy')` {#BetaWithSoftplusAB.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -3213,7 +3274,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf')` {#BetaWithSoftplusAB.log_cdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf', **condition_kwargs)` {#BetaWithSoftplusAB.log_cdf} Log cumulative distribution function. @@ -3240,6 +3301,7 @@ 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: @@ -3250,7 +3312,7 @@ 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')` {#BetaWithSoftplusAB.log_pdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pdf(value, name='log_pdf', **condition_kwargs)` {#BetaWithSoftplusAB.log_pdf} Log probability density function. @@ -3259,6 +3321,7 @@ 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: @@ -3274,7 +3337,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf')` {#BetaWithSoftplusAB.log_pmf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf', **condition_kwargs)` {#BetaWithSoftplusAB.log_pmf} Log probability mass function. @@ -3283,6 +3346,7 @@ 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: @@ -3298,7 +3362,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob')` {#BetaWithSoftplusAB.log_prob} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob', **condition_kwargs)` {#BetaWithSoftplusAB.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -3307,6 +3371,7 @@ 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: @@ -3317,7 +3382,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function')` {#BetaWithSoftplusAB.log_survival_function} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#BetaWithSoftplusAB.log_survival_function} Log survival function. @@ -3337,6 +3402,7 @@ 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: @@ -3423,7 +3489,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf')` {#BetaWithSoftplusAB.pdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf', **condition_kwargs)` {#BetaWithSoftplusAB.pdf} Probability density function. @@ -3432,6 +3498,7 @@ 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: @@ -3447,7 +3514,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf')` {#BetaWithSoftplusAB.pmf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf', **condition_kwargs)` {#BetaWithSoftplusAB.pmf} Probability mass function. @@ -3456,6 +3523,7 @@ 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: @@ -3471,7 +3539,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob')` {#BetaWithSoftplusAB.prob} +#### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob', **condition_kwargs)` {#BetaWithSoftplusAB.prob} Probability density/mass function (depending on `is_continuous`). @@ -3488,6 +3556,7 @@ 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: @@ -3498,7 +3567,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample')` {#BetaWithSoftplusAB.sample} +#### `tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#BetaWithSoftplusAB.sample} Generate samples of the specified shape. @@ -3511,6 +3580,7 @@ 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: @@ -3520,7 +3590,7 @@ sample. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.sample_n(n, seed=None, name='sample_n')` {#BetaWithSoftplusAB.sample_n} +#### `tf.contrib.distributions.BetaWithSoftplusAB.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#BetaWithSoftplusAB.sample_n} Generate `n` samples. @@ -3531,6 +3601,7 @@ Generate `n` samples. observations to sample. * <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: @@ -3552,7 +3623,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function')` {#BetaWithSoftplusAB.survival_function} +#### `tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function', **condition_kwargs)` {#BetaWithSoftplusAB.survival_function} Survival function. @@ -3569,6 +3640,7 @@ 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: @@ -3706,7 +3778,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Categorical.cdf(value, name='cdf')` {#Categorical.cdf} +#### `tf.contrib.distributions.Categorical.cdf(value, name='cdf', **condition_kwargs)` {#Categorical.cdf} Cumulative distribution function. @@ -3721,6 +3793,7 @@ 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: @@ -3740,7 +3813,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Categorical.entropy(name='entropy')` {#Categorical.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -3804,7 +3877,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf')` {#Categorical.log_cdf} +#### `tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Categorical.log_cdf} Log cumulative distribution function. @@ -3823,6 +3896,7 @@ 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: @@ -3833,7 +3907,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf')` {#Categorical.log_pdf} +#### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Categorical.log_pdf} Log probability density function. @@ -3842,6 +3916,7 @@ 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: @@ -3857,7 +3932,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf')` {#Categorical.log_pmf} +#### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Categorical.log_pmf} Log probability mass function. @@ -3866,6 +3941,7 @@ 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: @@ -3881,7 +3957,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob')` {#Categorical.log_prob} +#### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob', **condition_kwargs)` {#Categorical.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -3890,6 +3966,7 @@ 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: @@ -3900,7 +3977,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Categorical.log_survival_function(value, name='log_survival_function')` {#Categorical.log_survival_function} +#### `tf.contrib.distributions.Categorical.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Categorical.log_survival_function} Log survival function. @@ -3920,6 +3997,7 @@ 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: @@ -4022,7 +4100,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Categorical.pdf(value, name='pdf')` {#Categorical.pdf} +#### `tf.contrib.distributions.Categorical.pdf(value, name='pdf', **condition_kwargs)` {#Categorical.pdf} Probability density function. @@ -4031,6 +4109,7 @@ 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: @@ -4046,7 +4125,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Categorical.pmf(value, name='pmf')` {#Categorical.pmf} +#### `tf.contrib.distributions.Categorical.pmf(value, name='pmf', **condition_kwargs)` {#Categorical.pmf} Probability mass function. @@ -4055,6 +4134,7 @@ 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: @@ -4070,7 +4150,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Categorical.prob(value, name='prob')` {#Categorical.prob} +#### `tf.contrib.distributions.Categorical.prob(value, name='prob', **condition_kwargs)` {#Categorical.prob} Probability density/mass function (depending on `is_continuous`). @@ -4079,6 +4159,7 @@ 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: @@ -4089,7 +4170,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample')` {#Categorical.sample} +#### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Categorical.sample} Generate samples of the specified shape. @@ -4102,6 +4183,7 @@ 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: @@ -4111,7 +4193,7 @@ sample. - - - -#### `tf.contrib.distributions.Categorical.sample_n(n, seed=None, name='sample_n')` {#Categorical.sample_n} +#### `tf.contrib.distributions.Categorical.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Categorical.sample_n} Generate `n` samples. @@ -4122,6 +4204,7 @@ Generate `n` samples. observations to sample. * <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: @@ -4143,7 +4226,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Categorical.survival_function(value, name='survival_function')` {#Categorical.survival_function} +#### `tf.contrib.distributions.Categorical.survival_function(value, name='survival_function', **condition_kwargs)` {#Categorical.survival_function} Survival function. @@ -4160,6 +4243,7 @@ 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: @@ -4273,7 +4357,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Chi2.cdf(value, name='cdf')` {#Chi2.cdf} +#### `tf.contrib.distributions.Chi2.cdf(value, name='cdf', **condition_kwargs)` {#Chi2.cdf} Cumulative distribution function. @@ -4288,6 +4372,7 @@ 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: @@ -4314,7 +4399,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Chi2.entropy(name='entropy')` {#Chi2.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -4389,7 +4474,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Chi2.log_cdf(value, name='log_cdf')` {#Chi2.log_cdf} +#### `tf.contrib.distributions.Chi2.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Chi2.log_cdf} Log cumulative distribution function. @@ -4408,6 +4493,7 @@ 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: @@ -4418,7 +4504,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf')` {#Chi2.log_pdf} +#### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Chi2.log_pdf} Log probability density function. @@ -4427,6 +4513,7 @@ 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: @@ -4442,7 +4529,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf')` {#Chi2.log_pmf} +#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Chi2.log_pmf} Log probability mass function. @@ -4451,6 +4538,7 @@ 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: @@ -4466,7 +4554,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob')` {#Chi2.log_prob} +#### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob', **condition_kwargs)` {#Chi2.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -4475,6 +4563,7 @@ 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: @@ -4485,7 +4574,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2.log_survival_function(value, name='log_survival_function')` {#Chi2.log_survival_function} +#### `tf.contrib.distributions.Chi2.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Chi2.log_survival_function} Log survival function. @@ -4505,6 +4594,7 @@ 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: @@ -4590,7 +4680,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf')` {#Chi2.pdf} +#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf', **condition_kwargs)` {#Chi2.pdf} Probability density function. @@ -4599,6 +4689,7 @@ 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: @@ -4614,7 +4705,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf')` {#Chi2.pmf} +#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf', **condition_kwargs)` {#Chi2.pmf} Probability mass function. @@ -4623,6 +4714,7 @@ 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: @@ -4638,7 +4730,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Chi2.prob(value, name='prob')` {#Chi2.prob} +#### `tf.contrib.distributions.Chi2.prob(value, name='prob', **condition_kwargs)` {#Chi2.prob} Probability density/mass function (depending on `is_continuous`). @@ -4647,6 +4739,7 @@ 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: @@ -4657,7 +4750,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample')` {#Chi2.sample} +#### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Chi2.sample} Generate samples of the specified shape. @@ -4670,6 +4763,7 @@ 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: @@ -4679,7 +4773,7 @@ sample. - - - -#### `tf.contrib.distributions.Chi2.sample_n(n, seed=None, name='sample_n')` {#Chi2.sample_n} +#### `tf.contrib.distributions.Chi2.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Chi2.sample_n} Generate `n` samples. @@ -4695,6 +4789,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -4716,7 +4811,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Chi2.survival_function(value, name='survival_function')` {#Chi2.survival_function} +#### `tf.contrib.distributions.Chi2.survival_function(value, name='survival_function', **condition_kwargs)` {#Chi2.survival_function} Survival function. @@ -4733,6 +4828,7 @@ 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: @@ -4824,7 +4920,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.cdf(value, name='cdf')` {#Chi2WithAbsDf.cdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.cdf(value, name='cdf', **condition_kwargs)` {#Chi2WithAbsDf.cdf} Cumulative distribution function. @@ -4839,6 +4935,7 @@ 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: @@ -4865,7 +4962,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Chi2WithAbsDf.entropy(name='entropy')` {#Chi2WithAbsDf.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -4940,7 +5037,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_cdf(value, name='log_cdf')` {#Chi2WithAbsDf.log_cdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Chi2WithAbsDf.log_cdf} Log cumulative distribution function. @@ -4959,6 +5056,7 @@ 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: @@ -4969,7 +5067,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf')` {#Chi2WithAbsDf.log_pdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Chi2WithAbsDf.log_pdf} Log probability density function. @@ -4978,6 +5076,7 @@ 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: @@ -4993,7 +5092,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf')` {#Chi2WithAbsDf.log_pmf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Chi2WithAbsDf.log_pmf} Log probability mass function. @@ -5002,6 +5101,7 @@ 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: @@ -5017,7 +5117,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob')` {#Chi2WithAbsDf.log_prob} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob', **condition_kwargs)` {#Chi2WithAbsDf.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -5026,6 +5126,7 @@ 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: @@ -5036,7 +5137,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_survival_function(value, name='log_survival_function')` {#Chi2WithAbsDf.log_survival_function} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Chi2WithAbsDf.log_survival_function} Log survival function. @@ -5056,6 +5157,7 @@ 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: @@ -5141,7 +5243,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf')` {#Chi2WithAbsDf.pdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf', **condition_kwargs)` {#Chi2WithAbsDf.pdf} Probability density function. @@ -5150,6 +5252,7 @@ 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: @@ -5165,7 +5268,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf')` {#Chi2WithAbsDf.pmf} +#### `tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf', **condition_kwargs)` {#Chi2WithAbsDf.pmf} Probability mass function. @@ -5174,6 +5277,7 @@ 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: @@ -5189,7 +5293,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob')` {#Chi2WithAbsDf.prob} +#### `tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob', **condition_kwargs)` {#Chi2WithAbsDf.prob} Probability density/mass function (depending on `is_continuous`). @@ -5198,6 +5302,7 @@ 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: @@ -5208,7 +5313,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample')` {#Chi2WithAbsDf.sample} +#### `tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Chi2WithAbsDf.sample} Generate samples of the specified shape. @@ -5221,6 +5326,7 @@ 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: @@ -5230,7 +5336,7 @@ sample. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.sample_n(n, seed=None, name='sample_n')` {#Chi2WithAbsDf.sample_n} +#### `tf.contrib.distributions.Chi2WithAbsDf.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Chi2WithAbsDf.sample_n} Generate `n` samples. @@ -5246,6 +5352,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -5267,7 +5374,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function')` {#Chi2WithAbsDf.survival_function} +#### `tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function', **condition_kwargs)` {#Chi2WithAbsDf.survival_function} Survival function. @@ -5284,6 +5391,7 @@ 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: @@ -5397,7 +5505,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Exponential.cdf(value, name='cdf')` {#Exponential.cdf} +#### `tf.contrib.distributions.Exponential.cdf(value, name='cdf', **condition_kwargs)` {#Exponential.cdf} Cumulative distribution function. @@ -5412,6 +5520,7 @@ 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: @@ -5431,7 +5540,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Exponential.entropy(name='entropy')` {#Exponential.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -5513,7 +5622,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf')` {#Exponential.log_cdf} +#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Exponential.log_cdf} Log cumulative distribution function. @@ -5532,6 +5641,7 @@ 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: @@ -5542,7 +5652,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf')` {#Exponential.log_pdf} +#### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Exponential.log_pdf} Log probability density function. @@ -5551,6 +5661,7 @@ 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: @@ -5566,7 +5677,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')` {#Exponential.log_pmf} +#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Exponential.log_pmf} Log probability mass function. @@ -5575,6 +5686,7 @@ 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: @@ -5590,7 +5702,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob')` {#Exponential.log_prob} +#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob', **condition_kwargs)` {#Exponential.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -5599,6 +5711,7 @@ 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: @@ -5609,7 +5722,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function')` {#Exponential.log_survival_function} +#### `tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Exponential.log_survival_function} Log survival function. @@ -5629,6 +5742,7 @@ 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: @@ -5714,7 +5828,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf')` {#Exponential.pdf} +#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf', **condition_kwargs)` {#Exponential.pdf} Probability density function. @@ -5723,6 +5837,7 @@ 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: @@ -5738,7 +5853,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf')` {#Exponential.pmf} +#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf', **condition_kwargs)` {#Exponential.pmf} Probability mass function. @@ -5747,6 +5862,7 @@ 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: @@ -5762,7 +5878,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Exponential.prob(value, name='prob')` {#Exponential.prob} +#### `tf.contrib.distributions.Exponential.prob(value, name='prob', **condition_kwargs)` {#Exponential.prob} Probability density/mass function (depending on `is_continuous`). @@ -5771,6 +5887,7 @@ 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: @@ -5781,7 +5898,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample')` {#Exponential.sample} +#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Exponential.sample} Generate samples of the specified shape. @@ -5794,6 +5911,7 @@ 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: @@ -5803,7 +5921,7 @@ sample. - - - -#### `tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n')` {#Exponential.sample_n} +#### `tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Exponential.sample_n} Generate `n` samples. @@ -5819,6 +5937,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -5840,7 +5959,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Exponential.survival_function(value, name='survival_function')` {#Exponential.survival_function} +#### `tf.contrib.distributions.Exponential.survival_function(value, name='survival_function', **condition_kwargs)` {#Exponential.survival_function} Survival function. @@ -5857,6 +5976,7 @@ 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: @@ -5948,7 +6068,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.cdf(value, name='cdf')` {#ExponentialWithSoftplusLam.cdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.cdf(value, name='cdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.cdf} Cumulative distribution function. @@ -5963,6 +6083,7 @@ 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: @@ -5982,7 +6103,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.ExponentialWithSoftplusLam.entropy(name='entropy')` {#ExponentialWithSoftplusLam.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -6064,7 +6185,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf')` {#ExponentialWithSoftplusLam.log_cdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_cdf} Log cumulative distribution function. @@ -6083,6 +6204,7 @@ 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: @@ -6093,7 +6215,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf')` {#ExponentialWithSoftplusLam.log_pdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_pdf} Log probability density function. @@ -6102,6 +6224,7 @@ 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: @@ -6117,7 +6240,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf')` {#ExponentialWithSoftplusLam.log_pmf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_pmf} Log probability mass function. @@ -6126,6 +6249,7 @@ 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: @@ -6141,7 +6265,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob')` {#ExponentialWithSoftplusLam.log_prob} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -6150,6 +6274,7 @@ 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: @@ -6160,7 +6285,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_survival_function(value, name='log_survival_function')` {#ExponentialWithSoftplusLam.log_survival_function} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_survival_function} Log survival function. @@ -6180,6 +6305,7 @@ 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: @@ -6265,7 +6391,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf')` {#ExponentialWithSoftplusLam.pdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.pdf} Probability density function. @@ -6274,6 +6400,7 @@ 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: @@ -6289,7 +6416,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf')` {#ExponentialWithSoftplusLam.pmf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf', **condition_kwargs)` {#ExponentialWithSoftplusLam.pmf} Probability mass function. @@ -6298,6 +6425,7 @@ 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: @@ -6313,7 +6441,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob')` {#ExponentialWithSoftplusLam.prob} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob', **condition_kwargs)` {#ExponentialWithSoftplusLam.prob} Probability density/mass function (depending on `is_continuous`). @@ -6322,6 +6450,7 @@ 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: @@ -6332,7 +6461,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample(sample_shape=(), seed=None, name='sample')` {#ExponentialWithSoftplusLam.sample} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#ExponentialWithSoftplusLam.sample} Generate samples of the specified shape. @@ -6345,6 +6474,7 @@ 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: @@ -6354,7 +6484,7 @@ sample. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample_n(n, seed=None, name='sample_n')` {#ExponentialWithSoftplusLam.sample_n} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#ExponentialWithSoftplusLam.sample_n} Generate `n` samples. @@ -6370,6 +6500,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -6391,7 +6522,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.survival_function(value, name='survival_function')` {#ExponentialWithSoftplusLam.survival_function} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.survival_function(value, name='survival_function', **condition_kwargs)` {#ExponentialWithSoftplusLam.survival_function} Survival function. @@ -6408,6 +6539,7 @@ 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: @@ -6548,7 +6680,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.Gamma.cdf(value, name='cdf')` {#Gamma.cdf} +#### `tf.contrib.distributions.Gamma.cdf(value, name='cdf', **condition_kwargs)` {#Gamma.cdf} Cumulative distribution function. @@ -6563,6 +6695,7 @@ 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: @@ -6582,7 +6715,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Gamma.entropy(name='entropy')` {#Gamma.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -6657,7 +6790,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf')` {#Gamma.log_cdf} +#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Gamma.log_cdf} Log cumulative distribution function. @@ -6676,6 +6809,7 @@ 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: @@ -6686,7 +6820,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf')` {#Gamma.log_pdf} +#### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Gamma.log_pdf} Log probability density function. @@ -6695,6 +6829,7 @@ 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: @@ -6710,7 +6845,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')` {#Gamma.log_pmf} +#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Gamma.log_pmf} Log probability mass function. @@ -6719,6 +6854,7 @@ 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: @@ -6734,7 +6870,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob')` {#Gamma.log_prob} +#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob', **condition_kwargs)` {#Gamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -6743,6 +6879,7 @@ 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: @@ -6753,7 +6890,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function')` {#Gamma.log_survival_function} +#### `tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Gamma.log_survival_function} Log survival function. @@ -6773,6 +6910,7 @@ 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: @@ -6858,7 +6996,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf')` {#Gamma.pdf} +#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf', **condition_kwargs)` {#Gamma.pdf} Probability density function. @@ -6867,6 +7005,7 @@ 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: @@ -6882,7 +7021,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf')` {#Gamma.pmf} +#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf', **condition_kwargs)` {#Gamma.pmf} Probability mass function. @@ -6891,6 +7030,7 @@ 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: @@ -6906,7 +7046,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Gamma.prob(value, name='prob')` {#Gamma.prob} +#### `tf.contrib.distributions.Gamma.prob(value, name='prob', **condition_kwargs)` {#Gamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -6915,6 +7055,7 @@ 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: @@ -6925,7 +7066,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample')` {#Gamma.sample} +#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Gamma.sample} Generate samples of the specified shape. @@ -6938,6 +7079,7 @@ 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: @@ -6947,7 +7089,7 @@ sample. - - - -#### `tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n')` {#Gamma.sample_n} +#### `tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Gamma.sample_n} Generate `n` samples. @@ -6963,6 +7105,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -6984,7 +7127,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Gamma.survival_function(value, name='survival_function')` {#Gamma.survival_function} +#### `tf.contrib.distributions.Gamma.survival_function(value, name='survival_function', **condition_kwargs)` {#Gamma.survival_function} Survival function. @@ -7001,6 +7144,7 @@ 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: @@ -7092,7 +7236,7 @@ Inverse scale parameter. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf')` {#GammaWithSoftplusAlphaBeta.cdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.cdf} Cumulative distribution function. @@ -7107,6 +7251,7 @@ 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: @@ -7126,7 +7271,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.entropy(name='entropy')` {#GammaWithSoftplusAlphaBeta.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -7201,7 +7346,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')` {#GammaWithSoftplusAlphaBeta.log_cdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_cdf} Log cumulative distribution function. @@ -7220,6 +7365,7 @@ 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: @@ -7230,7 +7376,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')` {#GammaWithSoftplusAlphaBeta.log_pdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_pdf} Log probability density function. @@ -7239,6 +7385,7 @@ 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: @@ -7254,7 +7401,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#GammaWithSoftplusAlphaBeta.log_pmf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_pmf} Log probability mass function. @@ -7263,6 +7410,7 @@ 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: @@ -7278,7 +7426,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#GammaWithSoftplusAlphaBeta.log_prob} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -7287,6 +7435,7 @@ 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: @@ -7297,7 +7446,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')` {#GammaWithSoftplusAlphaBeta.log_survival_function} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_survival_function} Log survival function. @@ -7317,6 +7466,7 @@ 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: @@ -7402,7 +7552,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#GammaWithSoftplusAlphaBeta.pdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.pdf} Probability density function. @@ -7411,6 +7561,7 @@ 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: @@ -7426,7 +7577,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#GammaWithSoftplusAlphaBeta.pmf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.pmf} Probability mass function. @@ -7435,6 +7586,7 @@ 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: @@ -7450,7 +7602,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#GammaWithSoftplusAlphaBeta.prob} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -7459,6 +7611,7 @@ 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: @@ -7469,7 +7622,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')` {#GammaWithSoftplusAlphaBeta.sample} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.sample} Generate samples of the specified shape. @@ -7482,6 +7635,7 @@ 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: @@ -7491,7 +7645,7 @@ sample. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n')` {#GammaWithSoftplusAlphaBeta.sample_n} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.sample_n} Generate `n` samples. @@ -7507,6 +7661,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -7528,7 +7683,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')` {#GammaWithSoftplusAlphaBeta.survival_function} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.survival_function} Survival function. @@ -7545,6 +7700,7 @@ 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: @@ -7681,7 +7837,7 @@ Scale parameter. - - - -#### `tf.contrib.distributions.InverseGamma.cdf(value, name='cdf')` {#InverseGamma.cdf} +#### `tf.contrib.distributions.InverseGamma.cdf(value, name='cdf', **condition_kwargs)` {#InverseGamma.cdf} Cumulative distribution function. @@ -7696,6 +7852,7 @@ 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: @@ -7715,7 +7872,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.InverseGamma.entropy(name='entropy')` {#InverseGamma.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `InverseGamma`: @@ -7790,7 +7947,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf')` {#InverseGamma.log_cdf} +#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#InverseGamma.log_cdf} Log cumulative distribution function. @@ -7809,6 +7966,7 @@ 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: @@ -7819,7 +7977,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf')` {#InverseGamma.log_pdf} +#### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#InverseGamma.log_pdf} Log probability density function. @@ -7828,6 +7986,7 @@ 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: @@ -7843,7 +8002,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')` {#InverseGamma.log_pmf} +#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#InverseGamma.log_pmf} Log probability mass function. @@ -7852,6 +8011,7 @@ 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: @@ -7867,7 +8027,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob')` {#InverseGamma.log_prob} +#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob', **condition_kwargs)` {#InverseGamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -7876,6 +8036,7 @@ 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: @@ -7886,7 +8047,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function')` {#InverseGamma.log_survival_function} +#### `tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#InverseGamma.log_survival_function} Log survival function. @@ -7906,6 +8067,7 @@ 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: @@ -7995,7 +8157,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')` {#InverseGamma.pdf} +#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf', **condition_kwargs)` {#InverseGamma.pdf} Probability density function. @@ -8004,6 +8166,7 @@ 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: @@ -8019,7 +8182,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')` {#InverseGamma.pmf} +#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf', **condition_kwargs)` {#InverseGamma.pmf} Probability mass function. @@ -8028,6 +8191,7 @@ 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: @@ -8043,7 +8207,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob')` {#InverseGamma.prob} +#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob', **condition_kwargs)` {#InverseGamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -8052,6 +8216,7 @@ 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: @@ -8062,7 +8227,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample')` {#InverseGamma.sample} +#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#InverseGamma.sample} Generate samples of the specified shape. @@ -8075,6 +8240,7 @@ 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: @@ -8084,7 +8250,7 @@ sample. - - - -#### `tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n')` {#InverseGamma.sample_n} +#### `tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#InverseGamma.sample_n} Generate `n` samples. @@ -8100,6 +8266,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -8121,7 +8288,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function')` {#InverseGamma.survival_function} +#### `tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function', **condition_kwargs)` {#InverseGamma.survival_function} Survival function. @@ -8138,6 +8305,7 @@ 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: @@ -8235,7 +8403,7 @@ Scale parameter. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf')` {#InverseGammaWithSoftplusAlphaBeta.cdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.cdf} Cumulative distribution function. @@ -8250,6 +8418,7 @@ 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: @@ -8269,7 +8438,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.entropy(name='entropy')` {#InverseGammaWithSoftplusAlphaBeta.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `InverseGamma`: @@ -8344,7 +8513,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')` {#InverseGammaWithSoftplusAlphaBeta.log_cdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_cdf} Log cumulative distribution function. @@ -8363,6 +8532,7 @@ 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: @@ -8373,7 +8543,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')` {#InverseGammaWithSoftplusAlphaBeta.log_pdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_pdf} Log probability density function. @@ -8382,6 +8552,7 @@ 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: @@ -8397,7 +8568,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} Log probability mass function. @@ -8406,6 +8577,7 @@ 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: @@ -8421,7 +8593,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#InverseGammaWithSoftplusAlphaBeta.log_prob} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -8430,6 +8602,7 @@ 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: @@ -8440,7 +8613,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')` {#InverseGammaWithSoftplusAlphaBeta.log_survival_function} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_survival_function} Log survival function. @@ -8460,6 +8633,7 @@ 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: @@ -8549,7 +8723,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#InverseGammaWithSoftplusAlphaBeta.pdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.pdf} Probability density function. @@ -8558,6 +8732,7 @@ 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: @@ -8573,7 +8748,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#InverseGammaWithSoftplusAlphaBeta.pmf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.pmf} Probability mass function. @@ -8582,6 +8757,7 @@ 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: @@ -8597,7 +8773,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#InverseGammaWithSoftplusAlphaBeta.prob} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -8606,6 +8782,7 @@ 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: @@ -8616,7 +8793,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')` {#InverseGammaWithSoftplusAlphaBeta.sample} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.sample} Generate samples of the specified shape. @@ -8629,6 +8806,7 @@ 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: @@ -8638,7 +8816,7 @@ sample. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n')` {#InverseGammaWithSoftplusAlphaBeta.sample_n} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.sample_n} Generate `n` samples. @@ -8654,6 +8832,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -8675,7 +8854,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')` {#InverseGammaWithSoftplusAlphaBeta.survival_function} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.survival_function} Survival function. @@ -8692,6 +8871,7 @@ 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: @@ -8808,7 +8988,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Laplace.cdf(value, name='cdf')` {#Laplace.cdf} +#### `tf.contrib.distributions.Laplace.cdf(value, name='cdf', **condition_kwargs)` {#Laplace.cdf} Cumulative distribution function. @@ -8823,6 +9003,7 @@ 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: @@ -8842,7 +9023,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Laplace.entropy(name='entropy')` {#Laplace.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -8913,7 +9094,7 @@ Distribution parameter for the location. - - - -#### `tf.contrib.distributions.Laplace.log_cdf(value, name='log_cdf')` {#Laplace.log_cdf} +#### `tf.contrib.distributions.Laplace.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Laplace.log_cdf} Log cumulative distribution function. @@ -8932,6 +9113,7 @@ 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: @@ -8942,7 +9124,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf')` {#Laplace.log_pdf} +#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Laplace.log_pdf} Log probability density function. @@ -8951,6 +9133,7 @@ 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: @@ -8966,7 +9149,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf} +#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Laplace.log_pmf} Log probability mass function. @@ -8975,6 +9158,7 @@ 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: @@ -8990,7 +9174,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob')` {#Laplace.log_prob} +#### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob', **condition_kwargs)` {#Laplace.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -8999,6 +9183,7 @@ 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: @@ -9009,7 +9194,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Laplace.log_survival_function(value, name='log_survival_function')` {#Laplace.log_survival_function} +#### `tf.contrib.distributions.Laplace.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Laplace.log_survival_function} Log survival function. @@ -9029,6 +9214,7 @@ 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: @@ -9108,7 +9294,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf')` {#Laplace.pdf} +#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf', **condition_kwargs)` {#Laplace.pdf} Probability density function. @@ -9117,6 +9303,7 @@ 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: @@ -9132,7 +9319,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf')` {#Laplace.pmf} +#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf', **condition_kwargs)` {#Laplace.pmf} Probability mass function. @@ -9141,6 +9328,7 @@ 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: @@ -9156,7 +9344,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Laplace.prob(value, name='prob')` {#Laplace.prob} +#### `tf.contrib.distributions.Laplace.prob(value, name='prob', **condition_kwargs)` {#Laplace.prob} Probability density/mass function (depending on `is_continuous`). @@ -9165,6 +9353,7 @@ 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: @@ -9175,7 +9364,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample')` {#Laplace.sample} +#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Laplace.sample} Generate samples of the specified shape. @@ -9188,6 +9377,7 @@ 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: @@ -9197,7 +9387,7 @@ sample. - - - -#### `tf.contrib.distributions.Laplace.sample_n(n, seed=None, name='sample_n')` {#Laplace.sample_n} +#### `tf.contrib.distributions.Laplace.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Laplace.sample_n} Generate `n` samples. @@ -9208,6 +9398,7 @@ Generate `n` samples. observations to sample. * <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: @@ -9236,7 +9427,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Laplace.survival_function(value, name='survival_function')` {#Laplace.survival_function} +#### `tf.contrib.distributions.Laplace.survival_function(value, name='survival_function', **condition_kwargs)` {#Laplace.survival_function} Survival function. @@ -9253,6 +9444,7 @@ 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: @@ -9330,7 +9522,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.cdf(value, name='cdf')` {#LaplaceWithSoftplusScale.cdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.cdf(value, name='cdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.cdf} Cumulative distribution function. @@ -9345,6 +9537,7 @@ 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: @@ -9364,7 +9557,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.LaplaceWithSoftplusScale.entropy(name='entropy')` {#LaplaceWithSoftplusScale.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -9435,7 +9628,7 @@ Distribution parameter for the location. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_cdf(value, name='log_cdf')` {#LaplaceWithSoftplusScale.log_cdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_cdf(value, name='log_cdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_cdf} Log cumulative distribution function. @@ -9454,6 +9647,7 @@ 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: @@ -9464,7 +9658,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf')` {#LaplaceWithSoftplusScale.log_pdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_pdf} Log probability density function. @@ -9473,6 +9667,7 @@ 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: @@ -9488,7 +9683,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf')` {#LaplaceWithSoftplusScale.log_pmf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_pmf} Log probability mass function. @@ -9497,6 +9692,7 @@ 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: @@ -9512,7 +9708,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob')` {#LaplaceWithSoftplusScale.log_prob} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -9521,6 +9717,7 @@ 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: @@ -9531,7 +9728,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_survival_function(value, name='log_survival_function')` {#LaplaceWithSoftplusScale.log_survival_function} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_survival_function} Log survival function. @@ -9551,6 +9748,7 @@ 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: @@ -9630,7 +9828,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf')` {#LaplaceWithSoftplusScale.pdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.pdf} Probability density function. @@ -9639,6 +9837,7 @@ 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: @@ -9654,7 +9853,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf')` {#LaplaceWithSoftplusScale.pmf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf', **condition_kwargs)` {#LaplaceWithSoftplusScale.pmf} Probability mass function. @@ -9663,6 +9862,7 @@ 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: @@ -9678,7 +9878,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob')` {#LaplaceWithSoftplusScale.prob} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob', **condition_kwargs)` {#LaplaceWithSoftplusScale.prob} Probability density/mass function (depending on `is_continuous`). @@ -9687,6 +9887,7 @@ 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: @@ -9697,7 +9898,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample(sample_shape=(), seed=None, name='sample')` {#LaplaceWithSoftplusScale.sample} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#LaplaceWithSoftplusScale.sample} Generate samples of the specified shape. @@ -9710,6 +9911,7 @@ 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: @@ -9719,7 +9921,7 @@ sample. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample_n(n, seed=None, name='sample_n')` {#LaplaceWithSoftplusScale.sample_n} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#LaplaceWithSoftplusScale.sample_n} Generate `n` samples. @@ -9730,6 +9932,7 @@ Generate `n` samples. observations to sample. * <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: @@ -9758,7 +9961,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.survival_function(value, name='survival_function')` {#LaplaceWithSoftplusScale.survival_function} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.survival_function(value, name='survival_function', **condition_kwargs)` {#LaplaceWithSoftplusScale.survival_function} Survival function. @@ -9775,6 +9978,7 @@ 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: @@ -9916,7 +10120,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Normal.cdf(value, name='cdf')` {#Normal.cdf} +#### `tf.contrib.distributions.Normal.cdf(value, name='cdf', **condition_kwargs)` {#Normal.cdf} Cumulative distribution function. @@ -9931,6 +10135,7 @@ 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: @@ -9950,7 +10155,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Normal.entropy(name='entropy')` {#Normal.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -10014,7 +10219,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf')` {#Normal.log_cdf} +#### `tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Normal.log_cdf} Log cumulative distribution function. @@ -10033,6 +10238,7 @@ 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: @@ -10043,7 +10249,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf')` {#Normal.log_pdf} +#### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Normal.log_pdf} Log probability density function. @@ -10052,6 +10258,7 @@ 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: @@ -10067,7 +10274,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf')` {#Normal.log_pmf} +#### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Normal.log_pmf} Log probability mass function. @@ -10076,6 +10283,7 @@ 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: @@ -10091,7 +10299,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob')` {#Normal.log_prob} +#### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob', **condition_kwargs)` {#Normal.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -10100,6 +10308,7 @@ 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: @@ -10110,7 +10319,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Normal.log_survival_function(value, name='log_survival_function')` {#Normal.log_survival_function} +#### `tf.contrib.distributions.Normal.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Normal.log_survival_function} Log survival function. @@ -10130,6 +10339,7 @@ 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: @@ -10216,7 +10426,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Normal.pdf(value, name='pdf')` {#Normal.pdf} +#### `tf.contrib.distributions.Normal.pdf(value, name='pdf', **condition_kwargs)` {#Normal.pdf} Probability density function. @@ -10225,6 +10435,7 @@ 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: @@ -10240,7 +10451,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Normal.pmf(value, name='pmf')` {#Normal.pmf} +#### `tf.contrib.distributions.Normal.pmf(value, name='pmf', **condition_kwargs)` {#Normal.pmf} Probability mass function. @@ -10249,6 +10460,7 @@ 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: @@ -10264,7 +10476,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Normal.prob(value, name='prob')` {#Normal.prob} +#### `tf.contrib.distributions.Normal.prob(value, name='prob', **condition_kwargs)` {#Normal.prob} Probability density/mass function (depending on `is_continuous`). @@ -10273,6 +10485,7 @@ 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: @@ -10283,7 +10496,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample')` {#Normal.sample} +#### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Normal.sample} Generate samples of the specified shape. @@ -10296,6 +10509,7 @@ 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: @@ -10305,7 +10519,7 @@ sample. - - - -#### `tf.contrib.distributions.Normal.sample_n(n, seed=None, name='sample_n')` {#Normal.sample_n} +#### `tf.contrib.distributions.Normal.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Normal.sample_n} Generate `n` samples. @@ -10316,6 +10530,7 @@ Generate `n` samples. observations to sample. * <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: @@ -10344,7 +10559,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Normal.survival_function(value, name='survival_function')` {#Normal.survival_function} +#### `tf.contrib.distributions.Normal.survival_function(value, name='survival_function', **condition_kwargs)` {#Normal.survival_function} Survival function. @@ -10361,6 +10576,7 @@ 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: @@ -10438,7 +10654,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.cdf(value, name='cdf')` {#NormalWithSoftplusSigma.cdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.cdf(value, name='cdf', **condition_kwargs)` {#NormalWithSoftplusSigma.cdf} Cumulative distribution function. @@ -10453,6 +10669,7 @@ 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: @@ -10472,7 +10689,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.NormalWithSoftplusSigma.entropy(name='entropy')` {#NormalWithSoftplusSigma.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -10536,7 +10753,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_cdf(value, name='log_cdf')` {#NormalWithSoftplusSigma.log_cdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_cdf} Log cumulative distribution function. @@ -10555,6 +10772,7 @@ 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: @@ -10565,7 +10783,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pdf(value, name='log_pdf')` {#NormalWithSoftplusSigma.log_pdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_pdf} Log probability density function. @@ -10574,6 +10792,7 @@ 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: @@ -10589,7 +10808,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pmf(value, name='log_pmf')` {#NormalWithSoftplusSigma.log_pmf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_pmf} Log probability mass function. @@ -10598,6 +10817,7 @@ 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: @@ -10613,7 +10833,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_prob(value, name='log_prob')` {#NormalWithSoftplusSigma.log_prob} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_prob(value, name='log_prob', **condition_kwargs)` {#NormalWithSoftplusSigma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -10622,6 +10842,7 @@ 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: @@ -10632,7 +10853,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_survival_function(value, name='log_survival_function')` {#NormalWithSoftplusSigma.log_survival_function} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#NormalWithSoftplusSigma.log_survival_function} Log survival function. @@ -10652,6 +10873,7 @@ 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: @@ -10738,7 +10960,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.pdf(value, name='pdf')` {#NormalWithSoftplusSigma.pdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.pdf(value, name='pdf', **condition_kwargs)` {#NormalWithSoftplusSigma.pdf} Probability density function. @@ -10747,6 +10969,7 @@ 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: @@ -10762,7 +10985,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.pmf(value, name='pmf')` {#NormalWithSoftplusSigma.pmf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.pmf(value, name='pmf', **condition_kwargs)` {#NormalWithSoftplusSigma.pmf} Probability mass function. @@ -10771,6 +10994,7 @@ 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: @@ -10786,7 +11010,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.prob(value, name='prob')` {#NormalWithSoftplusSigma.prob} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.prob(value, name='prob', **condition_kwargs)` {#NormalWithSoftplusSigma.prob} Probability density/mass function (depending on `is_continuous`). @@ -10795,6 +11019,7 @@ 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: @@ -10805,7 +11030,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample(sample_shape=(), seed=None, name='sample')` {#NormalWithSoftplusSigma.sample} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#NormalWithSoftplusSigma.sample} Generate samples of the specified shape. @@ -10818,6 +11043,7 @@ 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: @@ -10827,7 +11053,7 @@ sample. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample_n(n, seed=None, name='sample_n')` {#NormalWithSoftplusSigma.sample_n} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#NormalWithSoftplusSigma.sample_n} Generate `n` samples. @@ -10838,6 +11064,7 @@ Generate `n` samples. observations to sample. * <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: @@ -10866,7 +11093,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.survival_function(value, name='survival_function')` {#NormalWithSoftplusSigma.survival_function} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.survival_function(value, name='survival_function', **condition_kwargs)` {#NormalWithSoftplusSigma.survival_function} Survival function. @@ -10883,6 +11110,7 @@ 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: @@ -10984,7 +11212,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Poisson.cdf(value, name='cdf')` {#Poisson.cdf} +#### `tf.contrib.distributions.Poisson.cdf(value, name='cdf', **condition_kwargs)` {#Poisson.cdf} Cumulative distribution function. @@ -10999,6 +11227,7 @@ 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: @@ -11018,7 +11247,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Poisson.entropy(name='entropy')` {#Poisson.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -11089,7 +11318,7 @@ Rate parameter. - - - -#### `tf.contrib.distributions.Poisson.log_cdf(value, name='log_cdf')` {#Poisson.log_cdf} +#### `tf.contrib.distributions.Poisson.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Poisson.log_cdf} Log cumulative distribution function. @@ -11108,6 +11337,7 @@ 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: @@ -11118,7 +11348,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf')` {#Poisson.log_pdf} +#### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Poisson.log_pdf} Log probability density function. @@ -11127,6 +11357,7 @@ 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: @@ -11142,7 +11373,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf')` {#Poisson.log_pmf} +#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Poisson.log_pmf} Log probability mass function. @@ -11151,6 +11382,7 @@ 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: @@ -11166,7 +11398,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob')` {#Poisson.log_prob} +#### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob', **condition_kwargs)` {#Poisson.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -11182,6 +11414,7 @@ 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: @@ -11192,7 +11425,7 @@ 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')` {#Poisson.log_survival_function} +#### `tf.contrib.distributions.Poisson.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Poisson.log_survival_function} Log survival function. @@ -11212,6 +11445,7 @@ 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: @@ -11297,7 +11531,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf')` {#Poisson.pdf} +#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf', **condition_kwargs)` {#Poisson.pdf} Probability density function. @@ -11306,6 +11540,7 @@ 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: @@ -11321,7 +11556,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf')` {#Poisson.pmf} +#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf', **condition_kwargs)` {#Poisson.pmf} Probability mass function. @@ -11330,6 +11565,7 @@ 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: @@ -11345,7 +11581,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Poisson.prob(value, name='prob')` {#Poisson.prob} +#### `tf.contrib.distributions.Poisson.prob(value, name='prob', **condition_kwargs)` {#Poisson.prob} Probability density/mass function (depending on `is_continuous`). @@ -11361,6 +11597,7 @@ 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: @@ -11371,7 +11608,7 @@ legal if it is non-negative and its components are equal to integer values. - - - -#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample')` {#Poisson.sample} +#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Poisson.sample} Generate samples of the specified shape. @@ -11384,6 +11621,7 @@ 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: @@ -11393,7 +11631,7 @@ sample. - - - -#### `tf.contrib.distributions.Poisson.sample_n(n, seed=None, name='sample_n')` {#Poisson.sample_n} +#### `tf.contrib.distributions.Poisson.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Poisson.sample_n} Generate `n` samples. @@ -11404,6 +11642,7 @@ Generate `n` samples. observations to sample. * <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: @@ -11425,7 +11664,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Poisson.survival_function(value, name='survival_function')` {#Poisson.survival_function} +#### `tf.contrib.distributions.Poisson.survival_function(value, name='survival_function', **condition_kwargs)` {#Poisson.survival_function} Survival function. @@ -11442,6 +11681,7 @@ 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: @@ -11591,7 +11831,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.StudentT.cdf(value, name='cdf')` {#StudentT.cdf} +#### `tf.contrib.distributions.StudentT.cdf(value, name='cdf', **condition_kwargs)` {#StudentT.cdf} Cumulative distribution function. @@ -11606,6 +11846,7 @@ 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: @@ -11632,7 +11873,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.StudentT.entropy(name='entropy')` {#StudentT.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -11696,7 +11937,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf')` {#StudentT.log_cdf} +#### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf', **condition_kwargs)` {#StudentT.log_cdf} Log cumulative distribution function. @@ -11715,6 +11956,7 @@ 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: @@ -11725,7 +11967,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf')` {#StudentT.log_pdf} +#### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf', **condition_kwargs)` {#StudentT.log_pdf} Log probability density function. @@ -11734,6 +11976,7 @@ 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: @@ -11749,7 +11992,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf')` {#StudentT.log_pmf} +#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf', **condition_kwargs)` {#StudentT.log_pmf} Log probability mass function. @@ -11758,6 +12001,7 @@ 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: @@ -11773,7 +12017,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob')` {#StudentT.log_prob} +#### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob', **condition_kwargs)` {#StudentT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -11782,6 +12026,7 @@ 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: @@ -11792,7 +12037,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentT.log_survival_function(value, name='log_survival_function')` {#StudentT.log_survival_function} +#### `tf.contrib.distributions.StudentT.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#StudentT.log_survival_function} Log survival function. @@ -11812,6 +12057,7 @@ 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: @@ -11904,7 +12150,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.StudentT.pdf(value, name='pdf')` {#StudentT.pdf} +#### `tf.contrib.distributions.StudentT.pdf(value, name='pdf', **condition_kwargs)` {#StudentT.pdf} Probability density function. @@ -11913,6 +12159,7 @@ 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: @@ -11928,7 +12175,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.StudentT.pmf(value, name='pmf')` {#StudentT.pmf} +#### `tf.contrib.distributions.StudentT.pmf(value, name='pmf', **condition_kwargs)` {#StudentT.pmf} Probability mass function. @@ -11937,6 +12184,7 @@ 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: @@ -11952,7 +12200,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.StudentT.prob(value, name='prob')` {#StudentT.prob} +#### `tf.contrib.distributions.StudentT.prob(value, name='prob', **condition_kwargs)` {#StudentT.prob} Probability density/mass function (depending on `is_continuous`). @@ -11961,6 +12209,7 @@ 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: @@ -11971,7 +12220,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample')` {#StudentT.sample} +#### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#StudentT.sample} Generate samples of the specified shape. @@ -11984,6 +12233,7 @@ 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: @@ -11993,7 +12243,7 @@ sample. - - - -#### `tf.contrib.distributions.StudentT.sample_n(n, seed=None, name='sample_n')` {#StudentT.sample_n} +#### `tf.contrib.distributions.StudentT.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#StudentT.sample_n} Generate `n` samples. @@ -12004,6 +12254,7 @@ Generate `n` samples. observations to sample. * <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: @@ -12032,7 +12283,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.StudentT.survival_function(value, name='survival_function')` {#StudentT.survival_function} +#### `tf.contrib.distributions.StudentT.survival_function(value, name='survival_function', **condition_kwargs)` {#StudentT.survival_function} Survival function. @@ -12049,6 +12300,7 @@ 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: @@ -12136,7 +12388,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.cdf(value, name='cdf')` {#StudentTWithAbsDfSoftplusSigma.cdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.cdf(value, name='cdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.cdf} Cumulative distribution function. @@ -12151,6 +12403,7 @@ 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: @@ -12177,7 +12430,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.entropy(name='entropy')` {#StudentTWithAbsDfSoftplusSigma.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -12241,7 +12494,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_cdf(value, name='log_cdf')` {#StudentTWithAbsDfSoftplusSigma.log_cdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_cdf} Log cumulative distribution function. @@ -12260,6 +12513,7 @@ 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: @@ -12270,7 +12524,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pdf(value, name='log_pdf')` {#StudentTWithAbsDfSoftplusSigma.log_pdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_pdf} Log probability density function. @@ -12279,6 +12533,7 @@ 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: @@ -12294,7 +12549,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pmf(value, name='log_pmf')` {#StudentTWithAbsDfSoftplusSigma.log_pmf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_pmf} Log probability mass function. @@ -12303,6 +12558,7 @@ 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: @@ -12318,7 +12574,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob(value, name='log_prob')` {#StudentTWithAbsDfSoftplusSigma.log_prob} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob(value, name='log_prob', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -12327,6 +12583,7 @@ 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: @@ -12337,7 +12594,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_survival_function(value, name='log_survival_function')` {#StudentTWithAbsDfSoftplusSigma.log_survival_function} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_survival_function} Log survival function. @@ -12357,6 +12614,7 @@ 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: @@ -12449,7 +12707,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pdf(value, name='pdf')` {#StudentTWithAbsDfSoftplusSigma.pdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pdf(value, name='pdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.pdf} Probability density function. @@ -12458,6 +12716,7 @@ 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: @@ -12473,7 +12732,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pmf(value, name='pmf')` {#StudentTWithAbsDfSoftplusSigma.pmf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pmf(value, name='pmf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.pmf} Probability mass function. @@ -12482,6 +12741,7 @@ 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: @@ -12497,7 +12757,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.prob(value, name='prob')` {#StudentTWithAbsDfSoftplusSigma.prob} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.prob(value, name='prob', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.prob} Probability density/mass function (depending on `is_continuous`). @@ -12506,6 +12766,7 @@ 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: @@ -12516,7 +12777,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample(sample_shape=(), seed=None, name='sample')` {#StudentTWithAbsDfSoftplusSigma.sample} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.sample} Generate samples of the specified shape. @@ -12529,6 +12790,7 @@ 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: @@ -12538,7 +12800,7 @@ sample. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample_n(n, seed=None, name='sample_n')` {#StudentTWithAbsDfSoftplusSigma.sample_n} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.sample_n} Generate `n` samples. @@ -12549,6 +12811,7 @@ Generate `n` samples. observations to sample. * <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: @@ -12577,7 +12840,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.survival_function(value, name='survival_function')` {#StudentTWithAbsDfSoftplusSigma.survival_function} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.survival_function(value, name='survival_function', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.survival_function} Survival function. @@ -12594,6 +12857,7 @@ 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: @@ -12737,7 +13001,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Uniform.cdf(value, name='cdf')` {#Uniform.cdf} +#### `tf.contrib.distributions.Uniform.cdf(value, name='cdf', **condition_kwargs)` {#Uniform.cdf} Cumulative distribution function. @@ -12752,6 +13016,7 @@ 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: @@ -12771,7 +13036,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Uniform.entropy(name='entropy')` {#Uniform.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -12835,7 +13100,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Uniform.log_cdf(value, name='log_cdf')` {#Uniform.log_cdf} +#### `tf.contrib.distributions.Uniform.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Uniform.log_cdf} Log cumulative distribution function. @@ -12854,6 +13119,7 @@ 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: @@ -12864,7 +13130,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf')` {#Uniform.log_pdf} +#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Uniform.log_pdf} Log probability density function. @@ -12873,6 +13139,7 @@ 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: @@ -12888,7 +13155,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf')` {#Uniform.log_pmf} +#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Uniform.log_pmf} Log probability mass function. @@ -12897,6 +13164,7 @@ 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: @@ -12912,7 +13180,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob')` {#Uniform.log_prob} +#### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob', **condition_kwargs)` {#Uniform.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -12921,6 +13189,7 @@ 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: @@ -12931,7 +13200,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Uniform.log_survival_function(value, name='log_survival_function')` {#Uniform.log_survival_function} +#### `tf.contrib.distributions.Uniform.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Uniform.log_survival_function} Log survival function. @@ -12951,6 +13220,7 @@ 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: @@ -13030,7 +13300,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Uniform.pdf(value, name='pdf')` {#Uniform.pdf} +#### `tf.contrib.distributions.Uniform.pdf(value, name='pdf', **condition_kwargs)` {#Uniform.pdf} Probability density function. @@ -13039,6 +13309,7 @@ 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: @@ -13054,7 +13325,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Uniform.pmf(value, name='pmf')` {#Uniform.pmf} +#### `tf.contrib.distributions.Uniform.pmf(value, name='pmf', **condition_kwargs)` {#Uniform.pmf} Probability mass function. @@ -13063,6 +13334,7 @@ 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: @@ -13078,7 +13350,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Uniform.prob(value, name='prob')` {#Uniform.prob} +#### `tf.contrib.distributions.Uniform.prob(value, name='prob', **condition_kwargs)` {#Uniform.prob} Probability density/mass function (depending on `is_continuous`). @@ -13087,6 +13359,7 @@ 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: @@ -13104,7 +13377,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample')` {#Uniform.sample} +#### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Uniform.sample} Generate samples of the specified shape. @@ -13117,6 +13390,7 @@ 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: @@ -13126,7 +13400,7 @@ sample. - - - -#### `tf.contrib.distributions.Uniform.sample_n(n, seed=None, name='sample_n')` {#Uniform.sample_n} +#### `tf.contrib.distributions.Uniform.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Uniform.sample_n} Generate `n` samples. @@ -13137,6 +13411,7 @@ Generate `n` samples. observations to sample. * <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: @@ -13158,7 +13433,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Uniform.survival_function(value, name='survival_function')` {#Uniform.survival_function} +#### `tf.contrib.distributions.Uniform.survival_function(value, name='survival_function', **condition_kwargs)` {#Uniform.survival_function} Survival function. @@ -13175,6 +13450,7 @@ 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: @@ -13326,7 +13602,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.cdf(value, name='cdf')` {#MultivariateNormalDiag.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiag.cdf} Cumulative distribution function. @@ -13341,6 +13617,7 @@ 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: @@ -13360,7 +13637,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.MultivariateNormalDiag.entropy(name='entropy')` {#MultivariateNormalDiag.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -13424,7 +13701,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiag.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiag.log_cdf} Log cumulative distribution function. @@ -13443,6 +13720,7 @@ 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: @@ -13453,7 +13731,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiag.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiag.log_pdf} Log probability density function. @@ -13462,6 +13740,7 @@ 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: @@ -13477,7 +13756,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiag.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiag.log_pmf} Log probability mass function. @@ -13486,6 +13765,7 @@ 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: @@ -13501,7 +13781,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob')` {#MultivariateNormalDiag.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiag.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -13526,6 +13806,7 @@ 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: @@ -13543,7 +13824,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiag.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiag.log_survival_function} Log survival function. @@ -13563,6 +13844,7 @@ 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: @@ -13649,7 +13931,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf')` {#MultivariateNormalDiag.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiag.pdf} Probability density function. @@ -13658,6 +13940,7 @@ 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: @@ -13673,7 +13956,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf')` {#MultivariateNormalDiag.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiag.pmf} Probability mass function. @@ -13682,6 +13965,7 @@ 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: @@ -13697,7 +13981,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob')` {#MultivariateNormalDiag.prob} +#### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiag.prob} Probability density/mass function (depending on `is_continuous`). @@ -13722,6 +14006,7 @@ 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: @@ -13732,7 +14017,7 @@ or - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiag.sample} +#### `tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiag.sample} Generate samples of the specified shape. @@ -13745,6 +14030,7 @@ 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: @@ -13754,7 +14040,7 @@ sample. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalDiag.sample_n} +#### `tf.contrib.distributions.MultivariateNormalDiag.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#MultivariateNormalDiag.sample_n} Generate `n` samples. @@ -13765,6 +14051,7 @@ Generate `n` samples. observations to sample. * <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: @@ -13800,7 +14087,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.survival_function(value, name='survival_function')` {#MultivariateNormalDiag.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiag.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiag.survival_function} Survival function. @@ -13817,6 +14104,7 @@ 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: @@ -13955,7 +14243,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.cdf(value, name='cdf')` {#MultivariateNormalFull.cdf} +#### `tf.contrib.distributions.MultivariateNormalFull.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalFull.cdf} Cumulative distribution function. @@ -13970,6 +14258,7 @@ 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: @@ -13989,7 +14278,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.MultivariateNormalFull.entropy(name='entropy')` {#MultivariateNormalFull.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -14053,7 +14342,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_cdf(value, name='log_cdf')` {#MultivariateNormalFull.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalFull.log_cdf} Log cumulative distribution function. @@ -14072,6 +14361,7 @@ 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: @@ -14082,7 +14372,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf')` {#MultivariateNormalFull.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalFull.log_pdf} Log probability density function. @@ -14091,6 +14381,7 @@ 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: @@ -14106,7 +14397,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf')` {#MultivariateNormalFull.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalFull.log_pmf} Log probability mass function. @@ -14115,6 +14406,7 @@ 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: @@ -14130,7 +14422,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob')` {#MultivariateNormalFull.log_prob} +#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -14155,6 +14447,7 @@ 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: @@ -14172,7 +14465,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalFull.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalFull.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalFull.log_survival_function} Log survival function. @@ -14192,6 +14485,7 @@ 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: @@ -14278,7 +14572,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf')` {#MultivariateNormalFull.pdf} +#### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalFull.pdf} Probability density function. @@ -14287,6 +14581,7 @@ 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: @@ -14302,7 +14597,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf')` {#MultivariateNormalFull.pmf} +#### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalFull.pmf} Probability mass function. @@ -14311,6 +14606,7 @@ 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: @@ -14326,7 +14622,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob')` {#MultivariateNormalFull.prob} +#### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalFull.prob} Probability density/mass function (depending on `is_continuous`). @@ -14351,6 +14647,7 @@ 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: @@ -14361,7 +14658,7 @@ or - - - -#### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalFull.sample} +#### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalFull.sample} Generate samples of the specified shape. @@ -14374,6 +14671,7 @@ 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: @@ -14383,7 +14681,7 @@ sample. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalFull.sample_n} +#### `tf.contrib.distributions.MultivariateNormalFull.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#MultivariateNormalFull.sample_n} Generate `n` samples. @@ -14394,6 +14692,7 @@ Generate `n` samples. observations to sample. * <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: @@ -14429,7 +14728,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.survival_function(value, name='survival_function')` {#MultivariateNormalFull.survival_function} +#### `tf.contrib.distributions.MultivariateNormalFull.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalFull.survival_function} Survival function. @@ -14446,6 +14745,7 @@ 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: @@ -14593,7 +14893,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.cdf(value, name='cdf')` {#MultivariateNormalCholesky.cdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalCholesky.cdf} Cumulative distribution function. @@ -14608,6 +14908,7 @@ 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: @@ -14627,7 +14928,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.MultivariateNormalCholesky.entropy(name='entropy')` {#MultivariateNormalCholesky.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -14691,7 +14992,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf')` {#MultivariateNormalCholesky.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalCholesky.log_cdf} Log cumulative distribution function. @@ -14710,6 +15011,7 @@ 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: @@ -14720,7 +15022,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf')` {#MultivariateNormalCholesky.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalCholesky.log_pdf} Log probability density function. @@ -14729,6 +15031,7 @@ 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: @@ -14744,7 +15047,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf')` {#MultivariateNormalCholesky.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalCholesky.log_pmf} Log probability mass function. @@ -14753,6 +15056,7 @@ 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: @@ -14768,7 +15072,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob')` {#MultivariateNormalCholesky.log_prob} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -14793,6 +15097,7 @@ 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: @@ -14810,7 +15115,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalCholesky.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalCholesky.log_survival_function} Log survival function. @@ -14830,6 +15135,7 @@ 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: @@ -14916,7 +15222,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf')` {#MultivariateNormalCholesky.pdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalCholesky.pdf} Probability density function. @@ -14925,6 +15231,7 @@ 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: @@ -14940,7 +15247,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf')` {#MultivariateNormalCholesky.pmf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalCholesky.pmf} Probability mass function. @@ -14949,6 +15256,7 @@ 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: @@ -14964,7 +15272,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob')` {#MultivariateNormalCholesky.prob} +#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalCholesky.prob} Probability density/mass function (depending on `is_continuous`). @@ -14989,6 +15297,7 @@ 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: @@ -14999,7 +15308,7 @@ or - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalCholesky.sample} +#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalCholesky.sample} Generate samples of the specified shape. @@ -15012,6 +15321,7 @@ 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: @@ -15021,7 +15331,7 @@ sample. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalCholesky.sample_n} +#### `tf.contrib.distributions.MultivariateNormalCholesky.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#MultivariateNormalCholesky.sample_n} Generate `n` samples. @@ -15032,6 +15342,7 @@ Generate `n` samples. observations to sample. * <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: @@ -15067,7 +15378,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.survival_function(value, name='survival_function')` {#MultivariateNormalCholesky.survival_function} +#### `tf.contrib.distributions.MultivariateNormalCholesky.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalCholesky.survival_function} Survival function. @@ -15084,6 +15395,7 @@ 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: @@ -15257,7 +15569,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.cdf(value, name='cdf')` {#MultivariateNormalDiagPlusVDVT.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.cdf} Cumulative distribution function. @@ -15272,6 +15584,7 @@ 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: @@ -15291,7 +15604,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.entropy(name='entropy')` {#MultivariateNormalDiagPlusVDVT.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -15355,7 +15668,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiagPlusVDVT.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_cdf} Log cumulative distribution function. @@ -15374,6 +15687,7 @@ 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: @@ -15384,7 +15698,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagPlusVDVT.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_pdf} Log probability density function. @@ -15393,6 +15707,7 @@ 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: @@ -15408,7 +15723,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagPlusVDVT.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_pmf} Log probability mass function. @@ -15417,6 +15732,7 @@ 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: @@ -15432,7 +15748,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob')` {#MultivariateNormalDiagPlusVDVT.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -15457,6 +15773,7 @@ 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: @@ -15474,7 +15791,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiagPlusVDVT.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_survival_function} Log survival function. @@ -15494,6 +15811,7 @@ 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: @@ -15580,7 +15898,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf')` {#MultivariateNormalDiagPlusVDVT.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.pdf} Probability density function. @@ -15589,6 +15907,7 @@ 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: @@ -15604,7 +15923,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf')` {#MultivariateNormalDiagPlusVDVT.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.pmf} Probability mass function. @@ -15613,6 +15932,7 @@ 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: @@ -15628,7 +15948,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob')` {#MultivariateNormalDiagPlusVDVT.prob} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.prob} Probability density/mass function (depending on `is_continuous`). @@ -15653,6 +15973,7 @@ 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: @@ -15663,7 +15984,7 @@ or - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiagPlusVDVT.sample} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.sample} Generate samples of the specified shape. @@ -15676,6 +15997,7 @@ 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: @@ -15685,7 +16007,7 @@ sample. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalDiagPlusVDVT.sample_n} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.sample_n} Generate `n` samples. @@ -15696,6 +16018,7 @@ Generate `n` samples. observations to sample. * <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: @@ -15731,7 +16054,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function')` {#MultivariateNormalDiagPlusVDVT.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.survival_function} Survival function. @@ -15748,6 +16071,7 @@ 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: @@ -15825,7 +16149,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.cdf(value, name='cdf')` {#MultivariateNormalDiagWithSoftplusStDev.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.cdf} Cumulative distribution function. @@ -15840,6 +16164,7 @@ 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: @@ -15859,7 +16184,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.entropy(name='entropy')` {#MultivariateNormalDiagWithSoftplusStDev.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -15923,7 +16248,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiagWithSoftplusStDev.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_cdf} Log cumulative distribution function. @@ -15942,6 +16267,7 @@ 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: @@ -15952,7 +16278,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_pdf} Log probability density function. @@ -15961,6 +16287,7 @@ 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: @@ -15976,7 +16303,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_pmf} Log probability mass function. @@ -15985,6 +16312,7 @@ 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: @@ -16000,7 +16328,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob')` {#MultivariateNormalDiagWithSoftplusStDev.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -16025,6 +16353,7 @@ 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: @@ -16042,7 +16371,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiagWithSoftplusStDev.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_survival_function} Log survival function. @@ -16062,6 +16391,7 @@ 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: @@ -16148,7 +16478,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf')` {#MultivariateNormalDiagWithSoftplusStDev.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.pdf} Probability density function. @@ -16157,6 +16487,7 @@ 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: @@ -16172,7 +16503,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf')` {#MultivariateNormalDiagWithSoftplusStDev.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.pmf} Probability mass function. @@ -16181,6 +16512,7 @@ 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: @@ -16196,7 +16528,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob')` {#MultivariateNormalDiagWithSoftplusStDev.prob} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.prob} Probability density/mass function (depending on `is_continuous`). @@ -16221,6 +16553,7 @@ 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: @@ -16231,7 +16564,7 @@ or - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiagWithSoftplusStDev.sample} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.sample} Generate samples of the specified shape. @@ -16244,6 +16577,7 @@ 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: @@ -16253,7 +16587,7 @@ sample. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalDiagWithSoftplusStDev.sample_n} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.sample_n} Generate `n` samples. @@ -16264,6 +16598,7 @@ Generate `n` samples. observations to sample. * <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: @@ -16299,7 +16634,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.survival_function(value, name='survival_function')` {#MultivariateNormalDiagWithSoftplusStDev.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.survival_function} Survival function. @@ -16316,6 +16651,7 @@ 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: @@ -16553,7 +16889,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Dirichlet.cdf(value, name='cdf')` {#Dirichlet.cdf} +#### `tf.contrib.distributions.Dirichlet.cdf(value, name='cdf', **condition_kwargs)` {#Dirichlet.cdf} Cumulative distribution function. @@ -16568,6 +16904,7 @@ 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: @@ -16587,7 +16924,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Dirichlet.entropy(name='entropy')` {#Dirichlet.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -16651,7 +16988,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Dirichlet.log_cdf(value, name='log_cdf')` {#Dirichlet.log_cdf} +#### `tf.contrib.distributions.Dirichlet.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Dirichlet.log_cdf} Log cumulative distribution function. @@ -16670,6 +17007,7 @@ 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: @@ -16680,7 +17018,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf')` {#Dirichlet.log_pdf} +#### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Dirichlet.log_pdf} Log probability density function. @@ -16689,6 +17027,7 @@ 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: @@ -16704,7 +17043,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf')` {#Dirichlet.log_pmf} +#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Dirichlet.log_pmf} Log probability mass function. @@ -16713,6 +17052,7 @@ 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: @@ -16728,7 +17068,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob')` {#Dirichlet.log_prob} +#### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob', **condition_kwargs)` {#Dirichlet.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -16745,6 +17085,7 @@ 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: @@ -16755,7 +17096,7 @@ 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')` {#Dirichlet.log_survival_function} +#### `tf.contrib.distributions.Dirichlet.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Dirichlet.log_survival_function} Log survival function. @@ -16775,6 +17116,7 @@ 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: @@ -16861,7 +17203,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf')` {#Dirichlet.pdf} +#### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf', **condition_kwargs)` {#Dirichlet.pdf} Probability density function. @@ -16870,6 +17212,7 @@ 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: @@ -16885,7 +17228,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf')` {#Dirichlet.pmf} +#### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf', **condition_kwargs)` {#Dirichlet.pmf} Probability mass function. @@ -16894,6 +17237,7 @@ 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: @@ -16909,7 +17253,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Dirichlet.prob(value, name='prob')` {#Dirichlet.prob} +#### `tf.contrib.distributions.Dirichlet.prob(value, name='prob', **condition_kwargs)` {#Dirichlet.prob} Probability density/mass function (depending on `is_continuous`). @@ -16926,6 +17270,7 @@ 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: @@ -16936,7 +17281,7 @@ in `self.alpha`. `x` is only legal if it sums up to one. - - - -#### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample')` {#Dirichlet.sample} +#### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Dirichlet.sample} Generate samples of the specified shape. @@ -16949,6 +17294,7 @@ 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: @@ -16958,7 +17304,7 @@ sample. - - - -#### `tf.contrib.distributions.Dirichlet.sample_n(n, seed=None, name='sample_n')` {#Dirichlet.sample_n} +#### `tf.contrib.distributions.Dirichlet.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Dirichlet.sample_n} Generate `n` samples. @@ -16969,6 +17315,7 @@ Generate `n` samples. observations to sample. * <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: @@ -16990,7 +17337,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Dirichlet.survival_function(value, name='survival_function')` {#Dirichlet.survival_function} +#### `tf.contrib.distributions.Dirichlet.survival_function(value, name='survival_function', **condition_kwargs)` {#Dirichlet.survival_function} Survival function. @@ -17007,6 +17354,7 @@ 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: @@ -17197,7 +17545,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf')` {#DirichletMultinomial.cdf} +#### `tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf', **condition_kwargs)` {#DirichletMultinomial.cdf} Cumulative distribution function. @@ -17212,6 +17560,7 @@ 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: @@ -17231,7 +17580,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.DirichletMultinomial.entropy(name='entropy')` {#DirichletMultinomial.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -17295,7 +17644,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf')` {#DirichletMultinomial.log_cdf} +#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#DirichletMultinomial.log_cdf} Log cumulative distribution function. @@ -17314,6 +17663,7 @@ 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: @@ -17324,7 +17674,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf')` {#DirichletMultinomial.log_pdf} +#### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#DirichletMultinomial.log_pdf} Log probability density function. @@ -17333,6 +17683,7 @@ 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: @@ -17348,7 +17699,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf')` {#DirichletMultinomial.log_pmf} +#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#DirichletMultinomial.log_pmf} Log probability mass function. @@ -17357,6 +17708,7 @@ 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: @@ -17372,7 +17724,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob')` {#DirichletMultinomial.log_prob} +#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob', **condition_kwargs)` {#DirichletMultinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -17396,6 +17748,7 @@ 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: @@ -17406,7 +17759,7 @@ 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')` {#DirichletMultinomial.log_survival_function} +#### `tf.contrib.distributions.DirichletMultinomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#DirichletMultinomial.log_survival_function} Log survival function. @@ -17426,6 +17779,7 @@ 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: @@ -17512,7 +17866,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf')` {#DirichletMultinomial.pdf} +#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf', **condition_kwargs)` {#DirichletMultinomial.pdf} Probability density function. @@ -17521,6 +17875,7 @@ 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: @@ -17536,7 +17891,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf')` {#DirichletMultinomial.pmf} +#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf', **condition_kwargs)` {#DirichletMultinomial.pmf} Probability mass function. @@ -17545,6 +17900,7 @@ 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: @@ -17560,7 +17916,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob')` {#DirichletMultinomial.prob} +#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob', **condition_kwargs)` {#DirichletMultinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -17584,6 +17940,7 @@ 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: @@ -17594,7 +17951,7 @@ it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample')` {#DirichletMultinomial.sample} +#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#DirichletMultinomial.sample} Generate samples of the specified shape. @@ -17607,6 +17964,7 @@ 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: @@ -17616,7 +17974,7 @@ sample. - - - -#### `tf.contrib.distributions.DirichletMultinomial.sample_n(n, seed=None, name='sample_n')` {#DirichletMultinomial.sample_n} +#### `tf.contrib.distributions.DirichletMultinomial.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#DirichletMultinomial.sample_n} Generate `n` samples. @@ -17627,6 +17985,7 @@ Generate `n` samples. observations to sample. * <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: @@ -17648,7 +18007,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.DirichletMultinomial.survival_function(value, name='survival_function')` {#DirichletMultinomial.survival_function} +#### `tf.contrib.distributions.DirichletMultinomial.survival_function(value, name='survival_function', **condition_kwargs)` {#DirichletMultinomial.survival_function} Survival function. @@ -17665,6 +18024,7 @@ 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: @@ -17856,7 +18216,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Multinomial.cdf(value, name='cdf')` {#Multinomial.cdf} +#### `tf.contrib.distributions.Multinomial.cdf(value, name='cdf', **condition_kwargs)` {#Multinomial.cdf} Cumulative distribution function. @@ -17871,6 +18231,7 @@ 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: @@ -17890,7 +18251,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Multinomial.entropy(name='entropy')` {#Multinomial.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -17954,7 +18315,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf')` {#Multinomial.log_cdf} +#### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Multinomial.log_cdf} Log cumulative distribution function. @@ -17973,6 +18334,7 @@ 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: @@ -17983,7 +18345,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf')` {#Multinomial.log_pdf} +#### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Multinomial.log_pdf} Log probability density function. @@ -17992,6 +18354,7 @@ 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: @@ -18007,7 +18370,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf')` {#Multinomial.log_pmf} +#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Multinomial.log_pmf} Log probability mass function. @@ -18016,6 +18379,7 @@ 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: @@ -18031,7 +18395,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob')` {#Multinomial.log_prob} +#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob', **condition_kwargs)` {#Multinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -18055,6 +18419,7 @@ 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: @@ -18065,7 +18430,7 @@ 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')` {#Multinomial.log_survival_function} +#### `tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Multinomial.log_survival_function} Log survival function. @@ -18085,6 +18450,7 @@ 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: @@ -18187,7 +18553,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf')` {#Multinomial.pdf} +#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf', **condition_kwargs)` {#Multinomial.pdf} Probability density function. @@ -18196,6 +18562,7 @@ 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: @@ -18211,7 +18578,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf')` {#Multinomial.pmf} +#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf', **condition_kwargs)` {#Multinomial.pmf} Probability mass function. @@ -18220,6 +18587,7 @@ 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: @@ -18235,7 +18603,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Multinomial.prob(value, name='prob')` {#Multinomial.prob} +#### `tf.contrib.distributions.Multinomial.prob(value, name='prob', **condition_kwargs)` {#Multinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -18259,6 +18627,7 @@ 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: @@ -18269,7 +18638,7 @@ if it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample')` {#Multinomial.sample} +#### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Multinomial.sample} Generate samples of the specified shape. @@ -18282,6 +18651,7 @@ 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: @@ -18291,7 +18661,7 @@ sample. - - - -#### `tf.contrib.distributions.Multinomial.sample_n(n, seed=None, name='sample_n')` {#Multinomial.sample_n} +#### `tf.contrib.distributions.Multinomial.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Multinomial.sample_n} Generate `n` samples. @@ -18302,6 +18672,7 @@ Generate `n` samples. observations to sample. * <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: @@ -18323,7 +18694,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Multinomial.survival_function(value, name='survival_function')` {#Multinomial.survival_function} +#### `tf.contrib.distributions.Multinomial.survival_function(value, name='survival_function', **condition_kwargs)` {#Multinomial.survival_function} Survival function. @@ -18340,6 +18711,7 @@ 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: @@ -18496,7 +18868,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.WishartCholesky.cdf(value, name='cdf')` {#WishartCholesky.cdf} +#### `tf.contrib.distributions.WishartCholesky.cdf(value, name='cdf', **condition_kwargs)` {#WishartCholesky.cdf} Cumulative distribution function. @@ -18511,6 +18883,7 @@ 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: @@ -18551,7 +18924,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.WishartCholesky.entropy(name='entropy')` {#WishartCholesky.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -18615,7 +18988,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf')` {#WishartCholesky.log_cdf} +#### `tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf', **condition_kwargs)` {#WishartCholesky.log_cdf} Log cumulative distribution function. @@ -18634,6 +19007,7 @@ 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: @@ -18651,7 +19025,7 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf')` {#WishartCholesky.log_pdf} +#### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf', **condition_kwargs)` {#WishartCholesky.log_pdf} Log probability density function. @@ -18660,6 +19034,7 @@ 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: @@ -18675,7 +19050,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf')` {#WishartCholesky.log_pmf} +#### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf', **condition_kwargs)` {#WishartCholesky.log_pmf} Log probability mass function. @@ -18684,6 +19059,7 @@ 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: @@ -18699,7 +19075,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob')` {#WishartCholesky.log_prob} +#### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob', **condition_kwargs)` {#WishartCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -18708,6 +19084,7 @@ 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: @@ -18718,7 +19095,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartCholesky.log_survival_function(value, name='log_survival_function')` {#WishartCholesky.log_survival_function} +#### `tf.contrib.distributions.WishartCholesky.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#WishartCholesky.log_survival_function} Log survival function. @@ -18738,6 +19115,7 @@ 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: @@ -18824,7 +19202,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf')` {#WishartCholesky.pdf} +#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf', **condition_kwargs)` {#WishartCholesky.pdf} Probability density function. @@ -18833,6 +19211,7 @@ 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: @@ -18848,7 +19227,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf')` {#WishartCholesky.pmf} +#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf', **condition_kwargs)` {#WishartCholesky.pmf} Probability mass function. @@ -18857,6 +19236,7 @@ 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: @@ -18872,7 +19252,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob')` {#WishartCholesky.prob} +#### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob', **condition_kwargs)` {#WishartCholesky.prob} Probability density/mass function (depending on `is_continuous`). @@ -18881,6 +19261,7 @@ 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: @@ -18891,7 +19272,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample')` {#WishartCholesky.sample} +#### `tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#WishartCholesky.sample} Generate samples of the specified shape. @@ -18904,6 +19285,7 @@ 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: @@ -18913,7 +19295,7 @@ sample. - - - -#### `tf.contrib.distributions.WishartCholesky.sample_n(n, seed=None, name='sample_n')` {#WishartCholesky.sample_n} +#### `tf.contrib.distributions.WishartCholesky.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#WishartCholesky.sample_n} Generate `n` samples. @@ -18924,6 +19306,7 @@ Generate `n` samples. observations to sample. * <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: @@ -18959,7 +19342,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.WishartCholesky.survival_function(value, name='survival_function')` {#WishartCholesky.survival_function} +#### `tf.contrib.distributions.WishartCholesky.survival_function(value, name='survival_function', **condition_kwargs)` {#WishartCholesky.survival_function} Survival function. @@ -18976,6 +19359,7 @@ 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: @@ -19128,7 +19512,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.WishartFull.cdf(value, name='cdf')` {#WishartFull.cdf} +#### `tf.contrib.distributions.WishartFull.cdf(value, name='cdf', **condition_kwargs)` {#WishartFull.cdf} Cumulative distribution function. @@ -19143,6 +19527,7 @@ 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: @@ -19183,7 +19568,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.WishartFull.entropy(name='entropy')` {#WishartFull.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -19247,7 +19632,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf')` {#WishartFull.log_cdf} +#### `tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf', **condition_kwargs)` {#WishartFull.log_cdf} Log cumulative distribution function. @@ -19266,6 +19651,7 @@ 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: @@ -19283,7 +19669,7 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf')` {#WishartFull.log_pdf} +#### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf', **condition_kwargs)` {#WishartFull.log_pdf} Log probability density function. @@ -19292,6 +19678,7 @@ 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: @@ -19307,7 +19694,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf')` {#WishartFull.log_pmf} +#### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf', **condition_kwargs)` {#WishartFull.log_pmf} Log probability mass function. @@ -19316,6 +19703,7 @@ 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: @@ -19331,7 +19719,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob')` {#WishartFull.log_prob} +#### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob', **condition_kwargs)` {#WishartFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -19340,6 +19728,7 @@ 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: @@ -19350,7 +19739,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartFull.log_survival_function(value, name='log_survival_function')` {#WishartFull.log_survival_function} +#### `tf.contrib.distributions.WishartFull.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#WishartFull.log_survival_function} Log survival function. @@ -19370,6 +19759,7 @@ 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: @@ -19456,7 +19846,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf')` {#WishartFull.pdf} +#### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf', **condition_kwargs)` {#WishartFull.pdf} Probability density function. @@ -19465,6 +19855,7 @@ 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: @@ -19480,7 +19871,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf')` {#WishartFull.pmf} +#### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf', **condition_kwargs)` {#WishartFull.pmf} Probability mass function. @@ -19489,6 +19880,7 @@ 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: @@ -19504,7 +19896,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.WishartFull.prob(value, name='prob')` {#WishartFull.prob} +#### `tf.contrib.distributions.WishartFull.prob(value, name='prob', **condition_kwargs)` {#WishartFull.prob} Probability density/mass function (depending on `is_continuous`). @@ -19513,6 +19905,7 @@ 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: @@ -19523,7 +19916,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample')` {#WishartFull.sample} +#### `tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#WishartFull.sample} Generate samples of the specified shape. @@ -19536,6 +19929,7 @@ 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: @@ -19545,7 +19939,7 @@ sample. - - - -#### `tf.contrib.distributions.WishartFull.sample_n(n, seed=None, name='sample_n')` {#WishartFull.sample_n} +#### `tf.contrib.distributions.WishartFull.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#WishartFull.sample_n} Generate `n` samples. @@ -19556,6 +19950,7 @@ Generate `n` samples. observations to sample. * <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: @@ -19591,7 +19986,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.WishartFull.survival_function(value, name='survival_function')` {#WishartFull.survival_function} +#### `tf.contrib.distributions.WishartFull.survival_function(value, name='survival_function', **condition_kwargs)` {#WishartFull.survival_function} Survival function. @@ -19608,6 +20003,7 @@ 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: @@ -19639,11 +20035,17 @@ Variance. A Transformed Distribution. -A Transformed Distribution models `p(y)` given a base distribution `p(x)`, and -a deterministic, invertible, differentiable transform, `Y = g(X)`. The +A `TransformedDistribution` models `p(y)` given a base distribution `p(x)`, +and a deterministic, invertible, differentiable transform, `Y = g(X)`. The transform is typically an instance of the `Bijector` class and the base distribution is typically an instance of the `Distribution` class. +A `Bijector` is expected to implement the following functions: +- `forward`, +- `inverse`, +- `inverse_log_det_jacobian`. +The semantics of these functions are outlined in the `Bijector` documentation. + Shapes, type, and reparameterization are taken from the base distribution. Write `P(Y=y)` for cumulative density function of random variable (rv) `Y` and @@ -19657,7 +20059,7 @@ associated with rv `X` in the following ways: Mathematically: - ``` + ```none Y = g(X) ``` @@ -19671,7 +20073,7 @@ associated with rv `X` in the following ways: Mathematically: - ``` + ```none (log o p o g^{-1})(y) + (log o det o J o g^{-1})(y) ``` @@ -19686,7 +20088,7 @@ associated with rv `X` in the following ways: Mathematically: - ``` + ```none (log o P o g^{-1})(y) ``` @@ -19705,7 +20107,7 @@ distribution: ```python ds = tf.contrib.distributions log_normal = ds.TransformedDistribution( - base_distribution=ds.Normal(mu=mu, sigma=sigma), + distribution=ds.Normal(mu=mu, sigma=sigma), bijector=ds.bijector.Exp(), name="LogNormalTransformedDistribution") ``` @@ -19715,7 +20117,7 @@ A `LogNormal` made from callables: ```python ds = tf.contrib.distributions log_normal = ds.TransformedDistribution( - base_distribution=ds.Normal(mu=mu, sigma=sigma), + distribution=ds.Normal(mu=mu, sigma=sigma), bijector=ds.bijector.Inline( forward_fn=tf.exp, inverse_fn=tf.log, @@ -19729,24 +20131,25 @@ Another example constructing a Normal from a StandardNormal: ```python ds = tf.contrib.distributions normal = ds.TransformedDistribution( - base_distribution=ds.Normal(mu=0, sigma=1), + distribution=ds.Normal(mu=0, sigma=1), bijector=ds.bijector.ScaleAndShift(loc=mu, scale=sigma, event_ndims=0), name="NormalTransformedDistribution") ``` - - - -#### `tf.contrib.distributions.TransformedDistribution.__init__(base_distribution, bijector, name='TransformedDistribution')` {#TransformedDistribution.__init__} +#### `tf.contrib.distributions.TransformedDistribution.__init__(distribution, bijector, name=None)` {#TransformedDistribution.__init__} Construct a Transformed Distribution. ##### Args: -* <b>`base_distribution`</b>: The base distribution class to transform. Typically an +* <b>`distribution`</b>: The base distribution class to transform. Typically an instance of `Distribution`. * <b>`bijector`</b>: The object responsible for calculating the transformation. Typically an instance of `Bijector`. -* <b>`name`</b>: The name for the distribution. +* <b>`name`</b>: The name for the distribution. Default: + `bijector.name + distribution.name`. - - - @@ -19772,13 +20175,6 @@ undefined. - - - -#### `tf.contrib.distributions.TransformedDistribution.base_distribution` {#TransformedDistribution.base_distribution} - -Base distribution, p(x). - - -- - - - #### `tf.contrib.distributions.TransformedDistribution.batch_shape(name='batch_shape')` {#TransformedDistribution.batch_shape} Shape of a single sample from a single event index as a 1-D `Tensor`. @@ -19806,7 +20202,7 @@ Function transforming x => y. - - - -#### `tf.contrib.distributions.TransformedDistribution.cdf(value, name='cdf')` {#TransformedDistribution.cdf} +#### `tf.contrib.distributions.TransformedDistribution.cdf(value, name='cdf', **condition_kwargs)` {#TransformedDistribution.cdf} Cumulative distribution function. @@ -19816,11 +20212,20 @@ Given random variable `X`, the cumulative distribution function `cdf` is: cdf(x) := P[X <= x] ``` + +Additional documentation from `TransformedDistribution`: + +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. + ##### 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: @@ -19831,6 +20236,13 @@ cdf(x) := P[X <= x] - - - +#### `tf.contrib.distributions.TransformedDistribution.distribution` {#TransformedDistribution.distribution} + +Base distribution, p(x). + + +- - - + #### `tf.contrib.distributions.TransformedDistribution.dtype` {#TransformedDistribution.dtype} The `DType` of `Tensor`s handled by this `Distribution`. @@ -19840,7 +20252,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.TransformedDistribution.entropy(name='entropy')` {#TransformedDistribution.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -19904,7 +20316,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf')` {#TransformedDistribution.log_cdf} +#### `tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#TransformedDistribution.log_cdf} Log cumulative distribution function. @@ -19918,11 +20330,20 @@ 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`: + +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. + ##### 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: @@ -19933,7 +20354,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf')` {#TransformedDistribution.log_pdf} +#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#TransformedDistribution.log_pdf} Log probability density function. @@ -19942,6 +20363,7 @@ 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: @@ -19957,7 +20379,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf')` {#TransformedDistribution.log_pmf} +#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#TransformedDistribution.log_pmf} Log probability mass function. @@ -19966,6 +20388,7 @@ 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: @@ -19981,7 +20404,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob')` {#TransformedDistribution.log_prob} +#### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob', **condition_kwargs)` {#TransformedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -19989,16 +20412,22 @@ Log probability density/mass function (depending on `is_continuous`). Additional documentation from `TransformedDistribution`: Implements `(log o p o g^{-1})(y) + (log o det o J o g^{-1})(y)`, -where `g^{-1}` is the inverse of `transform`. + 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`. -Also raises a `ValueError` if `inverse` was not provided to the -distribution and `y` was not returned from `sample`. +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. ##### 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: @@ -20009,7 +20438,7 @@ distribution and `y` was not returned from `sample`. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_survival_function(value, name='log_survival_function')` {#TransformedDistribution.log_survival_function} +#### `tf.contrib.distributions.TransformedDistribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#TransformedDistribution.log_survival_function} Log survival function. @@ -20024,11 +20453,20 @@ 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`: + +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. + ##### 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: @@ -20108,7 +20546,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf')` {#TransformedDistribution.pdf} +#### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf', **condition_kwargs)` {#TransformedDistribution.pdf} Probability density function. @@ -20117,6 +20555,7 @@ 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: @@ -20132,7 +20571,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf')` {#TransformedDistribution.pmf} +#### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf', **condition_kwargs)` {#TransformedDistribution.pmf} Probability mass function. @@ -20141,6 +20580,7 @@ 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: @@ -20156,7 +20596,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob')` {#TransformedDistribution.prob} +#### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob', **condition_kwargs)` {#TransformedDistribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -20164,16 +20604,22 @@ 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`. + inverse of `transform`. + + Also raises a `ValueError` if `inverse` was not provided to the + distribution and `y` was not returned from `sample`. + +##### <b>`condition_kwargs`</b>: -Also raises a `ValueError` if `inverse` was not provided to the -distribution and `y` was not returned from `sample`. +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. ##### 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: @@ -20184,7 +20630,7 @@ distribution and `y` was not returned from `sample`. - - - -#### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#TransformedDistribution.sample} +#### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#TransformedDistribution.sample} Generate samples of the specified shape. @@ -20197,6 +20643,7 @@ 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: @@ -20206,7 +20653,7 @@ sample. - - - -#### `tf.contrib.distributions.TransformedDistribution.sample_n(n, seed=None, name='sample_n')` {#TransformedDistribution.sample_n} +#### `tf.contrib.distributions.TransformedDistribution.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#TransformedDistribution.sample_n} Generate `n` samples. @@ -20214,7 +20661,12 @@ Generate `n` samples. Additional documentation from `TransformedDistribution`: Samples from the base distribution and then passes through -the bijector's forward transform. + the bijector's forward transform. + +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. ##### Args: @@ -20223,6 +20675,7 @@ the bijector's forward transform. observations to sample. * <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: @@ -20244,7 +20697,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.TransformedDistribution.survival_function(value, name='survival_function')` {#TransformedDistribution.survival_function} +#### `tf.contrib.distributions.TransformedDistribution.survival_function(value, name='survival_function', **condition_kwargs)` {#TransformedDistribution.survival_function} Survival function. @@ -20256,11 +20709,20 @@ survival_function(x) = P[X > x] = 1 - cdf(x). ``` + +Additional documentation from `TransformedDistribution`: + +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. + ##### 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: @@ -20424,7 +20886,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.QuantizedDistribution.cdf(value, name='cdf')` {#QuantizedDistribution.cdf} +#### `tf.contrib.distributions.QuantizedDistribution.cdf(value, name='cdf', **condition_kwargs)` {#QuantizedDistribution.cdf} Cumulative distribution function. @@ -20457,6 +20919,7 @@ 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: @@ -20476,7 +20939,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.QuantizedDistribution.entropy(name='entropy')` {#QuantizedDistribution.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -20540,7 +21003,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_cdf(value, name='log_cdf')` {#QuantizedDistribution.log_cdf} +#### `tf.contrib.distributions.QuantizedDistribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#QuantizedDistribution.log_cdf} Log cumulative distribution function. @@ -20577,6 +21040,7 @@ 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: @@ -20587,7 +21051,7 @@ The base distribution's `log_cdf` method must be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pdf(value, name='log_pdf')` {#QuantizedDistribution.log_pdf} +#### `tf.contrib.distributions.QuantizedDistribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#QuantizedDistribution.log_pdf} Log probability density function. @@ -20596,6 +21060,7 @@ 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: @@ -20611,7 +21076,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pmf(value, name='log_pmf')` {#QuantizedDistribution.log_pmf} +#### `tf.contrib.distributions.QuantizedDistribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#QuantizedDistribution.log_pmf} Log probability mass function. @@ -20620,6 +21085,7 @@ 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: @@ -20635,7 +21101,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob')` {#QuantizedDistribution.log_prob} +#### `tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob', **condition_kwargs)` {#QuantizedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -20662,6 +21128,7 @@ 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: @@ -20672,7 +21139,7 @@ must also be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_survival_function(value, name='log_survival_function')` {#QuantizedDistribution.log_survival_function} +#### `tf.contrib.distributions.QuantizedDistribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#QuantizedDistribution.log_survival_function} Log survival function. @@ -20710,6 +21177,7 @@ 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: @@ -20789,7 +21257,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.pdf(value, name='pdf')` {#QuantizedDistribution.pdf} +#### `tf.contrib.distributions.QuantizedDistribution.pdf(value, name='pdf', **condition_kwargs)` {#QuantizedDistribution.pdf} Probability density function. @@ -20798,6 +21266,7 @@ 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: @@ -20813,7 +21282,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.pmf(value, name='pmf')` {#QuantizedDistribution.pmf} +#### `tf.contrib.distributions.QuantizedDistribution.pmf(value, name='pmf', **condition_kwargs)` {#QuantizedDistribution.pmf} Probability mass function. @@ -20822,6 +21291,7 @@ 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: @@ -20837,7 +21307,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob')` {#QuantizedDistribution.prob} +#### `tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob', **condition_kwargs)` {#QuantizedDistribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -20864,6 +21334,7 @@ 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: @@ -20874,7 +21345,7 @@ also be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#QuantizedDistribution.sample} +#### `tf.contrib.distributions.QuantizedDistribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#QuantizedDistribution.sample} Generate samples of the specified shape. @@ -20887,6 +21358,7 @@ 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: @@ -20896,7 +21368,7 @@ sample. - - - -#### `tf.contrib.distributions.QuantizedDistribution.sample_n(n, seed=None, name='sample_n')` {#QuantizedDistribution.sample_n} +#### `tf.contrib.distributions.QuantizedDistribution.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#QuantizedDistribution.sample_n} Generate `n` samples. @@ -20907,6 +21379,7 @@ Generate `n` samples. observations to sample. * <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: @@ -20928,7 +21401,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.QuantizedDistribution.survival_function(value, name='survival_function')` {#QuantizedDistribution.survival_function} +#### `tf.contrib.distributions.QuantizedDistribution.survival_function(value, name='survival_function', **condition_kwargs)` {#QuantizedDistribution.survival_function} Survival function. @@ -20963,6 +21436,7 @@ 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: @@ -21097,7 +21571,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Mixture.cdf(value, name='cdf')` {#Mixture.cdf} +#### `tf.contrib.distributions.Mixture.cdf(value, name='cdf', **condition_kwargs)` {#Mixture.cdf} Cumulative distribution function. @@ -21112,6 +21586,7 @@ 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: @@ -21138,7 +21613,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Mixture.entropy(name='entropy')` {#Mixture.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -21248,7 +21723,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Mixture.log_cdf(value, name='log_cdf')` {#Mixture.log_cdf} +#### `tf.contrib.distributions.Mixture.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Mixture.log_cdf} Log cumulative distribution function. @@ -21267,6 +21742,7 @@ 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: @@ -21277,7 +21753,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Mixture.log_pdf(value, name='log_pdf')` {#Mixture.log_pdf} +#### `tf.contrib.distributions.Mixture.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Mixture.log_pdf} Log probability density function. @@ -21286,6 +21762,7 @@ 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: @@ -21301,7 +21778,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf')` {#Mixture.log_pmf} +#### `tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Mixture.log_pmf} Log probability mass function. @@ -21310,6 +21787,7 @@ 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: @@ -21325,7 +21803,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Mixture.log_prob(value, name='log_prob')` {#Mixture.log_prob} +#### `tf.contrib.distributions.Mixture.log_prob(value, name='log_prob', **condition_kwargs)` {#Mixture.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -21334,6 +21812,7 @@ 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: @@ -21344,7 +21823,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Mixture.log_survival_function(value, name='log_survival_function')` {#Mixture.log_survival_function} +#### `tf.contrib.distributions.Mixture.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Mixture.log_survival_function} Log survival function. @@ -21364,6 +21843,7 @@ 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: @@ -21450,7 +21930,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Mixture.pdf(value, name='pdf')` {#Mixture.pdf} +#### `tf.contrib.distributions.Mixture.pdf(value, name='pdf', **condition_kwargs)` {#Mixture.pdf} Probability density function. @@ -21459,6 +21939,7 @@ 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: @@ -21474,7 +21955,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Mixture.pmf(value, name='pmf')` {#Mixture.pmf} +#### `tf.contrib.distributions.Mixture.pmf(value, name='pmf', **condition_kwargs)` {#Mixture.pmf} Probability mass function. @@ -21483,6 +21964,7 @@ 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: @@ -21498,7 +21980,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Mixture.prob(value, name='prob')` {#Mixture.prob} +#### `tf.contrib.distributions.Mixture.prob(value, name='prob', **condition_kwargs)` {#Mixture.prob} Probability density/mass function (depending on `is_continuous`). @@ -21507,6 +21989,7 @@ 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: @@ -21517,7 +22000,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Mixture.sample(sample_shape=(), seed=None, name='sample')` {#Mixture.sample} +#### `tf.contrib.distributions.Mixture.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Mixture.sample} Generate samples of the specified shape. @@ -21530,6 +22013,7 @@ 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: @@ -21539,7 +22023,7 @@ sample. - - - -#### `tf.contrib.distributions.Mixture.sample_n(n, seed=None, name='sample_n')` {#Mixture.sample_n} +#### `tf.contrib.distributions.Mixture.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Mixture.sample_n} Generate `n` samples. @@ -21550,6 +22034,7 @@ Generate `n` samples. observations to sample. * <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: @@ -21571,7 +22056,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Mixture.survival_function(value, name='survival_function')` {#Mixture.survival_function} +#### `tf.contrib.distributions.Mixture.survival_function(value, name='survival_function', **condition_kwargs)` {#Mixture.survival_function} Survival function. @@ -21588,6 +22073,7 @@ 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/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md index f05cdbab6c..e303de1647 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.learn.md +++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md @@ -85,7 +85,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). @@ -391,7 +391,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). @@ -670,9 +670,9 @@ Initializes a DNNClassifier instance. * <b>`feature_columns`</b>: An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from `FeatureColumn`. -* <b>`model_dir`</b>: Directory to save model parameters, graph and etc. This can also - be used to load checkpoints from the directory into a estimator to continue - training a previously saved model. +* <b>`model_dir`</b>: Directory to save model parameters, graph and etc. This can + also be used to load checkpoints from the directory into a estimator to + continue training a previously saved model. * <b>`n_classes`</b>: number of target classes. Default is binary classification. It must be greater than 1. * <b>`weight_column_name`</b>: A string defining feature column name representing @@ -915,9 +915,9 @@ Initializes a `DNNRegressor` instance. * <b>`feature_columns`</b>: An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from `FeatureColumn`. -* <b>`model_dir`</b>: Directory to save model parameters, graph and etc. This can also - be used to load checkpoints from the directory into a estimator to continue - training a previously saved model. +* <b>`model_dir`</b>: Directory to save model parameters, graph and etc. This can + also be used to load checkpoints from the directory into a estimator to + continue training a previously saved model. * <b>`weight_column_name`</b>: A string defining feature column name representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. @@ -1025,7 +1025,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). @@ -1362,7 +1362,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). @@ -2003,7 +2003,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). @@ -2354,7 +2354,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). @@ -2751,7 +2751,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). 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 e9b11e4b4e..eba50999b7 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')` {#Bernoulli.cdf} +#### `tf.contrib.distributions.Bernoulli.cdf(value, name='cdf', **condition_kwargs)` {#Bernoulli.cdf} Cumulative distribution function. @@ -93,6 +93,7 @@ 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: @@ -112,7 +113,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Bernoulli.entropy(name='entropy')` {#Bernoulli.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -176,7 +177,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf')` {#Bernoulli.log_cdf} +#### `tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Bernoulli.log_cdf} Log cumulative distribution function. @@ -195,6 +196,7 @@ 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: @@ -205,7 +207,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf')` {#Bernoulli.log_pdf} +#### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Bernoulli.log_pdf} Log probability density function. @@ -214,6 +216,7 @@ 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: @@ -229,7 +232,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf')` {#Bernoulli.log_pmf} +#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Bernoulli.log_pmf} Log probability mass function. @@ -238,6 +241,7 @@ 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: @@ -253,7 +257,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob')` {#Bernoulli.log_prob} +#### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob', **condition_kwargs)` {#Bernoulli.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -262,6 +266,7 @@ 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: @@ -272,7 +277,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Bernoulli.log_survival_function(value, name='log_survival_function')` {#Bernoulli.log_survival_function} +#### `tf.contrib.distributions.Bernoulli.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Bernoulli.log_survival_function} Log survival function. @@ -292,6 +297,7 @@ 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: @@ -389,7 +395,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf')` {#Bernoulli.pdf} +#### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf', **condition_kwargs)` {#Bernoulli.pdf} Probability density function. @@ -398,6 +404,7 @@ 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: @@ -413,7 +420,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf')` {#Bernoulli.pmf} +#### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf', **condition_kwargs)` {#Bernoulli.pmf} Probability mass function. @@ -422,6 +429,7 @@ 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: @@ -437,7 +445,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Bernoulli.prob(value, name='prob')` {#Bernoulli.prob} +#### `tf.contrib.distributions.Bernoulli.prob(value, name='prob', **condition_kwargs)` {#Bernoulli.prob} Probability density/mass function (depending on `is_continuous`). @@ -446,6 +454,7 @@ 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: @@ -463,7 +472,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample')` {#Bernoulli.sample} +#### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Bernoulli.sample} Generate samples of the specified shape. @@ -476,6 +485,7 @@ 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: @@ -485,7 +495,7 @@ sample. - - - -#### `tf.contrib.distributions.Bernoulli.sample_n(n, seed=None, name='sample_n')` {#Bernoulli.sample_n} +#### `tf.contrib.distributions.Bernoulli.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Bernoulli.sample_n} Generate `n` samples. @@ -496,6 +506,7 @@ Generate `n` samples. observations to sample. * <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: @@ -517,7 +528,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Bernoulli.survival_function(value, name='survival_function')` {#Bernoulli.survival_function} +#### `tf.contrib.distributions.Bernoulli.survival_function(value, name='survival_function', **condition_kwargs)` {#Bernoulli.survival_function} Survival function. @@ -534,6 +545,7 @@ 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 6208ff3862..2f605addc7 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')` {#Chi2WithAbsDf.cdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.cdf(value, name='cdf', **condition_kwargs)` {#Chi2WithAbsDf.cdf} Cumulative distribution function. @@ -78,6 +78,7 @@ 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: @@ -104,7 +105,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Chi2WithAbsDf.entropy(name='entropy')` {#Chi2WithAbsDf.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -179,7 +180,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_cdf(value, name='log_cdf')` {#Chi2WithAbsDf.log_cdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Chi2WithAbsDf.log_cdf} Log cumulative distribution function. @@ -198,6 +199,7 @@ 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: @@ -208,7 +210,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf')` {#Chi2WithAbsDf.log_pdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Chi2WithAbsDf.log_pdf} Log probability density function. @@ -217,6 +219,7 @@ 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: @@ -232,7 +235,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf')` {#Chi2WithAbsDf.log_pmf} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Chi2WithAbsDf.log_pmf} Log probability mass function. @@ -241,6 +244,7 @@ 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: @@ -256,7 +260,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob')` {#Chi2WithAbsDf.log_prob} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob', **condition_kwargs)` {#Chi2WithAbsDf.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -265,6 +269,7 @@ 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: @@ -275,7 +280,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.log_survival_function(value, name='log_survival_function')` {#Chi2WithAbsDf.log_survival_function} +#### `tf.contrib.distributions.Chi2WithAbsDf.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Chi2WithAbsDf.log_survival_function} Log survival function. @@ -295,6 +300,7 @@ 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: @@ -380,7 +386,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf')` {#Chi2WithAbsDf.pdf} +#### `tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf', **condition_kwargs)` {#Chi2WithAbsDf.pdf} Probability density function. @@ -389,6 +395,7 @@ 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: @@ -404,7 +411,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf')` {#Chi2WithAbsDf.pmf} +#### `tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf', **condition_kwargs)` {#Chi2WithAbsDf.pmf} Probability mass function. @@ -413,6 +420,7 @@ 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: @@ -428,7 +436,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob')` {#Chi2WithAbsDf.prob} +#### `tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob', **condition_kwargs)` {#Chi2WithAbsDf.prob} Probability density/mass function (depending on `is_continuous`). @@ -437,6 +445,7 @@ 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: @@ -447,7 +456,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample')` {#Chi2WithAbsDf.sample} +#### `tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Chi2WithAbsDf.sample} Generate samples of the specified shape. @@ -460,6 +469,7 @@ 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: @@ -469,7 +479,7 @@ sample. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.sample_n(n, seed=None, name='sample_n')` {#Chi2WithAbsDf.sample_n} +#### `tf.contrib.distributions.Chi2WithAbsDf.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Chi2WithAbsDf.sample_n} Generate `n` samples. @@ -485,6 +495,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -506,7 +517,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function')` {#Chi2WithAbsDf.survival_function} +#### `tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function', **condition_kwargs)` {#Chi2WithAbsDf.survival_function} Survival function. @@ -523,6 +534,7 @@ 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 1a3f86d7b8..0e749b855b 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')` {#Dirichlet.cdf} +#### `tf.contrib.distributions.Dirichlet.cdf(value, name='cdf', **condition_kwargs)` {#Dirichlet.cdf} Cumulative distribution function. @@ -165,6 +165,7 @@ 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: @@ -184,7 +185,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Dirichlet.entropy(name='entropy')` {#Dirichlet.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -248,7 +249,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Dirichlet.log_cdf(value, name='log_cdf')` {#Dirichlet.log_cdf} +#### `tf.contrib.distributions.Dirichlet.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Dirichlet.log_cdf} Log cumulative distribution function. @@ -267,6 +268,7 @@ 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: @@ -277,7 +279,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf')` {#Dirichlet.log_pdf} +#### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Dirichlet.log_pdf} Log probability density function. @@ -286,6 +288,7 @@ 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: @@ -301,7 +304,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf')` {#Dirichlet.log_pmf} +#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Dirichlet.log_pmf} Log probability mass function. @@ -310,6 +313,7 @@ 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: @@ -325,7 +329,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob')` {#Dirichlet.log_prob} +#### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob', **condition_kwargs)` {#Dirichlet.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -342,6 +346,7 @@ 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: @@ -352,7 +357,7 @@ 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')` {#Dirichlet.log_survival_function} +#### `tf.contrib.distributions.Dirichlet.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Dirichlet.log_survival_function} Log survival function. @@ -372,6 +377,7 @@ 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: @@ -458,7 +464,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf')` {#Dirichlet.pdf} +#### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf', **condition_kwargs)` {#Dirichlet.pdf} Probability density function. @@ -467,6 +473,7 @@ 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 +489,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf')` {#Dirichlet.pmf} +#### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf', **condition_kwargs)` {#Dirichlet.pmf} Probability mass function. @@ -491,6 +498,7 @@ 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: @@ -506,7 +514,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Dirichlet.prob(value, name='prob')` {#Dirichlet.prob} +#### `tf.contrib.distributions.Dirichlet.prob(value, name='prob', **condition_kwargs)` {#Dirichlet.prob} Probability density/mass function (depending on `is_continuous`). @@ -523,6 +531,7 @@ 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: @@ -533,7 +542,7 @@ in `self.alpha`. `x` is only legal if it sums up to one. - - - -#### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample')` {#Dirichlet.sample} +#### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Dirichlet.sample} Generate samples of the specified shape. @@ -546,6 +555,7 @@ 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: @@ -555,7 +565,7 @@ sample. - - - -#### `tf.contrib.distributions.Dirichlet.sample_n(n, seed=None, name='sample_n')` {#Dirichlet.sample_n} +#### `tf.contrib.distributions.Dirichlet.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Dirichlet.sample_n} Generate `n` samples. @@ -566,6 +576,7 @@ Generate `n` samples. observations to sample. * <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: @@ -587,7 +598,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Dirichlet.survival_function(value, name='survival_function')` {#Dirichlet.survival_function} +#### `tf.contrib.distributions.Dirichlet.survival_function(value, name='survival_function', **condition_kwargs)` {#Dirichlet.survival_function} Survival function. @@ -604,6 +615,7 @@ 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 2ecf7194af..23e5ebdeba 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 @@ -5,7 +5,7 @@ A generic probability distribution base class. ### Subclassing -Subclasess are expected to implement a leading-underscore version of the +Subclasses are expected to implement a leading-underscore version of the same-named function. The argument signature should be identical except for the omission of `name="..."`. For example, to enable `log_prob(value, name="log_prob")` a subclass should implement `_log_prob(value)`. @@ -182,7 +182,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.Distribution.cdf(value, name='cdf')` {#Distribution.cdf} +#### `tf.contrib.distributions.Distribution.cdf(value, name='cdf', **condition_kwargs)` {#Distribution.cdf} Cumulative distribution function. @@ -197,6 +197,7 @@ 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: @@ -216,7 +217,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Distribution.entropy(name='entropy')` {#Distribution.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -280,7 +281,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Distribution.log_cdf(value, name='log_cdf')` {#Distribution.log_cdf} +#### `tf.contrib.distributions.Distribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Distribution.log_cdf} Log cumulative distribution function. @@ -299,6 +300,7 @@ 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: @@ -309,7 +311,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf')` {#Distribution.log_pdf} +#### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Distribution.log_pdf} Log probability density function. @@ -318,6 +320,7 @@ 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: @@ -333,7 +336,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf')` {#Distribution.log_pmf} +#### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Distribution.log_pmf} Log probability mass function. @@ -342,6 +345,7 @@ 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: @@ -357,7 +361,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob')` {#Distribution.log_prob} +#### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob', **condition_kwargs)` {#Distribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -366,6 +370,7 @@ 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: @@ -376,7 +381,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Distribution.log_survival_function(value, name='log_survival_function')` {#Distribution.log_survival_function} +#### `tf.contrib.distributions.Distribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Distribution.log_survival_function} Log survival function. @@ -396,6 +401,7 @@ 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: @@ -475,7 +481,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Distribution.pdf(value, name='pdf')` {#Distribution.pdf} +#### `tf.contrib.distributions.Distribution.pdf(value, name='pdf', **condition_kwargs)` {#Distribution.pdf} Probability density function. @@ -484,6 +490,7 @@ 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: @@ -499,7 +506,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Distribution.pmf(value, name='pmf')` {#Distribution.pmf} +#### `tf.contrib.distributions.Distribution.pmf(value, name='pmf', **condition_kwargs)` {#Distribution.pmf} Probability mass function. @@ -508,6 +515,7 @@ 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: @@ -523,7 +531,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Distribution.prob(value, name='prob')` {#Distribution.prob} +#### `tf.contrib.distributions.Distribution.prob(value, name='prob', **condition_kwargs)` {#Distribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -532,6 +540,7 @@ 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: @@ -542,7 +551,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample')` {#Distribution.sample} +#### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Distribution.sample} Generate samples of the specified shape. @@ -555,6 +564,7 @@ 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: @@ -564,7 +574,7 @@ sample. - - - -#### `tf.contrib.distributions.Distribution.sample_n(n, seed=None, name='sample_n')` {#Distribution.sample_n} +#### `tf.contrib.distributions.Distribution.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Distribution.sample_n} Generate `n` samples. @@ -575,6 +585,7 @@ Generate `n` samples. observations to sample. * <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: @@ -596,7 +607,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Distribution.survival_function(value, name='survival_function')` {#Distribution.survival_function} +#### `tf.contrib.distributions.Distribution.survival_function(value, name='survival_function', **condition_kwargs)` {#Distribution.survival_function} Survival function. @@ -613,6 +624,7 @@ 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 c0451a17ef..24576be51c 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')` {#MultivariateNormalCholesky.cdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalCholesky.cdf} Cumulative distribution function. @@ -134,6 +134,7 @@ 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: @@ -153,7 +154,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.MultivariateNormalCholesky.entropy(name='entropy')` {#MultivariateNormalCholesky.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -217,7 +218,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf')` {#MultivariateNormalCholesky.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalCholesky.log_cdf} Log cumulative distribution function. @@ -236,6 +237,7 @@ 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: @@ -246,7 +248,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf')` {#MultivariateNormalCholesky.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalCholesky.log_pdf} Log probability density function. @@ -255,6 +257,7 @@ 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: @@ -270,7 +273,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf')` {#MultivariateNormalCholesky.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalCholesky.log_pmf} Log probability mass function. @@ -279,6 +282,7 @@ 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: @@ -294,7 +298,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob')` {#MultivariateNormalCholesky.log_prob} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -319,6 +323,7 @@ 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: @@ -336,7 +341,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalCholesky.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalCholesky.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalCholesky.log_survival_function} Log survival function. @@ -356,6 +361,7 @@ 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: @@ -442,7 +448,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf')` {#MultivariateNormalCholesky.pdf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalCholesky.pdf} Probability density function. @@ -451,6 +457,7 @@ 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 +473,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf')` {#MultivariateNormalCholesky.pmf} +#### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalCholesky.pmf} Probability mass function. @@ -475,6 +482,7 @@ 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 +498,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob')` {#MultivariateNormalCholesky.prob} +#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalCholesky.prob} Probability density/mass function (depending on `is_continuous`). @@ -515,6 +523,7 @@ 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: @@ -525,7 +534,7 @@ or - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalCholesky.sample} +#### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalCholesky.sample} Generate samples of the specified shape. @@ -538,6 +547,7 @@ 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: @@ -547,7 +557,7 @@ sample. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalCholesky.sample_n} +#### `tf.contrib.distributions.MultivariateNormalCholesky.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#MultivariateNormalCholesky.sample_n} Generate `n` samples. @@ -558,6 +568,7 @@ Generate `n` samples. observations to sample. * <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: @@ -593,7 +604,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalCholesky.survival_function(value, name='survival_function')` {#MultivariateNormalCholesky.survival_function} +#### `tf.contrib.distributions.MultivariateNormalCholesky.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalCholesky.survival_function} Survival function. @@ -610,6 +621,7 @@ 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.learn.LinearRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md index 75bb09740b..e2243985b8 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md @@ -163,7 +163,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). 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 9ce8038ffb..9f659969d6 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 @@ -118,7 +118,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.cdf(value, name='cdf')` {#MultivariateNormalDiag.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiag.cdf} Cumulative distribution function. @@ -133,6 +133,7 @@ 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: @@ -152,7 +153,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.MultivariateNormalDiag.entropy(name='entropy')` {#MultivariateNormalDiag.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -216,7 +217,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiag.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiag.log_cdf} Log cumulative distribution function. @@ -235,6 +236,7 @@ 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: @@ -245,7 +247,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiag.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiag.log_pdf} Log probability density function. @@ -254,6 +256,7 @@ 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: @@ -269,7 +272,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiag.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiag.log_pmf} Log probability mass function. @@ -278,6 +281,7 @@ 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: @@ -293,7 +297,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob')` {#MultivariateNormalDiag.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiag.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -318,6 +322,7 @@ 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: @@ -335,7 +340,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiag.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiag.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiag.log_survival_function} Log survival function. @@ -355,6 +360,7 @@ 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 +447,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf')` {#MultivariateNormalDiag.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiag.pdf} Probability density function. @@ -450,6 +456,7 @@ 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 +472,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf')` {#MultivariateNormalDiag.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiag.pmf} Probability mass function. @@ -474,6 +481,7 @@ 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: @@ -489,7 +497,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob')` {#MultivariateNormalDiag.prob} +#### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiag.prob} Probability density/mass function (depending on `is_continuous`). @@ -514,6 +522,7 @@ 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: @@ -524,7 +533,7 @@ or - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiag.sample} +#### `tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiag.sample} Generate samples of the specified shape. @@ -537,6 +546,7 @@ 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: @@ -546,7 +556,7 @@ sample. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalDiag.sample_n} +#### `tf.contrib.distributions.MultivariateNormalDiag.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#MultivariateNormalDiag.sample_n} Generate `n` samples. @@ -557,6 +567,7 @@ Generate `n` samples. observations to sample. * <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: @@ -592,7 +603,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiag.survival_function(value, name='survival_function')` {#MultivariateNormalDiag.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiag.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiag.survival_function} Survival function. @@ -609,6 +620,7 @@ 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 0182517d09..cdc4362531 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 @@ -135,7 +135,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.QuantizedDistribution.cdf(value, name='cdf')` {#QuantizedDistribution.cdf} +#### `tf.contrib.distributions.QuantizedDistribution.cdf(value, name='cdf', **condition_kwargs)` {#QuantizedDistribution.cdf} Cumulative distribution function. @@ -168,6 +168,7 @@ 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: @@ -187,7 +188,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.QuantizedDistribution.entropy(name='entropy')` {#QuantizedDistribution.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -251,7 +252,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_cdf(value, name='log_cdf')` {#QuantizedDistribution.log_cdf} +#### `tf.contrib.distributions.QuantizedDistribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#QuantizedDistribution.log_cdf} Log cumulative distribution function. @@ -288,6 +289,7 @@ 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: @@ -298,7 +300,7 @@ The base distribution's `log_cdf` method must be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pdf(value, name='log_pdf')` {#QuantizedDistribution.log_pdf} +#### `tf.contrib.distributions.QuantizedDistribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#QuantizedDistribution.log_pdf} Log probability density function. @@ -307,6 +309,7 @@ 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: @@ -322,7 +325,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_pmf(value, name='log_pmf')` {#QuantizedDistribution.log_pmf} +#### `tf.contrib.distributions.QuantizedDistribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#QuantizedDistribution.log_pmf} Log probability mass function. @@ -331,6 +334,7 @@ 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: @@ -346,7 +350,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob')` {#QuantizedDistribution.log_prob} +#### `tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob', **condition_kwargs)` {#QuantizedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -373,6 +377,7 @@ 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: @@ -383,7 +388,7 @@ must also be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.log_survival_function(value, name='log_survival_function')` {#QuantizedDistribution.log_survival_function} +#### `tf.contrib.distributions.QuantizedDistribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#QuantizedDistribution.log_survival_function} Log survival function. @@ -421,6 +426,7 @@ 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: @@ -500,7 +506,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.pdf(value, name='pdf')` {#QuantizedDistribution.pdf} +#### `tf.contrib.distributions.QuantizedDistribution.pdf(value, name='pdf', **condition_kwargs)` {#QuantizedDistribution.pdf} Probability density function. @@ -509,6 +515,7 @@ 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: @@ -524,7 +531,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.pmf(value, name='pmf')` {#QuantizedDistribution.pmf} +#### `tf.contrib.distributions.QuantizedDistribution.pmf(value, name='pmf', **condition_kwargs)` {#QuantizedDistribution.pmf} Probability mass function. @@ -533,6 +540,7 @@ 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: @@ -548,7 +556,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob')` {#QuantizedDistribution.prob} +#### `tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob', **condition_kwargs)` {#QuantizedDistribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -575,6 +583,7 @@ 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: @@ -585,7 +594,7 @@ also be defined on `y - 1`. - - - -#### `tf.contrib.distributions.QuantizedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#QuantizedDistribution.sample} +#### `tf.contrib.distributions.QuantizedDistribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#QuantizedDistribution.sample} Generate samples of the specified shape. @@ -598,6 +607,7 @@ 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: @@ -607,7 +617,7 @@ sample. - - - -#### `tf.contrib.distributions.QuantizedDistribution.sample_n(n, seed=None, name='sample_n')` {#QuantizedDistribution.sample_n} +#### `tf.contrib.distributions.QuantizedDistribution.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#QuantizedDistribution.sample_n} Generate `n` samples. @@ -618,6 +628,7 @@ Generate `n` samples. observations to sample. * <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 +650,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.QuantizedDistribution.survival_function(value, name='survival_function')` {#QuantizedDistribution.survival_function} +#### `tf.contrib.distributions.QuantizedDistribution.survival_function(value, name='survival_function', **condition_kwargs)` {#QuantizedDistribution.survival_function} Survival function. @@ -674,6 +685,7 @@ 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 18971e4429..84b0c01067 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 @@ -121,7 +121,7 @@ independent distributions of this kind the instance represents. - - - -#### `tf.contrib.distributions.StudentT.cdf(value, name='cdf')` {#StudentT.cdf} +#### `tf.contrib.distributions.StudentT.cdf(value, name='cdf', **condition_kwargs)` {#StudentT.cdf} Cumulative distribution function. @@ -136,6 +136,7 @@ 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: @@ -162,7 +163,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.StudentT.entropy(name='entropy')` {#StudentT.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -226,7 +227,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf')` {#StudentT.log_cdf} +#### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf', **condition_kwargs)` {#StudentT.log_cdf} Log cumulative distribution function. @@ -245,6 +246,7 @@ 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: @@ -255,7 +257,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf')` {#StudentT.log_pdf} +#### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf', **condition_kwargs)` {#StudentT.log_pdf} Log probability density function. @@ -264,6 +266,7 @@ 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: @@ -279,7 +282,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf')` {#StudentT.log_pmf} +#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf', **condition_kwargs)` {#StudentT.log_pmf} Log probability mass function. @@ -288,6 +291,7 @@ 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 +307,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob')` {#StudentT.log_prob} +#### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob', **condition_kwargs)` {#StudentT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -312,6 +316,7 @@ 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: @@ -322,7 +327,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentT.log_survival_function(value, name='log_survival_function')` {#StudentT.log_survival_function} +#### `tf.contrib.distributions.StudentT.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#StudentT.log_survival_function} Log survival function. @@ -342,6 +347,7 @@ 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: @@ -434,7 +440,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.StudentT.pdf(value, name='pdf')` {#StudentT.pdf} +#### `tf.contrib.distributions.StudentT.pdf(value, name='pdf', **condition_kwargs)` {#StudentT.pdf} Probability density function. @@ -443,6 +449,7 @@ 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: @@ -458,7 +465,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.StudentT.pmf(value, name='pmf')` {#StudentT.pmf} +#### `tf.contrib.distributions.StudentT.pmf(value, name='pmf', **condition_kwargs)` {#StudentT.pmf} Probability mass function. @@ -467,6 +474,7 @@ 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: @@ -482,7 +490,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.StudentT.prob(value, name='prob')` {#StudentT.prob} +#### `tf.contrib.distributions.StudentT.prob(value, name='prob', **condition_kwargs)` {#StudentT.prob} Probability density/mass function (depending on `is_continuous`). @@ -491,6 +499,7 @@ 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: @@ -501,7 +510,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample')` {#StudentT.sample} +#### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#StudentT.sample} Generate samples of the specified shape. @@ -514,6 +523,7 @@ 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: @@ -523,7 +533,7 @@ sample. - - - -#### `tf.contrib.distributions.StudentT.sample_n(n, seed=None, name='sample_n')` {#StudentT.sample_n} +#### `tf.contrib.distributions.StudentT.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#StudentT.sample_n} Generate `n` samples. @@ -534,6 +544,7 @@ Generate `n` samples. observations to sample. * <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: @@ -562,7 +573,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.StudentT.survival_function(value, name='survival_function')` {#StudentT.survival_function} +#### `tf.contrib.distributions.StudentT.survival_function(value, name='survival_function', **condition_kwargs)` {#StudentT.survival_function} Survival function. @@ -579,6 +590,7 @@ 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 2ac3000749..fc76841477 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 @@ -1,10 +1,16 @@ A Transformed Distribution. -A Transformed Distribution models `p(y)` given a base distribution `p(x)`, and -a deterministic, invertible, differentiable transform, `Y = g(X)`. The +A `TransformedDistribution` models `p(y)` given a base distribution `p(x)`, +and a deterministic, invertible, differentiable transform, `Y = g(X)`. The transform is typically an instance of the `Bijector` class and the base distribution is typically an instance of the `Distribution` class. +A `Bijector` is expected to implement the following functions: +- `forward`, +- `inverse`, +- `inverse_log_det_jacobian`. +The semantics of these functions are outlined in the `Bijector` documentation. + Shapes, type, and reparameterization are taken from the base distribution. Write `P(Y=y)` for cumulative density function of random variable (rv) `Y` and @@ -18,7 +24,7 @@ associated with rv `X` in the following ways: Mathematically: - ``` + ```none Y = g(X) ``` @@ -32,7 +38,7 @@ associated with rv `X` in the following ways: Mathematically: - ``` + ```none (log o p o g^{-1})(y) + (log o det o J o g^{-1})(y) ``` @@ -47,7 +53,7 @@ associated with rv `X` in the following ways: Mathematically: - ``` + ```none (log o P o g^{-1})(y) ``` @@ -66,7 +72,7 @@ distribution: ```python ds = tf.contrib.distributions log_normal = ds.TransformedDistribution( - base_distribution=ds.Normal(mu=mu, sigma=sigma), + distribution=ds.Normal(mu=mu, sigma=sigma), bijector=ds.bijector.Exp(), name="LogNormalTransformedDistribution") ``` @@ -76,7 +82,7 @@ A `LogNormal` made from callables: ```python ds = tf.contrib.distributions log_normal = ds.TransformedDistribution( - base_distribution=ds.Normal(mu=mu, sigma=sigma), + distribution=ds.Normal(mu=mu, sigma=sigma), bijector=ds.bijector.Inline( forward_fn=tf.exp, inverse_fn=tf.log, @@ -90,24 +96,25 @@ Another example constructing a Normal from a StandardNormal: ```python ds = tf.contrib.distributions normal = ds.TransformedDistribution( - base_distribution=ds.Normal(mu=0, sigma=1), + distribution=ds.Normal(mu=0, sigma=1), bijector=ds.bijector.ScaleAndShift(loc=mu, scale=sigma, event_ndims=0), name="NormalTransformedDistribution") ``` - - - -#### `tf.contrib.distributions.TransformedDistribution.__init__(base_distribution, bijector, name='TransformedDistribution')` {#TransformedDistribution.__init__} +#### `tf.contrib.distributions.TransformedDistribution.__init__(distribution, bijector, name=None)` {#TransformedDistribution.__init__} Construct a Transformed Distribution. ##### Args: -* <b>`base_distribution`</b>: The base distribution class to transform. Typically an +* <b>`distribution`</b>: The base distribution class to transform. Typically an instance of `Distribution`. * <b>`bijector`</b>: The object responsible for calculating the transformation. Typically an instance of `Bijector`. -* <b>`name`</b>: The name for the distribution. +* <b>`name`</b>: The name for the distribution. Default: + `bijector.name + distribution.name`. - - - @@ -133,13 +140,6 @@ undefined. - - - -#### `tf.contrib.distributions.TransformedDistribution.base_distribution` {#TransformedDistribution.base_distribution} - -Base distribution, p(x). - - -- - - - #### `tf.contrib.distributions.TransformedDistribution.batch_shape(name='batch_shape')` {#TransformedDistribution.batch_shape} Shape of a single sample from a single event index as a 1-D `Tensor`. @@ -167,7 +167,7 @@ Function transforming x => y. - - - -#### `tf.contrib.distributions.TransformedDistribution.cdf(value, name='cdf')` {#TransformedDistribution.cdf} +#### `tf.contrib.distributions.TransformedDistribution.cdf(value, name='cdf', **condition_kwargs)` {#TransformedDistribution.cdf} Cumulative distribution function. @@ -177,11 +177,20 @@ Given random variable `X`, the cumulative distribution function `cdf` is: cdf(x) := P[X <= x] ``` + +Additional documentation from `TransformedDistribution`: + +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. + ##### 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: @@ -192,6 +201,13 @@ cdf(x) := P[X <= x] - - - +#### `tf.contrib.distributions.TransformedDistribution.distribution` {#TransformedDistribution.distribution} + +Base distribution, p(x). + + +- - - + #### `tf.contrib.distributions.TransformedDistribution.dtype` {#TransformedDistribution.dtype} The `DType` of `Tensor`s handled by this `Distribution`. @@ -201,7 +217,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.TransformedDistribution.entropy(name='entropy')` {#TransformedDistribution.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -265,7 +281,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf')` {#TransformedDistribution.log_cdf} +#### `tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf', **condition_kwargs)` {#TransformedDistribution.log_cdf} Log cumulative distribution function. @@ -279,11 +295,20 @@ 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`: + +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. + ##### 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: @@ -294,7 +319,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf')` {#TransformedDistribution.log_pdf} +#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf', **condition_kwargs)` {#TransformedDistribution.log_pdf} Log probability density function. @@ -303,6 +328,7 @@ 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: @@ -318,7 +344,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf')` {#TransformedDistribution.log_pmf} +#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf', **condition_kwargs)` {#TransformedDistribution.log_pmf} Log probability mass function. @@ -327,6 +353,7 @@ 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 +369,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob')` {#TransformedDistribution.log_prob} +#### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob', **condition_kwargs)` {#TransformedDistribution.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -350,16 +377,22 @@ Log probability density/mass function (depending on `is_continuous`). Additional documentation from `TransformedDistribution`: Implements `(log o p o g^{-1})(y) + (log o det o J o g^{-1})(y)`, -where `g^{-1}` is the inverse of `transform`. + 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`. -Also raises a `ValueError` if `inverse` was not provided to the -distribution and `y` was not returned from `sample`. +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. ##### 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 +403,7 @@ distribution and `y` was not returned from `sample`. - - - -#### `tf.contrib.distributions.TransformedDistribution.log_survival_function(value, name='log_survival_function')` {#TransformedDistribution.log_survival_function} +#### `tf.contrib.distributions.TransformedDistribution.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#TransformedDistribution.log_survival_function} Log survival function. @@ -385,11 +418,20 @@ 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`: + +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. + ##### 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: @@ -469,7 +511,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf')` {#TransformedDistribution.pdf} +#### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf', **condition_kwargs)` {#TransformedDistribution.pdf} Probability density function. @@ -478,6 +520,7 @@ 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: @@ -493,7 +536,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf')` {#TransformedDistribution.pmf} +#### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf', **condition_kwargs)` {#TransformedDistribution.pmf} Probability mass function. @@ -502,6 +545,7 @@ 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: @@ -517,7 +561,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob')` {#TransformedDistribution.prob} +#### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob', **condition_kwargs)` {#TransformedDistribution.prob} Probability density/mass function (depending on `is_continuous`). @@ -525,16 +569,22 @@ 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`. + inverse of `transform`. + + Also raises a `ValueError` if `inverse` was not provided to the + distribution and `y` was not returned from `sample`. + +##### <b>`condition_kwargs`</b>: -Also raises a `ValueError` if `inverse` was not provided to the -distribution and `y` was not returned from `sample`. +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. ##### 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: @@ -545,7 +595,7 @@ distribution and `y` was not returned from `sample`. - - - -#### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#TransformedDistribution.sample} +#### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#TransformedDistribution.sample} Generate samples of the specified shape. @@ -558,6 +608,7 @@ 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: @@ -567,7 +618,7 @@ sample. - - - -#### `tf.contrib.distributions.TransformedDistribution.sample_n(n, seed=None, name='sample_n')` {#TransformedDistribution.sample_n} +#### `tf.contrib.distributions.TransformedDistribution.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#TransformedDistribution.sample_n} Generate `n` samples. @@ -575,7 +626,12 @@ Generate `n` samples. Additional documentation from `TransformedDistribution`: Samples from the base distribution and then passes through -the bijector's forward transform. + the bijector's forward transform. + +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. ##### Args: @@ -584,6 +640,7 @@ the bijector's forward transform. observations to sample. * <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 +662,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.TransformedDistribution.survival_function(value, name='survival_function')` {#TransformedDistribution.survival_function} +#### `tf.contrib.distributions.TransformedDistribution.survival_function(value, name='survival_function', **condition_kwargs)` {#TransformedDistribution.survival_function} Survival function. @@ -617,11 +674,20 @@ survival_function(x) = P[X > x] = 1 - cdf(x). ``` + +Additional documentation from `TransformedDistribution`: + +##### <b>`condition_kwargs`</b>: + +* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution. +* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector. + ##### 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 7d3f2a3a25..0e15dca5bc 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')` {#Categorical.cdf} +#### `tf.contrib.distributions.Categorical.cdf(value, name='cdf', **condition_kwargs)` {#Categorical.cdf} Cumulative distribution function. @@ -124,6 +124,7 @@ 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: @@ -143,7 +144,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Categorical.entropy(name='entropy')` {#Categorical.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -207,7 +208,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf')` {#Categorical.log_cdf} +#### `tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Categorical.log_cdf} Log cumulative distribution function. @@ -226,6 +227,7 @@ 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: @@ -236,7 +238,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf')` {#Categorical.log_pdf} +#### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Categorical.log_pdf} Log probability density function. @@ -245,6 +247,7 @@ 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 +263,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf')` {#Categorical.log_pmf} +#### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Categorical.log_pmf} Log probability mass function. @@ -269,6 +272,7 @@ 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: @@ -284,7 +288,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob')` {#Categorical.log_prob} +#### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob', **condition_kwargs)` {#Categorical.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -293,6 +297,7 @@ 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: @@ -303,7 +308,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Categorical.log_survival_function(value, name='log_survival_function')` {#Categorical.log_survival_function} +#### `tf.contrib.distributions.Categorical.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Categorical.log_survival_function} Log survival function. @@ -323,6 +328,7 @@ 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: @@ -425,7 +431,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Categorical.pdf(value, name='pdf')` {#Categorical.pdf} +#### `tf.contrib.distributions.Categorical.pdf(value, name='pdf', **condition_kwargs)` {#Categorical.pdf} Probability density function. @@ -434,6 +440,7 @@ 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: @@ -449,7 +456,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Categorical.pmf(value, name='pmf')` {#Categorical.pmf} +#### `tf.contrib.distributions.Categorical.pmf(value, name='pmf', **condition_kwargs)` {#Categorical.pmf} Probability mass function. @@ -458,6 +465,7 @@ 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: @@ -473,7 +481,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Categorical.prob(value, name='prob')` {#Categorical.prob} +#### `tf.contrib.distributions.Categorical.prob(value, name='prob', **condition_kwargs)` {#Categorical.prob} Probability density/mass function (depending on `is_continuous`). @@ -482,6 +490,7 @@ 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: @@ -492,7 +501,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample')` {#Categorical.sample} +#### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Categorical.sample} Generate samples of the specified shape. @@ -505,6 +514,7 @@ 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: @@ -514,7 +524,7 @@ sample. - - - -#### `tf.contrib.distributions.Categorical.sample_n(n, seed=None, name='sample_n')` {#Categorical.sample_n} +#### `tf.contrib.distributions.Categorical.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Categorical.sample_n} Generate `n` samples. @@ -525,6 +535,7 @@ Generate `n` samples. observations to sample. * <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: @@ -546,7 +557,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Categorical.survival_function(value, name='survival_function')` {#Categorical.survival_function} +#### `tf.contrib.distributions.Categorical.survival_function(value, name='survival_function', **condition_kwargs)` {#Categorical.survival_function} Survival function. @@ -563,6 +574,7 @@ 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 db9a96cdf4..fedc42c416 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')` {#Chi2.cdf} +#### `tf.contrib.distributions.Chi2.cdf(value, name='cdf', **condition_kwargs)` {#Chi2.cdf} Cumulative distribution function. @@ -100,6 +100,7 @@ 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: @@ -126,7 +127,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Chi2.entropy(name='entropy')` {#Chi2.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -201,7 +202,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Chi2.log_cdf(value, name='log_cdf')` {#Chi2.log_cdf} +#### `tf.contrib.distributions.Chi2.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Chi2.log_cdf} Log cumulative distribution function. @@ -220,6 +221,7 @@ 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: @@ -230,7 +232,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf')` {#Chi2.log_pdf} +#### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Chi2.log_pdf} Log probability density function. @@ -239,6 +241,7 @@ 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: @@ -254,7 +257,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf')` {#Chi2.log_pmf} +#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Chi2.log_pmf} Log probability mass function. @@ -263,6 +266,7 @@ 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 +282,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob')` {#Chi2.log_prob} +#### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob', **condition_kwargs)` {#Chi2.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -287,6 +291,7 @@ 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: @@ -297,7 +302,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2.log_survival_function(value, name='log_survival_function')` {#Chi2.log_survival_function} +#### `tf.contrib.distributions.Chi2.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Chi2.log_survival_function} Log survival function. @@ -317,6 +322,7 @@ 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: @@ -402,7 +408,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf')` {#Chi2.pdf} +#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf', **condition_kwargs)` {#Chi2.pdf} Probability density function. @@ -411,6 +417,7 @@ 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: @@ -426,7 +433,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf')` {#Chi2.pmf} +#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf', **condition_kwargs)` {#Chi2.pmf} Probability mass function. @@ -435,6 +442,7 @@ 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: @@ -450,7 +458,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Chi2.prob(value, name='prob')` {#Chi2.prob} +#### `tf.contrib.distributions.Chi2.prob(value, name='prob', **condition_kwargs)` {#Chi2.prob} Probability density/mass function (depending on `is_continuous`). @@ -459,6 +467,7 @@ 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: @@ -469,7 +478,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample')` {#Chi2.sample} +#### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Chi2.sample} Generate samples of the specified shape. @@ -482,6 +491,7 @@ 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: @@ -491,7 +501,7 @@ sample. - - - -#### `tf.contrib.distributions.Chi2.sample_n(n, seed=None, name='sample_n')` {#Chi2.sample_n} +#### `tf.contrib.distributions.Chi2.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Chi2.sample_n} Generate `n` samples. @@ -507,6 +517,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -528,7 +539,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Chi2.survival_function(value, name='survival_function')` {#Chi2.survival_function} +#### `tf.contrib.distributions.Chi2.survival_function(value, name='survival_function', **condition_kwargs)` {#Chi2.survival_function} Survival function. @@ -545,6 +556,7 @@ 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.Uniform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md index 09bfa3d5fa..4addf13b42 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')` {#Uniform.cdf} +#### `tf.contrib.distributions.Uniform.cdf(value, name='cdf', **condition_kwargs)` {#Uniform.cdf} Cumulative distribution function. @@ -120,6 +120,7 @@ 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: @@ -139,7 +140,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Uniform.entropy(name='entropy')` {#Uniform.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -203,7 +204,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Uniform.log_cdf(value, name='log_cdf')` {#Uniform.log_cdf} +#### `tf.contrib.distributions.Uniform.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Uniform.log_cdf} Log cumulative distribution function. @@ -222,6 +223,7 @@ 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: @@ -232,7 +234,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf')` {#Uniform.log_pdf} +#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Uniform.log_pdf} Log probability density function. @@ -241,6 +243,7 @@ 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: @@ -256,7 +259,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf')` {#Uniform.log_pmf} +#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Uniform.log_pmf} Log probability mass function. @@ -265,6 +268,7 @@ 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: @@ -280,7 +284,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob')` {#Uniform.log_prob} +#### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob', **condition_kwargs)` {#Uniform.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -289,6 +293,7 @@ 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: @@ -299,7 +304,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Uniform.log_survival_function(value, name='log_survival_function')` {#Uniform.log_survival_function} +#### `tf.contrib.distributions.Uniform.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Uniform.log_survival_function} Log survival function. @@ -319,6 +324,7 @@ 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: @@ -398,7 +404,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Uniform.pdf(value, name='pdf')` {#Uniform.pdf} +#### `tf.contrib.distributions.Uniform.pdf(value, name='pdf', **condition_kwargs)` {#Uniform.pdf} Probability density function. @@ -407,6 +413,7 @@ 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: @@ -422,7 +429,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Uniform.pmf(value, name='pmf')` {#Uniform.pmf} +#### `tf.contrib.distributions.Uniform.pmf(value, name='pmf', **condition_kwargs)` {#Uniform.pmf} Probability mass function. @@ -431,6 +438,7 @@ 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: @@ -446,7 +454,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Uniform.prob(value, name='prob')` {#Uniform.prob} +#### `tf.contrib.distributions.Uniform.prob(value, name='prob', **condition_kwargs)` {#Uniform.prob} Probability density/mass function (depending on `is_continuous`). @@ -455,6 +463,7 @@ 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: @@ -472,7 +481,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample')` {#Uniform.sample} +#### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Uniform.sample} Generate samples of the specified shape. @@ -485,6 +494,7 @@ 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: @@ -494,7 +504,7 @@ sample. - - - -#### `tf.contrib.distributions.Uniform.sample_n(n, seed=None, name='sample_n')` {#Uniform.sample_n} +#### `tf.contrib.distributions.Uniform.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Uniform.sample_n} Generate `n` samples. @@ -505,6 +515,7 @@ Generate `n` samples. observations to sample. * <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: @@ -526,7 +537,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Uniform.survival_function(value, name='survival_function')` {#Uniform.survival_function} +#### `tf.contrib.distributions.Uniform.survival_function(value, name='survival_function', **condition_kwargs)` {#Uniform.survival_function} Survival function. @@ -543,6 +554,7 @@ 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 6859cb8bff..15a38fbf9a 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')` {#WishartCholesky.cdf} +#### `tf.contrib.distributions.WishartCholesky.cdf(value, name='cdf', **condition_kwargs)` {#WishartCholesky.cdf} Cumulative distribution function. @@ -143,6 +143,7 @@ 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: @@ -183,7 +184,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.WishartCholesky.entropy(name='entropy')` {#WishartCholesky.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -247,7 +248,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf')` {#WishartCholesky.log_cdf} +#### `tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf', **condition_kwargs)` {#WishartCholesky.log_cdf} Log cumulative distribution function. @@ -266,6 +267,7 @@ 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: @@ -283,7 +285,7 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf')` {#WishartCholesky.log_pdf} +#### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf', **condition_kwargs)` {#WishartCholesky.log_pdf} Log probability density function. @@ -292,6 +294,7 @@ 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 +310,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf')` {#WishartCholesky.log_pmf} +#### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf', **condition_kwargs)` {#WishartCholesky.log_pmf} Log probability mass function. @@ -316,6 +319,7 @@ 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: @@ -331,7 +335,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob')` {#WishartCholesky.log_prob} +#### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob', **condition_kwargs)` {#WishartCholesky.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -340,6 +344,7 @@ 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: @@ -350,7 +355,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartCholesky.log_survival_function(value, name='log_survival_function')` {#WishartCholesky.log_survival_function} +#### `tf.contrib.distributions.WishartCholesky.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#WishartCholesky.log_survival_function} Log survival function. @@ -370,6 +375,7 @@ 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: @@ -456,7 +462,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf')` {#WishartCholesky.pdf} +#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf', **condition_kwargs)` {#WishartCholesky.pdf} Probability density function. @@ -465,6 +471,7 @@ 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: @@ -480,7 +487,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf')` {#WishartCholesky.pmf} +#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf', **condition_kwargs)` {#WishartCholesky.pmf} Probability mass function. @@ -489,6 +496,7 @@ 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 +512,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob')` {#WishartCholesky.prob} +#### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob', **condition_kwargs)` {#WishartCholesky.prob} Probability density/mass function (depending on `is_continuous`). @@ -513,6 +521,7 @@ 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: @@ -523,7 +532,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample')` {#WishartCholesky.sample} +#### `tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#WishartCholesky.sample} Generate samples of the specified shape. @@ -536,6 +545,7 @@ 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: @@ -545,7 +555,7 @@ sample. - - - -#### `tf.contrib.distributions.WishartCholesky.sample_n(n, seed=None, name='sample_n')` {#WishartCholesky.sample_n} +#### `tf.contrib.distributions.WishartCholesky.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#WishartCholesky.sample_n} Generate `n` samples. @@ -556,6 +566,7 @@ Generate `n` samples. observations to sample. * <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: @@ -591,7 +602,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.WishartCholesky.survival_function(value, name='survival_function')` {#WishartCholesky.survival_function} +#### `tf.contrib.distributions.WishartCholesky.survival_function(value, name='survival_function', **condition_kwargs)` {#WishartCholesky.survival_function} Survival function. @@ -608,6 +619,7 @@ 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 be9565eb65..de26bb0ef7 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 @@ -120,6 +120,38 @@ Subclass Requirements: `inverse_log_det_jacobian` then he or she may also wish to implement these functions to avoid computing the `inverse_log_det_jacobian` or the `inverse`, respectively. + + +Tips for implementing `inverse_log_det_jacobian`: + +- In rare cases it may be easier to compute the Jacobian of the forward + transformation rather than the inverse. The two are equivalent up to sign. + + - Claim: + + Assume `Y=g(X)` is a bijection whose derivative exists and is nonzero + for its domain, i.e., `d/dX g(X)!=0`. Then: + + ```none + (log o det o jacobian o g^{-1})(Y) = -(log o det o jacobian o g)(X) + ``` + + - Proof: + + From the nonzero, differentiability of `g`, the [inverse function + theorem](https://en.wikipedia.org/wiki/Inverse_function_theorem) implies + `g^{-1}` is differentiable in the image of `g`. + Observe that `y = g(x) = g(g^{-1}(y))`. + From the chain rule we have `I = g'(g^{-1}(y))*g^{-1}'(y).` + Since `g` is a bijection and `g`, `g^{-1}` are differentiable, g{-1}' is + non-singular and: + `inv[ g'(g^{-1}(y)) ] = g^{-1}'(y)`. + The claim follows from [properties of determinant]( +https://en.wikipedia.org/wiki/Determinant#Multiplicativity_and_matrix_groups). + +- It is generally preferable to implement the Jacobian of the inverse. Doing + so should have better numerical stability and is likely to share operations + with the `inverse` implementation. - - - #### `tf.contrib.distributions.bijector.Bijector.__init__(batch_ndims=None, event_ndims=None, parameters=None, is_constant_jacobian=False, validate_args=False, dtype=None, name=None)` {#Bijector.__init__} @@ -165,7 +197,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Bijector.forward(x, name='forward')` {#Bijector.forward} +#### `tf.contrib.distributions.bijector.Bijector.forward(x, name='forward', **condition_kwargs)` {#Bijector.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -174,6 +206,7 @@ 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: @@ -189,15 +222,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse(x, name='inverse')` {#Bijector.inverse} +#### `tf.contrib.distributions.bijector.Bijector.inverse(y, name='inverse', **condition_kwargs)` {#Bijector.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -206,7 +240,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -214,7 +248,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#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', **condition_kwargs)` {#Bijector.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -226,8 +260,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -236,7 +271,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -244,20 +279,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.Bijector.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#Bijector.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Bijector.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Bijector.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -266,7 +301,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md index b90b5845a1..de88b01d89 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md @@ -70,7 +70,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). 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 35b8d12834..5c082fe602 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')` {#BetaWithSoftplusAB.cdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.cdf(value, name='cdf', **condition_kwargs)` {#BetaWithSoftplusAB.cdf} Cumulative distribution function. @@ -85,6 +85,7 @@ 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: @@ -104,7 +105,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.BetaWithSoftplusAB.entropy(name='entropy')` {#BetaWithSoftplusAB.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -168,7 +169,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf')` {#BetaWithSoftplusAB.log_cdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf', **condition_kwargs)` {#BetaWithSoftplusAB.log_cdf} Log cumulative distribution function. @@ -195,6 +196,7 @@ 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: @@ -205,7 +207,7 @@ 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')` {#BetaWithSoftplusAB.log_pdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pdf(value, name='log_pdf', **condition_kwargs)` {#BetaWithSoftplusAB.log_pdf} Log probability density function. @@ -214,6 +216,7 @@ 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: @@ -229,7 +232,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf')` {#BetaWithSoftplusAB.log_pmf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf', **condition_kwargs)` {#BetaWithSoftplusAB.log_pmf} Log probability mass function. @@ -238,6 +241,7 @@ 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: @@ -253,7 +257,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob')` {#BetaWithSoftplusAB.log_prob} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob', **condition_kwargs)` {#BetaWithSoftplusAB.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -262,6 +266,7 @@ 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: @@ -272,7 +277,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function')` {#BetaWithSoftplusAB.log_survival_function} +#### `tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#BetaWithSoftplusAB.log_survival_function} Log survival function. @@ -292,6 +297,7 @@ 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: @@ -378,7 +384,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf')` {#BetaWithSoftplusAB.pdf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf', **condition_kwargs)` {#BetaWithSoftplusAB.pdf} Probability density function. @@ -387,6 +393,7 @@ 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: @@ -402,7 +409,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf')` {#BetaWithSoftplusAB.pmf} +#### `tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf', **condition_kwargs)` {#BetaWithSoftplusAB.pmf} Probability mass function. @@ -411,6 +418,7 @@ 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: @@ -426,7 +434,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob')` {#BetaWithSoftplusAB.prob} +#### `tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob', **condition_kwargs)` {#BetaWithSoftplusAB.prob} Probability density/mass function (depending on `is_continuous`). @@ -443,6 +451,7 @@ 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: @@ -453,7 +462,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample')` {#BetaWithSoftplusAB.sample} +#### `tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#BetaWithSoftplusAB.sample} Generate samples of the specified shape. @@ -466,6 +475,7 @@ 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: @@ -475,7 +485,7 @@ sample. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.sample_n(n, seed=None, name='sample_n')` {#BetaWithSoftplusAB.sample_n} +#### `tf.contrib.distributions.BetaWithSoftplusAB.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#BetaWithSoftplusAB.sample_n} Generate `n` samples. @@ -486,6 +496,7 @@ Generate `n` samples. observations to sample. * <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: @@ -507,7 +518,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function')` {#BetaWithSoftplusAB.survival_function} +#### `tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function', **condition_kwargs)` {#BetaWithSoftplusAB.survival_function} Survival function. @@ -524,6 +535,7 @@ 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 10897cfe66..8a17fef2cf 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')` {#Binomial.cdf} +#### `tf.contrib.distributions.Binomial.cdf(value, name='cdf', **condition_kwargs)` {#Binomial.cdf} Cumulative distribution function. @@ -150,6 +150,7 @@ 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: @@ -169,7 +170,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Binomial.entropy(name='entropy')` {#Binomial.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -233,7 +234,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf')` {#Binomial.log_cdf} +#### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Binomial.log_cdf} Log cumulative distribution function. @@ -252,6 +253,7 @@ 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: @@ -262,7 +264,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf')` {#Binomial.log_pdf} +#### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Binomial.log_pdf} Log probability density function. @@ -271,6 +273,7 @@ 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: @@ -286,7 +289,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf')` {#Binomial.log_pmf} +#### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Binomial.log_pmf} Log probability mass function. @@ -295,6 +298,7 @@ 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 +314,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob')` {#Binomial.log_prob} +#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob', **condition_kwargs)` {#Binomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -332,6 +336,7 @@ 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: @@ -342,7 +347,7 @@ values. - - - -#### `tf.contrib.distributions.Binomial.log_survival_function(value, name='log_survival_function')` {#Binomial.log_survival_function} +#### `tf.contrib.distributions.Binomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Binomial.log_survival_function} Log survival function. @@ -362,6 +367,7 @@ 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: @@ -468,7 +474,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf')` {#Binomial.pdf} +#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf', **condition_kwargs)` {#Binomial.pdf} Probability density function. @@ -477,6 +483,7 @@ 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: @@ -492,7 +499,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf')` {#Binomial.pmf} +#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf', **condition_kwargs)` {#Binomial.pmf} Probability mass function. @@ -501,6 +508,7 @@ 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 +524,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Binomial.prob(value, name='prob')` {#Binomial.prob} +#### `tf.contrib.distributions.Binomial.prob(value, name='prob', **condition_kwargs)` {#Binomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -538,6 +546,7 @@ 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: @@ -548,7 +557,7 @@ values. - - - -#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample')` {#Binomial.sample} +#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Binomial.sample} Generate samples of the specified shape. @@ -561,6 +570,7 @@ 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: @@ -570,7 +580,7 @@ sample. - - - -#### `tf.contrib.distributions.Binomial.sample_n(n, seed=None, name='sample_n')` {#Binomial.sample_n} +#### `tf.contrib.distributions.Binomial.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Binomial.sample_n} Generate `n` samples. @@ -581,6 +591,7 @@ Generate `n` samples. observations to sample. * <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: @@ -602,7 +613,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Binomial.survival_function(value, name='survival_function')` {#Binomial.survival_function} +#### `tf.contrib.distributions.Binomial.survival_function(value, name='survival_function', **condition_kwargs)` {#Binomial.survival_function} Survival function. @@ -619,6 +630,7 @@ 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 b9838a4c66..d58f0a5654 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')` {#DirichletMultinomial.cdf} +#### `tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf', **condition_kwargs)` {#DirichletMultinomial.cdf} Cumulative distribution function. @@ -177,6 +177,7 @@ 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: @@ -196,7 +197,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.DirichletMultinomial.entropy(name='entropy')` {#DirichletMultinomial.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -260,7 +261,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf')` {#DirichletMultinomial.log_cdf} +#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#DirichletMultinomial.log_cdf} Log cumulative distribution function. @@ -279,6 +280,7 @@ 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 +291,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf')` {#DirichletMultinomial.log_pdf} +#### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#DirichletMultinomial.log_pdf} Log probability density function. @@ -298,6 +300,7 @@ 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 +316,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf')` {#DirichletMultinomial.log_pmf} +#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#DirichletMultinomial.log_pmf} Log probability mass function. @@ -322,6 +325,7 @@ 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: @@ -337,7 +341,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob')` {#DirichletMultinomial.log_prob} +#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob', **condition_kwargs)` {#DirichletMultinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -361,6 +365,7 @@ 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: @@ -371,7 +376,7 @@ 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')` {#DirichletMultinomial.log_survival_function} +#### `tf.contrib.distributions.DirichletMultinomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#DirichletMultinomial.log_survival_function} Log survival function. @@ -391,6 +396,7 @@ 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: @@ -477,7 +483,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf')` {#DirichletMultinomial.pdf} +#### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf', **condition_kwargs)` {#DirichletMultinomial.pdf} Probability density function. @@ -486,6 +492,7 @@ 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: @@ -501,7 +508,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf')` {#DirichletMultinomial.pmf} +#### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf', **condition_kwargs)` {#DirichletMultinomial.pmf} Probability mass function. @@ -510,6 +517,7 @@ 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: @@ -525,7 +533,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob')` {#DirichletMultinomial.prob} +#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob', **condition_kwargs)` {#DirichletMultinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -549,6 +557,7 @@ 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: @@ -559,7 +568,7 @@ it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample')` {#DirichletMultinomial.sample} +#### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#DirichletMultinomial.sample} Generate samples of the specified shape. @@ -572,6 +581,7 @@ 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: @@ -581,7 +591,7 @@ sample. - - - -#### `tf.contrib.distributions.DirichletMultinomial.sample_n(n, seed=None, name='sample_n')` {#DirichletMultinomial.sample_n} +#### `tf.contrib.distributions.DirichletMultinomial.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#DirichletMultinomial.sample_n} Generate `n` samples. @@ -592,6 +602,7 @@ Generate `n` samples. observations to sample. * <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: @@ -613,7 +624,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.DirichletMultinomial.survival_function(value, name='survival_function')` {#DirichletMultinomial.survival_function} +#### `tf.contrib.distributions.DirichletMultinomial.survival_function(value, name='survival_function', **condition_kwargs)` {#DirichletMultinomial.survival_function} Survival function. @@ -630,6 +641,7 @@ 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 a8f09489e6..c97978f684 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')` {#Exponential.cdf} +#### `tf.contrib.distributions.Exponential.cdf(value, name='cdf', **condition_kwargs)` {#Exponential.cdf} Cumulative distribution function. @@ -100,6 +100,7 @@ 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: @@ -119,7 +120,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Exponential.entropy(name='entropy')` {#Exponential.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -201,7 +202,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf')` {#Exponential.log_cdf} +#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Exponential.log_cdf} Log cumulative distribution function. @@ -220,6 +221,7 @@ 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: @@ -230,7 +232,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf')` {#Exponential.log_pdf} +#### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Exponential.log_pdf} Log probability density function. @@ -239,6 +241,7 @@ 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: @@ -254,7 +257,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')` {#Exponential.log_pmf} +#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Exponential.log_pmf} Log probability mass function. @@ -263,6 +266,7 @@ 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 +282,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob')` {#Exponential.log_prob} +#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob', **condition_kwargs)` {#Exponential.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -287,6 +291,7 @@ 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: @@ -297,7 +302,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function')` {#Exponential.log_survival_function} +#### `tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Exponential.log_survival_function} Log survival function. @@ -317,6 +322,7 @@ 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: @@ -402,7 +408,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf')` {#Exponential.pdf} +#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf', **condition_kwargs)` {#Exponential.pdf} Probability density function. @@ -411,6 +417,7 @@ 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: @@ -426,7 +433,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf')` {#Exponential.pmf} +#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf', **condition_kwargs)` {#Exponential.pmf} Probability mass function. @@ -435,6 +442,7 @@ 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: @@ -450,7 +458,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Exponential.prob(value, name='prob')` {#Exponential.prob} +#### `tf.contrib.distributions.Exponential.prob(value, name='prob', **condition_kwargs)` {#Exponential.prob} Probability density/mass function (depending on `is_continuous`). @@ -459,6 +467,7 @@ 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: @@ -469,7 +478,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample')` {#Exponential.sample} +#### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Exponential.sample} Generate samples of the specified shape. @@ -482,6 +491,7 @@ 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: @@ -491,7 +501,7 @@ sample. - - - -#### `tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n')` {#Exponential.sample_n} +#### `tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Exponential.sample_n} Generate `n` samples. @@ -507,6 +517,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -528,7 +539,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Exponential.survival_function(value, name='survival_function')` {#Exponential.survival_function} +#### `tf.contrib.distributions.Exponential.survival_function(value, name='survival_function', **condition_kwargs)` {#Exponential.survival_function} Survival function. @@ -545,6 +556,7 @@ 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 b63972021a..64a93021e8 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')` {#Gamma.cdf} +#### `tf.contrib.distributions.Gamma.cdf(value, name='cdf', **condition_kwargs)` {#Gamma.cdf} Cumulative distribution function. @@ -127,6 +127,7 @@ 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: @@ -146,7 +147,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Gamma.entropy(name='entropy')` {#Gamma.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -221,7 +222,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf')` {#Gamma.log_cdf} +#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Gamma.log_cdf} Log cumulative distribution function. @@ -240,6 +241,7 @@ 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: @@ -250,7 +252,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf')` {#Gamma.log_pdf} +#### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Gamma.log_pdf} Log probability density function. @@ -259,6 +261,7 @@ 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: @@ -274,7 +277,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')` {#Gamma.log_pmf} +#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Gamma.log_pmf} Log probability mass function. @@ -283,6 +286,7 @@ 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: @@ -298,7 +302,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob')` {#Gamma.log_prob} +#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob', **condition_kwargs)` {#Gamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -307,6 +311,7 @@ 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: @@ -317,7 +322,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function')` {#Gamma.log_survival_function} +#### `tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Gamma.log_survival_function} Log survival function. @@ -337,6 +342,7 @@ 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: @@ -422,7 +428,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf')` {#Gamma.pdf} +#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf', **condition_kwargs)` {#Gamma.pdf} Probability density function. @@ -431,6 +437,7 @@ 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: @@ -446,7 +453,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf')` {#Gamma.pmf} +#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf', **condition_kwargs)` {#Gamma.pmf} Probability mass function. @@ -455,6 +462,7 @@ 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: @@ -470,7 +478,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Gamma.prob(value, name='prob')` {#Gamma.prob} +#### `tf.contrib.distributions.Gamma.prob(value, name='prob', **condition_kwargs)` {#Gamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -479,6 +487,7 @@ 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: @@ -489,7 +498,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample')` {#Gamma.sample} +#### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Gamma.sample} Generate samples of the specified shape. @@ -502,6 +511,7 @@ 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: @@ -511,7 +521,7 @@ sample. - - - -#### `tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n')` {#Gamma.sample_n} +#### `tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Gamma.sample_n} Generate `n` samples. @@ -527,6 +537,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -548,7 +559,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Gamma.survival_function(value, name='survival_function')` {#Gamma.survival_function} +#### `tf.contrib.distributions.Gamma.survival_function(value, name='survival_function', **condition_kwargs)` {#Gamma.survival_function} Survival function. @@ -565,6 +576,7 @@ 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 4eff4bd5a6..c9b283547a 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')` {#GammaWithSoftplusAlphaBeta.cdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.cdf} Cumulative distribution function. @@ -78,6 +78,7 @@ 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: @@ -97,7 +98,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.entropy(name='entropy')` {#GammaWithSoftplusAlphaBeta.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -172,7 +173,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')` {#GammaWithSoftplusAlphaBeta.log_cdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_cdf} Log cumulative distribution function. @@ -191,6 +192,7 @@ 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: @@ -201,7 +203,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')` {#GammaWithSoftplusAlphaBeta.log_pdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_pdf} Log probability density function. @@ -210,6 +212,7 @@ 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: @@ -225,7 +228,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#GammaWithSoftplusAlphaBeta.log_pmf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_pmf} Log probability mass function. @@ -234,6 +237,7 @@ 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: @@ -249,7 +253,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#GammaWithSoftplusAlphaBeta.log_prob} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -258,6 +262,7 @@ 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: @@ -268,7 +273,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')` {#GammaWithSoftplusAlphaBeta.log_survival_function} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.log_survival_function} Log survival function. @@ -288,6 +293,7 @@ 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: @@ -373,7 +379,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#GammaWithSoftplusAlphaBeta.pdf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.pdf} Probability density function. @@ -382,6 +388,7 @@ 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: @@ -397,7 +404,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#GammaWithSoftplusAlphaBeta.pmf} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.pmf} Probability mass function. @@ -406,6 +413,7 @@ 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: @@ -421,7 +429,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#GammaWithSoftplusAlphaBeta.prob} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -430,6 +438,7 @@ 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 +449,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')` {#GammaWithSoftplusAlphaBeta.sample} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.sample} Generate samples of the specified shape. @@ -453,6 +462,7 @@ 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: @@ -462,7 +472,7 @@ sample. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n')` {#GammaWithSoftplusAlphaBeta.sample_n} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.sample_n} Generate `n` samples. @@ -478,6 +488,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -499,7 +510,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')` {#GammaWithSoftplusAlphaBeta.survival_function} +#### `tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function', **condition_kwargs)` {#GammaWithSoftplusAlphaBeta.survival_function} Survival function. @@ -516,6 +527,7 @@ 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 6dbfcfd6f8..a67eba1cfb 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')` {#InverseGamma.cdf} +#### `tf.contrib.distributions.InverseGamma.cdf(value, name='cdf', **condition_kwargs)` {#InverseGamma.cdf} Cumulative distribution function. @@ -123,6 +123,7 @@ 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: @@ -142,7 +143,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.InverseGamma.entropy(name='entropy')` {#InverseGamma.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `InverseGamma`: @@ -217,7 +218,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf')` {#InverseGamma.log_cdf} +#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#InverseGamma.log_cdf} Log cumulative distribution function. @@ -236,6 +237,7 @@ 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: @@ -246,7 +248,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf')` {#InverseGamma.log_pdf} +#### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#InverseGamma.log_pdf} Log probability density function. @@ -255,6 +257,7 @@ 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: @@ -270,7 +273,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')` {#InverseGamma.log_pmf} +#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#InverseGamma.log_pmf} Log probability mass function. @@ -279,6 +282,7 @@ 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: @@ -294,7 +298,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob')` {#InverseGamma.log_prob} +#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob', **condition_kwargs)` {#InverseGamma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -303,6 +307,7 @@ 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: @@ -313,7 +318,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function')` {#InverseGamma.log_survival_function} +#### `tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#InverseGamma.log_survival_function} Log survival function. @@ -333,6 +338,7 @@ 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: @@ -422,7 +428,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')` {#InverseGamma.pdf} +#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf', **condition_kwargs)` {#InverseGamma.pdf} Probability density function. @@ -431,6 +437,7 @@ 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: @@ -446,7 +453,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')` {#InverseGamma.pmf} +#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf', **condition_kwargs)` {#InverseGamma.pmf} Probability mass function. @@ -455,6 +462,7 @@ 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: @@ -470,7 +478,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob')` {#InverseGamma.prob} +#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob', **condition_kwargs)` {#InverseGamma.prob} Probability density/mass function (depending on `is_continuous`). @@ -479,6 +487,7 @@ 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: @@ -489,7 +498,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample')` {#InverseGamma.sample} +#### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#InverseGamma.sample} Generate samples of the specified shape. @@ -502,6 +511,7 @@ 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: @@ -511,7 +521,7 @@ sample. - - - -#### `tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n')` {#InverseGamma.sample_n} +#### `tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#InverseGamma.sample_n} Generate `n` samples. @@ -527,6 +537,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -548,7 +559,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function')` {#InverseGamma.survival_function} +#### `tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function', **condition_kwargs)` {#InverseGamma.survival_function} Survival function. @@ -565,6 +576,7 @@ 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 296a4ee985..38a1753221 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')` {#InverseGammaWithSoftplusAlphaBeta.cdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.cdf} Cumulative distribution function. @@ -78,6 +78,7 @@ 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: @@ -97,7 +98,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.entropy(name='entropy')` {#InverseGammaWithSoftplusAlphaBeta.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `InverseGamma`: @@ -172,7 +173,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')` {#InverseGammaWithSoftplusAlphaBeta.log_cdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_cdf} Log cumulative distribution function. @@ -191,6 +192,7 @@ 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: @@ -201,7 +203,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')` {#InverseGammaWithSoftplusAlphaBeta.log_pdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_pdf} Log probability density function. @@ -210,6 +212,7 @@ 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: @@ -225,7 +228,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_pmf} Log probability mass function. @@ -234,6 +237,7 @@ 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: @@ -249,7 +253,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')` {#InverseGammaWithSoftplusAlphaBeta.log_prob} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -258,6 +262,7 @@ 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: @@ -268,7 +273,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')` {#InverseGammaWithSoftplusAlphaBeta.log_survival_function} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.log_survival_function} Log survival function. @@ -288,6 +293,7 @@ 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: @@ -377,7 +383,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf')` {#InverseGammaWithSoftplusAlphaBeta.pdf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.pdf} Probability density function. @@ -386,6 +392,7 @@ 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: @@ -401,7 +408,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf')` {#InverseGammaWithSoftplusAlphaBeta.pmf} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.pmf} Probability mass function. @@ -410,6 +417,7 @@ 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: @@ -425,7 +433,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob')` {#InverseGammaWithSoftplusAlphaBeta.prob} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.prob} Probability density/mass function (depending on `is_continuous`). @@ -434,6 +442,7 @@ 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: @@ -444,7 +453,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')` {#InverseGammaWithSoftplusAlphaBeta.sample} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.sample} Generate samples of the specified shape. @@ -457,6 +466,7 @@ 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: @@ -466,7 +476,7 @@ sample. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n')` {#InverseGammaWithSoftplusAlphaBeta.sample_n} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.sample_n} Generate `n` samples. @@ -482,6 +492,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -503,7 +514,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')` {#InverseGammaWithSoftplusAlphaBeta.survival_function} +#### `tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function', **condition_kwargs)` {#InverseGammaWithSoftplusAlphaBeta.survival_function} Survival function. @@ -520,6 +531,7 @@ 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 15e6b46e83..82d265b2c2 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')` {#Multinomial.cdf} +#### `tf.contrib.distributions.Multinomial.cdf(value, name='cdf', **condition_kwargs)` {#Multinomial.cdf} Cumulative distribution function. @@ -160,6 +160,7 @@ 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: @@ -179,7 +180,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Multinomial.entropy(name='entropy')` {#Multinomial.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -243,7 +244,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf')` {#Multinomial.log_cdf} +#### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Multinomial.log_cdf} Log cumulative distribution function. @@ -262,6 +263,7 @@ 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: @@ -272,7 +274,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf')` {#Multinomial.log_pdf} +#### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Multinomial.log_pdf} Log probability density function. @@ -281,6 +283,7 @@ 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: @@ -296,7 +299,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf')` {#Multinomial.log_pmf} +#### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Multinomial.log_pmf} Log probability mass function. @@ -305,6 +308,7 @@ 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: @@ -320,7 +324,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob')` {#Multinomial.log_prob} +#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob', **condition_kwargs)` {#Multinomial.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -344,6 +348,7 @@ 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: @@ -354,7 +359,7 @@ 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')` {#Multinomial.log_survival_function} +#### `tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Multinomial.log_survival_function} Log survival function. @@ -374,6 +379,7 @@ 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: @@ -476,7 +482,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf')` {#Multinomial.pdf} +#### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf', **condition_kwargs)` {#Multinomial.pdf} Probability density function. @@ -485,6 +491,7 @@ 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: @@ -500,7 +507,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf')` {#Multinomial.pmf} +#### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf', **condition_kwargs)` {#Multinomial.pmf} Probability mass function. @@ -509,6 +516,7 @@ 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: @@ -524,7 +532,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Multinomial.prob(value, name='prob')` {#Multinomial.prob} +#### `tf.contrib.distributions.Multinomial.prob(value, name='prob', **condition_kwargs)` {#Multinomial.prob} Probability density/mass function (depending on `is_continuous`). @@ -548,6 +556,7 @@ 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: @@ -558,7 +567,7 @@ if it sums up to `n` and its components are equal to integer values. - - - -#### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample')` {#Multinomial.sample} +#### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Multinomial.sample} Generate samples of the specified shape. @@ -571,6 +580,7 @@ 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: @@ -580,7 +590,7 @@ sample. - - - -#### `tf.contrib.distributions.Multinomial.sample_n(n, seed=None, name='sample_n')` {#Multinomial.sample_n} +#### `tf.contrib.distributions.Multinomial.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Multinomial.sample_n} Generate `n` samples. @@ -591,6 +601,7 @@ Generate `n` samples. observations to sample. * <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: @@ -612,7 +623,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Multinomial.survival_function(value, name='survival_function')` {#Multinomial.survival_function} +#### `tf.contrib.distributions.Multinomial.survival_function(value, name='survival_function', **condition_kwargs)` {#Multinomial.survival_function} Survival function. @@ -629,6 +640,7 @@ 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 51c22b9e10..191e6e9a28 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')` {#MultivariateNormalDiagPlusVDVT.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.cdf} Cumulative distribution function. @@ -160,6 +160,7 @@ 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: @@ -179,7 +180,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.entropy(name='entropy')` {#MultivariateNormalDiagPlusVDVT.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -243,7 +244,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiagPlusVDVT.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_cdf} Log cumulative distribution function. @@ -262,6 +263,7 @@ 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: @@ -272,7 +274,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagPlusVDVT.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_pdf} Log probability density function. @@ -281,6 +283,7 @@ 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: @@ -296,7 +299,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagPlusVDVT.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_pmf} Log probability mass function. @@ -305,6 +308,7 @@ 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: @@ -320,7 +324,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob')` {#MultivariateNormalDiagPlusVDVT.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -345,6 +349,7 @@ 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: @@ -362,7 +367,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiagPlusVDVT.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.log_survival_function} Log survival function. @@ -382,6 +387,7 @@ 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: @@ -468,7 +474,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf')` {#MultivariateNormalDiagPlusVDVT.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.pdf} Probability density function. @@ -477,6 +483,7 @@ 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: @@ -492,7 +499,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf')` {#MultivariateNormalDiagPlusVDVT.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.pmf} Probability mass function. @@ -501,6 +508,7 @@ 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 +524,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob')` {#MultivariateNormalDiagPlusVDVT.prob} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.prob} Probability density/mass function (depending on `is_continuous`). @@ -541,6 +549,7 @@ 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: @@ -551,7 +560,7 @@ or - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiagPlusVDVT.sample} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.sample} Generate samples of the specified shape. @@ -564,6 +573,7 @@ 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: @@ -573,7 +583,7 @@ sample. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalDiagPlusVDVT.sample_n} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.sample_n} Generate `n` samples. @@ -584,6 +594,7 @@ Generate `n` samples. observations to sample. * <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: @@ -619,7 +630,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function')` {#MultivariateNormalDiagPlusVDVT.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiagPlusVDVT.survival_function} Survival function. @@ -636,6 +647,7 @@ 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.bijector.ScaleAndShift.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.bijector.ScaleAndShift.md index 7f1246b964..4e1eda55fb 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.bijector.ScaleAndShift.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.bijector.ScaleAndShift.md @@ -54,7 +54,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.forward(x, name='forward')` {#ScaleAndShift.forward} +#### `tf.contrib.distributions.bijector.ScaleAndShift.forward(x, name='forward', **condition_kwargs)` {#ScaleAndShift.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -63,6 +63,7 @@ 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: @@ -78,15 +79,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse(x, name='inverse')` {#ScaleAndShift.inverse} +#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse(y, name='inverse', **condition_kwargs)` {#ScaleAndShift.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -95,7 +97,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -103,7 +105,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#ScaleAndShift.inverse_and_inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_and_inverse_log_det_jacobian(y, name='inverse_and_inverse_log_det_jacobian', **condition_kwargs)` {#ScaleAndShift.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -115,8 +117,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -125,7 +128,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -133,20 +136,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#ScaleAndShift.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.ScaleAndShift.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#ScaleAndShift.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -155,7 +158,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md index 99899f1421..64c16118ca 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md @@ -105,7 +105,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). 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 97a2f4d2b8..4f7a38070f 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')` {#BernoulliWithSigmoidP.cdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.cdf(value, name='cdf', **condition_kwargs)` {#BernoulliWithSigmoidP.cdf} Cumulative distribution function. @@ -64,6 +64,7 @@ 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: @@ -83,7 +84,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.BernoulliWithSigmoidP.entropy(name='entropy')` {#BernoulliWithSigmoidP.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -147,7 +148,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf')` {#BernoulliWithSigmoidP.log_cdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_cdf} Log cumulative distribution function. @@ -166,6 +167,7 @@ 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: @@ -176,7 +178,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf')` {#BernoulliWithSigmoidP.log_pdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_pdf} Log probability density function. @@ -185,6 +187,7 @@ 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: @@ -200,7 +203,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf')` {#BernoulliWithSigmoidP.log_pmf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf', **condition_kwargs)` {#BernoulliWithSigmoidP.log_pmf} Log probability mass function. @@ -209,6 +212,7 @@ 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: @@ -224,7 +228,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob')` {#BernoulliWithSigmoidP.log_prob} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob', **condition_kwargs)` {#BernoulliWithSigmoidP.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -233,6 +237,7 @@ 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: @@ -243,7 +248,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function')` {#BernoulliWithSigmoidP.log_survival_function} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#BernoulliWithSigmoidP.log_survival_function} Log survival function. @@ -263,6 +268,7 @@ 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: @@ -360,7 +366,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf')` {#BernoulliWithSigmoidP.pdf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf', **condition_kwargs)` {#BernoulliWithSigmoidP.pdf} Probability density function. @@ -369,6 +375,7 @@ 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: @@ -384,7 +391,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf')` {#BernoulliWithSigmoidP.pmf} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf', **condition_kwargs)` {#BernoulliWithSigmoidP.pmf} Probability mass function. @@ -393,6 +400,7 @@ 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: @@ -408,7 +416,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob')` {#BernoulliWithSigmoidP.prob} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob', **condition_kwargs)` {#BernoulliWithSigmoidP.prob} Probability density/mass function (depending on `is_continuous`). @@ -417,6 +425,7 @@ 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: @@ -434,7 +443,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample')` {#BernoulliWithSigmoidP.sample} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#BernoulliWithSigmoidP.sample} Generate samples of the specified shape. @@ -447,6 +456,7 @@ 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: @@ -456,7 +466,7 @@ sample. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample_n(n, seed=None, name='sample_n')` {#BernoulliWithSigmoidP.sample_n} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#BernoulliWithSigmoidP.sample_n} Generate `n` samples. @@ -467,6 +477,7 @@ Generate `n` samples. observations to sample. * <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: @@ -488,7 +499,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function')` {#BernoulliWithSigmoidP.survival_function} +#### `tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function', **condition_kwargs)` {#BernoulliWithSigmoidP.survival_function} Survival function. @@ -505,6 +516,7 @@ 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.Softplus.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.bijector.Softplus.md index 16313d2e85..f401c1678d 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')` {#Softplus.forward} +#### `tf.contrib.distributions.bijector.Softplus.forward(x, name='forward', **condition_kwargs)` {#Softplus.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -47,6 +47,7 @@ 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: @@ -62,15 +63,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse(x, name='inverse')` {#Softplus.inverse} +#### `tf.contrib.distributions.bijector.Softplus.inverse(y, name='inverse', **condition_kwargs)` {#Softplus.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -79,7 +81,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -87,7 +89,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#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', **condition_kwargs)` {#Softplus.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -99,8 +101,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -109,7 +112,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -117,20 +120,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.Softplus.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#Softplus.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Softplus.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Softplus.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -139,7 +142,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md index 0d30b6a37a..0e153efd2b 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md @@ -66,9 +66,9 @@ Initializes a DNNClassifier instance. * <b>`feature_columns`</b>: An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from `FeatureColumn`. -* <b>`model_dir`</b>: Directory to save model parameters, graph and etc. This can also - be used to load checkpoints from the directory into a estimator to continue - training a previously saved model. +* <b>`model_dir`</b>: Directory to save model parameters, graph and etc. This can + also be used to load checkpoints from the directory into a estimator to + continue training a previously saved model. * <b>`n_classes`</b>: number of target classes. Default is binary classification. It must be greater than 1. * <b>`weight_column_name`</b>: A string defining feature column name representing diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md index 7c220189a3..76dd208cdb 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md @@ -103,7 +103,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md index bdac5ffbbc..8b48d9fbdc 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md @@ -118,7 +118,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). 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 b714ac4238..dbb4ec7aa7 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 @@ -32,7 +32,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Exp.forward(x, name='forward')` {#Exp.forward} +#### `tf.contrib.distributions.bijector.Exp.forward(x, name='forward', **condition_kwargs)` {#Exp.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -41,6 +41,7 @@ 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: @@ -56,15 +57,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.Exp.inverse(x, name='inverse')` {#Exp.inverse} +#### `tf.contrib.distributions.bijector.Exp.inverse(y, name='inverse', **condition_kwargs)` {#Exp.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -73,7 +75,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -81,7 +83,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Exp.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#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', **condition_kwargs)` {#Exp.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -93,8 +95,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -103,7 +106,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -111,20 +114,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.Exp.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#Exp.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Exp.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Exp.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -133,7 +136,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. 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 d75ada3edf..070a4a7a49 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')` {#Beta.cdf} +#### `tf.contrib.distributions.Beta.cdf(value, name='cdf', **condition_kwargs)` {#Beta.cdf} Cumulative distribution function. @@ -174,6 +174,7 @@ 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: @@ -193,7 +194,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Beta.entropy(name='entropy')` {#Beta.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -257,7 +258,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf')` {#Beta.log_cdf} +#### `tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Beta.log_cdf} Log cumulative distribution function. @@ -284,6 +285,7 @@ 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: @@ -294,7 +296,7 @@ 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')` {#Beta.log_pdf} +#### `tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Beta.log_pdf} Log probability density function. @@ -303,6 +305,7 @@ 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: @@ -318,7 +321,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf')` {#Beta.log_pmf} +#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Beta.log_pmf} Log probability mass function. @@ -327,6 +330,7 @@ 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 +346,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob')` {#Beta.log_prob} +#### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob', **condition_kwargs)` {#Beta.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -351,6 +355,7 @@ 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: @@ -361,7 +366,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Beta.log_survival_function(value, name='log_survival_function')` {#Beta.log_survival_function} +#### `tf.contrib.distributions.Beta.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Beta.log_survival_function} Log survival function. @@ -381,6 +386,7 @@ 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: @@ -467,7 +473,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Beta.pdf(value, name='pdf')` {#Beta.pdf} +#### `tf.contrib.distributions.Beta.pdf(value, name='pdf', **condition_kwargs)` {#Beta.pdf} Probability density function. @@ -476,6 +482,7 @@ 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 +498,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Beta.pmf(value, name='pmf')` {#Beta.pmf} +#### `tf.contrib.distributions.Beta.pmf(value, name='pmf', **condition_kwargs)` {#Beta.pmf} Probability mass function. @@ -500,6 +507,7 @@ 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: @@ -515,7 +523,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Beta.prob(value, name='prob')` {#Beta.prob} +#### `tf.contrib.distributions.Beta.prob(value, name='prob', **condition_kwargs)` {#Beta.prob} Probability density/mass function (depending on `is_continuous`). @@ -532,6 +540,7 @@ 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: @@ -542,7 +551,7 @@ distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`. - - - -#### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample')` {#Beta.sample} +#### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Beta.sample} Generate samples of the specified shape. @@ -555,6 +564,7 @@ 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: @@ -564,7 +574,7 @@ sample. - - - -#### `tf.contrib.distributions.Beta.sample_n(n, seed=None, name='sample_n')` {#Beta.sample_n} +#### `tf.contrib.distributions.Beta.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Beta.sample_n} Generate `n` samples. @@ -575,6 +585,7 @@ Generate `n` samples. observations to sample. * <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: @@ -596,7 +607,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Beta.survival_function(value, name='survival_function')` {#Beta.survival_function} +#### `tf.contrib.distributions.Beta.survival_function(value, name='survival_function', **condition_kwargs)` {#Beta.survival_function} Survival function. @@ -613,6 +624,7 @@ 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 50fab9ac8d..48359b697d 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')` {#Laplace.cdf} +#### `tf.contrib.distributions.Laplace.cdf(value, name='cdf', **condition_kwargs)` {#Laplace.cdf} Cumulative distribution function. @@ -97,6 +97,7 @@ 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: @@ -116,7 +117,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Laplace.entropy(name='entropy')` {#Laplace.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -187,7 +188,7 @@ Distribution parameter for the location. - - - -#### `tf.contrib.distributions.Laplace.log_cdf(value, name='log_cdf')` {#Laplace.log_cdf} +#### `tf.contrib.distributions.Laplace.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Laplace.log_cdf} Log cumulative distribution function. @@ -206,6 +207,7 @@ 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: @@ -216,7 +218,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf')` {#Laplace.log_pdf} +#### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Laplace.log_pdf} Log probability density function. @@ -225,6 +227,7 @@ 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: @@ -240,7 +243,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf} +#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Laplace.log_pmf} Log probability mass function. @@ -249,6 +252,7 @@ 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: @@ -264,7 +268,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob')` {#Laplace.log_prob} +#### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob', **condition_kwargs)` {#Laplace.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -273,6 +277,7 @@ 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: @@ -283,7 +288,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Laplace.log_survival_function(value, name='log_survival_function')` {#Laplace.log_survival_function} +#### `tf.contrib.distributions.Laplace.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Laplace.log_survival_function} Log survival function. @@ -303,6 +308,7 @@ 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: @@ -382,7 +388,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf')` {#Laplace.pdf} +#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf', **condition_kwargs)` {#Laplace.pdf} Probability density function. @@ -391,6 +397,7 @@ 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: @@ -406,7 +413,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf')` {#Laplace.pmf} +#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf', **condition_kwargs)` {#Laplace.pmf} Probability mass function. @@ -415,6 +422,7 @@ 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: @@ -430,7 +438,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Laplace.prob(value, name='prob')` {#Laplace.prob} +#### `tf.contrib.distributions.Laplace.prob(value, name='prob', **condition_kwargs)` {#Laplace.prob} Probability density/mass function (depending on `is_continuous`). @@ -439,6 +447,7 @@ 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: @@ -449,7 +458,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample')` {#Laplace.sample} +#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Laplace.sample} Generate samples of the specified shape. @@ -462,6 +471,7 @@ 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: @@ -471,7 +481,7 @@ sample. - - - -#### `tf.contrib.distributions.Laplace.sample_n(n, seed=None, name='sample_n')` {#Laplace.sample_n} +#### `tf.contrib.distributions.Laplace.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Laplace.sample_n} Generate `n` samples. @@ -482,6 +492,7 @@ Generate `n` samples. observations to sample. * <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: @@ -510,7 +521,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Laplace.survival_function(value, name='survival_function')` {#Laplace.survival_function} +#### `tf.contrib.distributions.Laplace.survival_function(value, name='survival_function', **condition_kwargs)` {#Laplace.survival_function} Survival function. @@ -527,6 +538,7 @@ 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 790034eab4..ea97c4f0bc 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')` {#LaplaceWithSoftplusScale.cdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.cdf(value, name='cdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.cdf} Cumulative distribution function. @@ -64,6 +64,7 @@ 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: @@ -83,7 +84,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.LaplaceWithSoftplusScale.entropy(name='entropy')` {#LaplaceWithSoftplusScale.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -154,7 +155,7 @@ Distribution parameter for the location. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_cdf(value, name='log_cdf')` {#LaplaceWithSoftplusScale.log_cdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_cdf(value, name='log_cdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_cdf} Log cumulative distribution function. @@ -173,6 +174,7 @@ 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: @@ -183,7 +185,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf')` {#LaplaceWithSoftplusScale.log_pdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_pdf} Log probability density function. @@ -192,6 +194,7 @@ 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: @@ -207,7 +210,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf')` {#LaplaceWithSoftplusScale.log_pmf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_pmf} Log probability mass function. @@ -216,6 +219,7 @@ 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: @@ -231,7 +235,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob')` {#LaplaceWithSoftplusScale.log_prob} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -240,6 +244,7 @@ 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: @@ -250,7 +255,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_survival_function(value, name='log_survival_function')` {#LaplaceWithSoftplusScale.log_survival_function} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#LaplaceWithSoftplusScale.log_survival_function} Log survival function. @@ -270,6 +275,7 @@ 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: @@ -349,7 +355,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf')` {#LaplaceWithSoftplusScale.pdf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf', **condition_kwargs)` {#LaplaceWithSoftplusScale.pdf} Probability density function. @@ -358,6 +364,7 @@ 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: @@ -373,7 +380,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf')` {#LaplaceWithSoftplusScale.pmf} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf', **condition_kwargs)` {#LaplaceWithSoftplusScale.pmf} Probability mass function. @@ -382,6 +389,7 @@ 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: @@ -397,7 +405,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob')` {#LaplaceWithSoftplusScale.prob} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob', **condition_kwargs)` {#LaplaceWithSoftplusScale.prob} Probability density/mass function (depending on `is_continuous`). @@ -406,6 +414,7 @@ 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 +425,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample(sample_shape=(), seed=None, name='sample')` {#LaplaceWithSoftplusScale.sample} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#LaplaceWithSoftplusScale.sample} Generate samples of the specified shape. @@ -429,6 +438,7 @@ 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: @@ -438,7 +448,7 @@ sample. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample_n(n, seed=None, name='sample_n')` {#LaplaceWithSoftplusScale.sample_n} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#LaplaceWithSoftplusScale.sample_n} Generate `n` samples. @@ -449,6 +459,7 @@ Generate `n` samples. observations to sample. * <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: @@ -477,7 +488,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.LaplaceWithSoftplusScale.survival_function(value, name='survival_function')` {#LaplaceWithSoftplusScale.survival_function} +#### `tf.contrib.distributions.LaplaceWithSoftplusScale.survival_function(value, name='survival_function', **condition_kwargs)` {#LaplaceWithSoftplusScale.survival_function} Survival function. @@ -494,6 +505,7 @@ 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 69f8e7b92b..8b19a5d0a5 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')` {#StudentTWithAbsDfSoftplusSigma.cdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.cdf(value, name='cdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.cdf} Cumulative distribution function. @@ -64,6 +64,7 @@ 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: @@ -90,7 +91,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.entropy(name='entropy')` {#StudentTWithAbsDfSoftplusSigma.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -154,7 +155,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_cdf(value, name='log_cdf')` {#StudentTWithAbsDfSoftplusSigma.log_cdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_cdf} Log cumulative distribution function. @@ -173,6 +174,7 @@ 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: @@ -183,7 +185,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pdf(value, name='log_pdf')` {#StudentTWithAbsDfSoftplusSigma.log_pdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_pdf} Log probability density function. @@ -192,6 +194,7 @@ 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: @@ -207,7 +210,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pmf(value, name='log_pmf')` {#StudentTWithAbsDfSoftplusSigma.log_pmf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_pmf} Log probability mass function. @@ -216,6 +219,7 @@ 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: @@ -231,7 +235,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob(value, name='log_prob')` {#StudentTWithAbsDfSoftplusSigma.log_prob} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob(value, name='log_prob', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -240,6 +244,7 @@ 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: @@ -250,7 +255,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_survival_function(value, name='log_survival_function')` {#StudentTWithAbsDfSoftplusSigma.log_survival_function} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.log_survival_function} Log survival function. @@ -270,6 +275,7 @@ 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: @@ -362,7 +368,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pdf(value, name='pdf')` {#StudentTWithAbsDfSoftplusSigma.pdf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pdf(value, name='pdf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.pdf} Probability density function. @@ -371,6 +377,7 @@ 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: @@ -386,7 +393,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pmf(value, name='pmf')` {#StudentTWithAbsDfSoftplusSigma.pmf} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.pmf(value, name='pmf', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.pmf} Probability mass function. @@ -395,6 +402,7 @@ 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: @@ -410,7 +418,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.prob(value, name='prob')` {#StudentTWithAbsDfSoftplusSigma.prob} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.prob(value, name='prob', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.prob} Probability density/mass function (depending on `is_continuous`). @@ -419,6 +427,7 @@ 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: @@ -429,7 +438,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample(sample_shape=(), seed=None, name='sample')` {#StudentTWithAbsDfSoftplusSigma.sample} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.sample} Generate samples of the specified shape. @@ -442,6 +451,7 @@ 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: @@ -451,7 +461,7 @@ sample. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample_n(n, seed=None, name='sample_n')` {#StudentTWithAbsDfSoftplusSigma.sample_n} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.sample_n} Generate `n` samples. @@ -462,6 +472,7 @@ Generate `n` samples. observations to sample. * <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: @@ -490,7 +501,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.survival_function(value, name='survival_function')` {#StudentTWithAbsDfSoftplusSigma.survival_function} +#### `tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.survival_function(value, name='survival_function', **condition_kwargs)` {#StudentTWithAbsDfSoftplusSigma.survival_function} Survival function. @@ -507,6 +518,7 @@ 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.Identity.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.bijector.Identity.md index 8f7f3c4f2f..74c1df822e 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')` {#Identity.forward} +#### `tf.contrib.distributions.bijector.Identity.forward(x, name='forward', **condition_kwargs)` {#Identity.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -35,6 +35,7 @@ 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: @@ -50,15 +51,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.Identity.inverse(x, name='inverse')` {#Identity.inverse} +#### `tf.contrib.distributions.bijector.Identity.inverse(y, name='inverse', **condition_kwargs)` {#Identity.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -67,7 +69,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -75,7 +77,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Identity.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#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', **condition_kwargs)` {#Identity.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -87,8 +89,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -97,7 +100,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -105,20 +108,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.Identity.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#Identity.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Identity.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Identity.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -127,7 +130,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. 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 97e926c55f..c02f781d50 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')` {#ExponentialWithSoftplusLam.cdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.cdf(value, name='cdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.cdf} Cumulative distribution function. @@ -78,6 +78,7 @@ 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: @@ -97,7 +98,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.ExponentialWithSoftplusLam.entropy(name='entropy')` {#ExponentialWithSoftplusLam.entropy} -Shanon entropy in nats. +Shannon entropy in nats. Additional documentation from `Gamma`: @@ -179,7 +180,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf')` {#ExponentialWithSoftplusLam.log_cdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_cdf} Log cumulative distribution function. @@ -198,6 +199,7 @@ 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: @@ -208,7 +210,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf')` {#ExponentialWithSoftplusLam.log_pdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_pdf} Log probability density function. @@ -217,6 +219,7 @@ 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: @@ -232,7 +235,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf')` {#ExponentialWithSoftplusLam.log_pmf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_pmf} Log probability mass function. @@ -241,6 +244,7 @@ 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: @@ -256,7 +260,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob')` {#ExponentialWithSoftplusLam.log_prob} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -265,6 +269,7 @@ 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: @@ -275,7 +280,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_survival_function(value, name='log_survival_function')` {#ExponentialWithSoftplusLam.log_survival_function} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#ExponentialWithSoftplusLam.log_survival_function} Log survival function. @@ -295,6 +300,7 @@ 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: @@ -380,7 +386,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf')` {#ExponentialWithSoftplusLam.pdf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf', **condition_kwargs)` {#ExponentialWithSoftplusLam.pdf} Probability density function. @@ -389,6 +395,7 @@ 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: @@ -404,7 +411,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf')` {#ExponentialWithSoftplusLam.pmf} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf', **condition_kwargs)` {#ExponentialWithSoftplusLam.pmf} Probability mass function. @@ -413,6 +420,7 @@ 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: @@ -428,7 +436,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob')` {#ExponentialWithSoftplusLam.prob} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob', **condition_kwargs)` {#ExponentialWithSoftplusLam.prob} Probability density/mass function (depending on `is_continuous`). @@ -437,6 +445,7 @@ 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: @@ -447,7 +456,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample(sample_shape=(), seed=None, name='sample')` {#ExponentialWithSoftplusLam.sample} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#ExponentialWithSoftplusLam.sample} Generate samples of the specified shape. @@ -460,6 +469,7 @@ 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: @@ -469,7 +479,7 @@ sample. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample_n(n, seed=None, name='sample_n')` {#ExponentialWithSoftplusLam.sample_n} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#ExponentialWithSoftplusLam.sample_n} Generate `n` samples. @@ -485,6 +495,7 @@ See the documentation for tf.random_gamma for more details. observations to sample. * <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: @@ -506,7 +517,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.ExponentialWithSoftplusLam.survival_function(value, name='survival_function')` {#ExponentialWithSoftplusLam.survival_function} +#### `tf.contrib.distributions.ExponentialWithSoftplusLam.survival_function(value, name='survival_function', **condition_kwargs)` {#ExponentialWithSoftplusLam.survival_function} Survival function. @@ -523,6 +534,7 @@ 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 4774b9c8ba..556deaebec 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')` {#MultivariateNormalFull.cdf} +#### `tf.contrib.distributions.MultivariateNormalFull.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalFull.cdf} Cumulative distribution function. @@ -125,6 +125,7 @@ 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: @@ -144,7 +145,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.MultivariateNormalFull.entropy(name='entropy')` {#MultivariateNormalFull.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -208,7 +209,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_cdf(value, name='log_cdf')` {#MultivariateNormalFull.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalFull.log_cdf} Log cumulative distribution function. @@ -227,6 +228,7 @@ 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: @@ -237,7 +239,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf')` {#MultivariateNormalFull.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalFull.log_pdf} Log probability density function. @@ -246,6 +248,7 @@ 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: @@ -261,7 +264,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf')` {#MultivariateNormalFull.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalFull.log_pmf} Log probability mass function. @@ -270,6 +273,7 @@ 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 +289,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob')` {#MultivariateNormalFull.log_prob} +#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -310,6 +314,7 @@ 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: @@ -327,7 +332,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalFull.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalFull.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalFull.log_survival_function} Log survival function. @@ -347,6 +352,7 @@ 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: @@ -433,7 +439,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf')` {#MultivariateNormalFull.pdf} +#### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalFull.pdf} Probability density function. @@ -442,6 +448,7 @@ 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: @@ -457,7 +464,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf')` {#MultivariateNormalFull.pmf} +#### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalFull.pmf} Probability mass function. @@ -466,6 +473,7 @@ 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: @@ -481,7 +489,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob')` {#MultivariateNormalFull.prob} +#### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalFull.prob} Probability density/mass function (depending on `is_continuous`). @@ -506,6 +514,7 @@ 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: @@ -516,7 +525,7 @@ or - - - -#### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalFull.sample} +#### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalFull.sample} Generate samples of the specified shape. @@ -529,6 +538,7 @@ 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: @@ -538,7 +548,7 @@ sample. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalFull.sample_n} +#### `tf.contrib.distributions.MultivariateNormalFull.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#MultivariateNormalFull.sample_n} Generate `n` samples. @@ -549,6 +559,7 @@ Generate `n` samples. observations to sample. * <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: @@ -584,7 +595,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalFull.survival_function(value, name='survival_function')` {#MultivariateNormalFull.survival_function} +#### `tf.contrib.distributions.MultivariateNormalFull.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalFull.survival_function} Survival function. @@ -601,6 +612,7 @@ 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 cde9b21840..7afe3597d5 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')` {#Normal.cdf} +#### `tf.contrib.distributions.Normal.cdf(value, name='cdf', **condition_kwargs)` {#Normal.cdf} Cumulative distribution function. @@ -128,6 +128,7 @@ 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: @@ -147,7 +148,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Normal.entropy(name='entropy')` {#Normal.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -211,7 +212,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf')` {#Normal.log_cdf} +#### `tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Normal.log_cdf} Log cumulative distribution function. @@ -230,6 +231,7 @@ 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: @@ -240,7 +242,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf')` {#Normal.log_pdf} +#### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Normal.log_pdf} Log probability density function. @@ -249,6 +251,7 @@ 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: @@ -264,7 +267,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf')` {#Normal.log_pmf} +#### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Normal.log_pmf} Log probability mass function. @@ -273,6 +276,7 @@ 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: @@ -288,7 +292,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob')` {#Normal.log_prob} +#### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob', **condition_kwargs)` {#Normal.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -297,6 +301,7 @@ 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: @@ -307,7 +312,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Normal.log_survival_function(value, name='log_survival_function')` {#Normal.log_survival_function} +#### `tf.contrib.distributions.Normal.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Normal.log_survival_function} Log survival function. @@ -327,6 +332,7 @@ 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: @@ -413,7 +419,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Normal.pdf(value, name='pdf')` {#Normal.pdf} +#### `tf.contrib.distributions.Normal.pdf(value, name='pdf', **condition_kwargs)` {#Normal.pdf} Probability density function. @@ -422,6 +428,7 @@ 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: @@ -437,7 +444,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Normal.pmf(value, name='pmf')` {#Normal.pmf} +#### `tf.contrib.distributions.Normal.pmf(value, name='pmf', **condition_kwargs)` {#Normal.pmf} Probability mass function. @@ -446,6 +453,7 @@ 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: @@ -461,7 +469,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Normal.prob(value, name='prob')` {#Normal.prob} +#### `tf.contrib.distributions.Normal.prob(value, name='prob', **condition_kwargs)` {#Normal.prob} Probability density/mass function (depending on `is_continuous`). @@ -470,6 +478,7 @@ 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: @@ -480,7 +489,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample')` {#Normal.sample} +#### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Normal.sample} Generate samples of the specified shape. @@ -493,6 +502,7 @@ 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: @@ -502,7 +512,7 @@ sample. - - - -#### `tf.contrib.distributions.Normal.sample_n(n, seed=None, name='sample_n')` {#Normal.sample_n} +#### `tf.contrib.distributions.Normal.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Normal.sample_n} Generate `n` samples. @@ -513,6 +523,7 @@ Generate `n` samples. observations to sample. * <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: @@ -541,7 +552,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Normal.survival_function(value, name='survival_function')` {#Normal.survival_function} +#### `tf.contrib.distributions.Normal.survival_function(value, name='survival_function', **condition_kwargs)` {#Normal.survival_function} Survival function. @@ -558,6 +569,7 @@ 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 439988379b..4d48a3e4a1 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 @@ -39,7 +39,7 @@ dtype of `Tensor`s transformable by this distribution. - - - -#### `tf.contrib.distributions.bijector.Inline.forward(x, name='forward')` {#Inline.forward} +#### `tf.contrib.distributions.bijector.Inline.forward(x, name='forward', **condition_kwargs)` {#Inline.forward} Returns the forward `Bijector` evaluation, i.e., X = g(Y). @@ -48,6 +48,7 @@ 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: @@ -63,15 +64,16 @@ Returns the forward `Bijector` evaluation, i.e., X = g(Y). - - - -#### `tf.contrib.distributions.bijector.Inline.inverse(x, name='inverse')` {#Inline.inverse} +#### `tf.contrib.distributions.bijector.Inline.inverse(y, name='inverse', **condition_kwargs)` {#Inline.inverse} Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" evaluation. +* <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: @@ -80,7 +82,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse` nor `_inverse_and_inverse_log_det_jacobian` are implemented. @@ -88,7 +90,7 @@ Returns the inverse `Bijector` evaluation, i.e., X = g^{-1}(Y). - - - -#### `tf.contrib.distributions.bijector.Inline.inverse_and_inverse_log_det_jacobian(x, name='inverse_and_inverse_log_det_jacobian')` {#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', **condition_kwargs)` {#Inline.inverse_and_inverse_log_det_jacobian} Returns both the inverse evaluation and inverse_log_det_jacobian. @@ -100,8 +102,9 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -110,7 +113,7 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_and_inverse_log_det_jacobian` nor {`_inverse`, `_inverse_log_det_jacobian`} are implemented. @@ -118,20 +121,20 @@ See `inverse()`, `inverse_log_det_jacobian()` for more details. - - - -#### `tf.contrib.distributions.bijector.Inline.inverse_log_det_jacobian(x, name='inverse_log_det_jacobian')` {#Inline.inverse_log_det_jacobian} +#### `tf.contrib.distributions.bijector.Inline.inverse_log_det_jacobian(y, name='inverse_log_det_jacobian', **condition_kwargs)` {#Inline.inverse_log_det_jacobian} -Returns the (log o det o Jacobian o inverse)(x). +Returns the (log o det o Jacobian o inverse)(y). -Mathematically, returns: log(det(dY/dX g^{-1}))(Y). +Mathematically, returns: `log(det(dX/dY))(Y)`. (Recall that: `X=g^{-1}(Y)`.) -Note that forward_log_det_jacobian is the negative of this function. (See -is_constant_jacobian for related proof.) +Note that `forward_log_det_jacobian` is the negative of this function. ##### Args: -* <b>`x`</b>: `Tensor`. The input to the "inverse" Jacobian evaluation. +* <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: @@ -140,7 +143,7 @@ is_constant_jacobian for related proof.) ##### Raises: -* <b>`TypeError`</b>: if `self.dtype` is specified and `x.dtype` is not +* <b>`TypeError`</b>: if `self.dtype` is specified and `y.dtype` is not `self.dtype`. * <b>`NotImplementedError`</b>: if neither `_inverse_log_det_jacobian` nor `_inverse_and_inverse_log_det_jacobian` are implemented. 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 a30ec2b22f..70cfbb7c81 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')` {#Mixture.cdf} +#### `tf.contrib.distributions.Mixture.cdf(value, name='cdf', **condition_kwargs)` {#Mixture.cdf} Cumulative distribution function. @@ -118,6 +118,7 @@ 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: @@ -144,7 +145,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Mixture.entropy(name='entropy')` {#Mixture.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -254,7 +255,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.Mixture.log_cdf(value, name='log_cdf')` {#Mixture.log_cdf} +#### `tf.contrib.distributions.Mixture.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Mixture.log_cdf} Log cumulative distribution function. @@ -273,6 +274,7 @@ 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: @@ -283,7 +285,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Mixture.log_pdf(value, name='log_pdf')` {#Mixture.log_pdf} +#### `tf.contrib.distributions.Mixture.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Mixture.log_pdf} Log probability density function. @@ -292,6 +294,7 @@ 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 +310,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf')` {#Mixture.log_pmf} +#### `tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Mixture.log_pmf} Log probability mass function. @@ -316,6 +319,7 @@ 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: @@ -331,7 +335,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Mixture.log_prob(value, name='log_prob')` {#Mixture.log_prob} +#### `tf.contrib.distributions.Mixture.log_prob(value, name='log_prob', **condition_kwargs)` {#Mixture.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -340,6 +344,7 @@ 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: @@ -350,7 +355,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Mixture.log_survival_function(value, name='log_survival_function')` {#Mixture.log_survival_function} +#### `tf.contrib.distributions.Mixture.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Mixture.log_survival_function} Log survival function. @@ -370,6 +375,7 @@ 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: @@ -456,7 +462,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Mixture.pdf(value, name='pdf')` {#Mixture.pdf} +#### `tf.contrib.distributions.Mixture.pdf(value, name='pdf', **condition_kwargs)` {#Mixture.pdf} Probability density function. @@ -465,6 +471,7 @@ 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: @@ -480,7 +487,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Mixture.pmf(value, name='pmf')` {#Mixture.pmf} +#### `tf.contrib.distributions.Mixture.pmf(value, name='pmf', **condition_kwargs)` {#Mixture.pmf} Probability mass function. @@ -489,6 +496,7 @@ 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 +512,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Mixture.prob(value, name='prob')` {#Mixture.prob} +#### `tf.contrib.distributions.Mixture.prob(value, name='prob', **condition_kwargs)` {#Mixture.prob} Probability density/mass function (depending on `is_continuous`). @@ -513,6 +521,7 @@ 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: @@ -523,7 +532,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.Mixture.sample(sample_shape=(), seed=None, name='sample')` {#Mixture.sample} +#### `tf.contrib.distributions.Mixture.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Mixture.sample} Generate samples of the specified shape. @@ -536,6 +545,7 @@ 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: @@ -545,7 +555,7 @@ sample. - - - -#### `tf.contrib.distributions.Mixture.sample_n(n, seed=None, name='sample_n')` {#Mixture.sample_n} +#### `tf.contrib.distributions.Mixture.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Mixture.sample_n} Generate `n` samples. @@ -556,6 +566,7 @@ Generate `n` samples. observations to sample. * <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: @@ -577,7 +588,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Mixture.survival_function(value, name='survival_function')` {#Mixture.survival_function} +#### `tf.contrib.distributions.Mixture.survival_function(value, name='survival_function', **condition_kwargs)` {#Mixture.survival_function} Survival function. @@ -594,6 +605,7 @@ 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 c17b970f26..2d392c9c79 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')` {#NormalWithSoftplusSigma.cdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.cdf(value, name='cdf', **condition_kwargs)` {#NormalWithSoftplusSigma.cdf} Cumulative distribution function. @@ -64,6 +64,7 @@ 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: @@ -83,7 +84,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.NormalWithSoftplusSigma.entropy(name='entropy')` {#NormalWithSoftplusSigma.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -147,7 +148,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_cdf(value, name='log_cdf')` {#NormalWithSoftplusSigma.log_cdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_cdf(value, name='log_cdf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_cdf} Log cumulative distribution function. @@ -166,6 +167,7 @@ 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: @@ -176,7 +178,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pdf(value, name='log_pdf')` {#NormalWithSoftplusSigma.log_pdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pdf(value, name='log_pdf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_pdf} Log probability density function. @@ -185,6 +187,7 @@ 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: @@ -200,7 +203,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pmf(value, name='log_pmf')` {#NormalWithSoftplusSigma.log_pmf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_pmf(value, name='log_pmf', **condition_kwargs)` {#NormalWithSoftplusSigma.log_pmf} Log probability mass function. @@ -209,6 +212,7 @@ 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: @@ -224,7 +228,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_prob(value, name='log_prob')` {#NormalWithSoftplusSigma.log_prob} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_prob(value, name='log_prob', **condition_kwargs)` {#NormalWithSoftplusSigma.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -233,6 +237,7 @@ 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: @@ -243,7 +248,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_survival_function(value, name='log_survival_function')` {#NormalWithSoftplusSigma.log_survival_function} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#NormalWithSoftplusSigma.log_survival_function} Log survival function. @@ -263,6 +268,7 @@ 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: @@ -349,7 +355,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.pdf(value, name='pdf')` {#NormalWithSoftplusSigma.pdf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.pdf(value, name='pdf', **condition_kwargs)` {#NormalWithSoftplusSigma.pdf} Probability density function. @@ -358,6 +364,7 @@ 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: @@ -373,7 +380,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.pmf(value, name='pmf')` {#NormalWithSoftplusSigma.pmf} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.pmf(value, name='pmf', **condition_kwargs)` {#NormalWithSoftplusSigma.pmf} Probability mass function. @@ -382,6 +389,7 @@ 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: @@ -397,7 +405,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.prob(value, name='prob')` {#NormalWithSoftplusSigma.prob} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.prob(value, name='prob', **condition_kwargs)` {#NormalWithSoftplusSigma.prob} Probability density/mass function (depending on `is_continuous`). @@ -406,6 +414,7 @@ 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 +425,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample(sample_shape=(), seed=None, name='sample')` {#NormalWithSoftplusSigma.sample} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#NormalWithSoftplusSigma.sample} Generate samples of the specified shape. @@ -429,6 +438,7 @@ 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: @@ -438,7 +448,7 @@ sample. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample_n(n, seed=None, name='sample_n')` {#NormalWithSoftplusSigma.sample_n} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#NormalWithSoftplusSigma.sample_n} Generate `n` samples. @@ -449,6 +459,7 @@ Generate `n` samples. observations to sample. * <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: @@ -477,7 +488,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.NormalWithSoftplusSigma.survival_function(value, name='survival_function')` {#NormalWithSoftplusSigma.survival_function} +#### `tf.contrib.distributions.NormalWithSoftplusSigma.survival_function(value, name='survival_function', **condition_kwargs)` {#NormalWithSoftplusSigma.survival_function} Survival function. @@ -494,6 +505,7 @@ 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.MultivariateNormalDiagWithSoftplusStDev.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.md index 2ffbbbf3a3..671f0fd61f 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')` {#MultivariateNormalDiagWithSoftplusStDev.cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.cdf(value, name='cdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.cdf} Cumulative distribution function. @@ -64,6 +64,7 @@ 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: @@ -83,7 +84,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.entropy(name='entropy')` {#MultivariateNormalDiagWithSoftplusStDev.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -147,7 +148,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiagWithSoftplusStDev.log_cdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_cdf(value, name='log_cdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_cdf} Log cumulative distribution function. @@ -166,6 +167,7 @@ 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: @@ -176,7 +178,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_pdf} Log probability density function. @@ -185,6 +187,7 @@ 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: @@ -200,7 +203,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagWithSoftplusStDev.log_pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_pmf} Log probability mass function. @@ -209,6 +212,7 @@ 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: @@ -224,7 +228,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob')` {#MultivariateNormalDiagWithSoftplusStDev.log_prob} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -249,6 +253,7 @@ 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: @@ -266,7 +271,7 @@ Log of determinant of covariance matrix. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_survival_function(value, name='log_survival_function')` {#MultivariateNormalDiagWithSoftplusStDev.log_survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.log_survival_function} Log survival function. @@ -286,6 +291,7 @@ 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: @@ -372,7 +378,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf')` {#MultivariateNormalDiagWithSoftplusStDev.pdf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.pdf} Probability density function. @@ -381,6 +387,7 @@ 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: @@ -396,7 +403,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf')` {#MultivariateNormalDiagWithSoftplusStDev.pmf} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.pmf} Probability mass function. @@ -405,6 +412,7 @@ 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 +428,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob')` {#MultivariateNormalDiagWithSoftplusStDev.prob} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.prob} Probability density/mass function (depending on `is_continuous`). @@ -445,6 +453,7 @@ 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: @@ -455,7 +464,7 @@ or - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiagWithSoftplusStDev.sample} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.sample} Generate samples of the specified shape. @@ -468,6 +477,7 @@ 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: @@ -477,7 +487,7 @@ sample. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalDiagWithSoftplusStDev.sample_n} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.sample_n} Generate `n` samples. @@ -488,6 +498,7 @@ Generate `n` samples. observations to sample. * <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: @@ -523,7 +534,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.survival_function(value, name='survival_function')` {#MultivariateNormalDiagWithSoftplusStDev.survival_function} +#### `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.survival_function(value, name='survival_function', **condition_kwargs)` {#MultivariateNormalDiagWithSoftplusStDev.survival_function} Survival function. @@ -540,6 +551,7 @@ 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 7653beb031..0c0e9447c6 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')` {#Poisson.cdf} +#### `tf.contrib.distributions.Poisson.cdf(value, name='cdf', **condition_kwargs)` {#Poisson.cdf} Cumulative distribution function. @@ -88,6 +88,7 @@ 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: @@ -107,7 +108,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.Poisson.entropy(name='entropy')` {#Poisson.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -178,7 +179,7 @@ Rate parameter. - - - -#### `tf.contrib.distributions.Poisson.log_cdf(value, name='log_cdf')` {#Poisson.log_cdf} +#### `tf.contrib.distributions.Poisson.log_cdf(value, name='log_cdf', **condition_kwargs)` {#Poisson.log_cdf} Log cumulative distribution function. @@ -197,6 +198,7 @@ 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: @@ -207,7 +209,7 @@ a more accurate answer than simply taking the logarithm of the `cdf` when - - - -#### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf')` {#Poisson.log_pdf} +#### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf', **condition_kwargs)` {#Poisson.log_pdf} Log probability density function. @@ -216,6 +218,7 @@ 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: @@ -231,7 +234,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf')` {#Poisson.log_pmf} +#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf', **condition_kwargs)` {#Poisson.log_pmf} Log probability mass function. @@ -240,6 +243,7 @@ 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: @@ -255,7 +259,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob')` {#Poisson.log_prob} +#### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob', **condition_kwargs)` {#Poisson.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -271,6 +275,7 @@ 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: @@ -281,7 +286,7 @@ 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')` {#Poisson.log_survival_function} +#### `tf.contrib.distributions.Poisson.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#Poisson.log_survival_function} Log survival function. @@ -301,6 +306,7 @@ 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: @@ -386,7 +392,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf')` {#Poisson.pdf} +#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf', **condition_kwargs)` {#Poisson.pdf} Probability density function. @@ -395,6 +401,7 @@ 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: @@ -410,7 +417,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf')` {#Poisson.pmf} +#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf', **condition_kwargs)` {#Poisson.pmf} Probability mass function. @@ -419,6 +426,7 @@ 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: @@ -434,7 +442,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.Poisson.prob(value, name='prob')` {#Poisson.prob} +#### `tf.contrib.distributions.Poisson.prob(value, name='prob', **condition_kwargs)` {#Poisson.prob} Probability density/mass function (depending on `is_continuous`). @@ -450,6 +458,7 @@ 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: @@ -460,7 +469,7 @@ legal if it is non-negative and its components are equal to integer values. - - - -#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample')` {#Poisson.sample} +#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#Poisson.sample} Generate samples of the specified shape. @@ -473,6 +482,7 @@ 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: @@ -482,7 +492,7 @@ sample. - - - -#### `tf.contrib.distributions.Poisson.sample_n(n, seed=None, name='sample_n')` {#Poisson.sample_n} +#### `tf.contrib.distributions.Poisson.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#Poisson.sample_n} Generate `n` samples. @@ -493,6 +503,7 @@ Generate `n` samples. observations to sample. * <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: @@ -514,7 +525,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.Poisson.survival_function(value, name='survival_function')` {#Poisson.survival_function} +#### `tf.contrib.distributions.Poisson.survival_function(value, name='survival_function', **condition_kwargs)` {#Poisson.survival_function} Survival function. @@ -531,6 +542,7 @@ 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 45d97bfd75..0588ef83ea 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')` {#WishartFull.cdf} +#### `tf.contrib.distributions.WishartFull.cdf(value, name='cdf', **condition_kwargs)` {#WishartFull.cdf} Cumulative distribution function. @@ -139,6 +139,7 @@ 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: @@ -179,7 +180,7 @@ The `DType` of `Tensor`s handled by this `Distribution`. #### `tf.contrib.distributions.WishartFull.entropy(name='entropy')` {#WishartFull.entropy} -Shanon entropy in nats. +Shannon entropy in nats. - - - @@ -243,7 +244,7 @@ Same meaning as `event_shape`. May be only partially defined. - - - -#### `tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf')` {#WishartFull.log_cdf} +#### `tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf', **condition_kwargs)` {#WishartFull.log_cdf} Log cumulative distribution function. @@ -262,6 +263,7 @@ 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: @@ -279,7 +281,7 @@ Computes the log normalizing constant, log(Z). - - - -#### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf')` {#WishartFull.log_pdf} +#### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf', **condition_kwargs)` {#WishartFull.log_pdf} Log probability density function. @@ -288,6 +290,7 @@ 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: @@ -303,7 +306,7 @@ Log probability density function. - - - -#### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf')` {#WishartFull.log_pmf} +#### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf', **condition_kwargs)` {#WishartFull.log_pmf} Log probability mass function. @@ -312,6 +315,7 @@ 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: @@ -327,7 +331,7 @@ Log probability mass function. - - - -#### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob')` {#WishartFull.log_prob} +#### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob', **condition_kwargs)` {#WishartFull.log_prob} Log probability density/mass function (depending on `is_continuous`). @@ -336,6 +340,7 @@ 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: @@ -346,7 +351,7 @@ Log probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartFull.log_survival_function(value, name='log_survival_function')` {#WishartFull.log_survival_function} +#### `tf.contrib.distributions.WishartFull.log_survival_function(value, name='log_survival_function', **condition_kwargs)` {#WishartFull.log_survival_function} Log survival function. @@ -366,6 +371,7 @@ 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: @@ -452,7 +458,7 @@ Dictionary of parameters used by this `Distribution`. - - - -#### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf')` {#WishartFull.pdf} +#### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf', **condition_kwargs)` {#WishartFull.pdf} Probability density function. @@ -461,6 +467,7 @@ 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: @@ -476,7 +483,7 @@ Probability density function. - - - -#### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf')` {#WishartFull.pmf} +#### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf', **condition_kwargs)` {#WishartFull.pmf} Probability mass function. @@ -485,6 +492,7 @@ 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 +508,7 @@ Probability mass function. - - - -#### `tf.contrib.distributions.WishartFull.prob(value, name='prob')` {#WishartFull.prob} +#### `tf.contrib.distributions.WishartFull.prob(value, name='prob', **condition_kwargs)` {#WishartFull.prob} Probability density/mass function (depending on `is_continuous`). @@ -509,6 +517,7 @@ 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: @@ -519,7 +528,7 @@ Probability density/mass function (depending on `is_continuous`). - - - -#### `tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample')` {#WishartFull.sample} +#### `tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)` {#WishartFull.sample} Generate samples of the specified shape. @@ -532,6 +541,7 @@ 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: @@ -541,7 +551,7 @@ sample. - - - -#### `tf.contrib.distributions.WishartFull.sample_n(n, seed=None, name='sample_n')` {#WishartFull.sample_n} +#### `tf.contrib.distributions.WishartFull.sample_n(n, seed=None, name='sample_n', **condition_kwargs)` {#WishartFull.sample_n} Generate `n` samples. @@ -552,6 +562,7 @@ Generate `n` samples. observations to sample. * <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: @@ -587,7 +598,7 @@ Standard deviation. - - - -#### `tf.contrib.distributions.WishartFull.survival_function(value, name='survival_function')` {#WishartFull.survival_function} +#### `tf.contrib.distributions.WishartFull.survival_function(value, name='survival_function', **condition_kwargs)` {#WishartFull.survival_function} Survival function. @@ -604,6 +615,7 @@ 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.learn.DNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md index 4943db998e..6eb8c204e6 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md @@ -66,9 +66,9 @@ Initializes a `DNNRegressor` instance. * <b>`feature_columns`</b>: An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from `FeatureColumn`. -* <b>`model_dir`</b>: Directory to save model parameters, graph and etc. This can also - be used to load checkpoints from the directory into a estimator to continue - training a previously saved model. +* <b>`model_dir`</b>: Directory to save model parameters, graph and etc. This can + also be used to load checkpoints from the directory into a estimator to + continue training a previously saved model. * <b>`weight_column_name`</b>: A string defining feature column name representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. @@ -176,7 +176,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md index 852d9199f9..bec8cab633 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md @@ -117,7 +117,7 @@ The signature of the input_fn accepted by export is changing to be consistent wi string key to `Tensor` and targets is a `Tensor` that's currently not used (and so can be `None`). input_feature_key: Only used if `use_deprecated_input_fn` is false. String - key into the features dict returned by `input_fn` that corresponds toa + key into the features dict returned by `input_fn` that corresponds to a the raw `Example` strings `Tensor` that the exported model will take as input. Can only be `None` if you're using a custom `signature_fn` that does not use the first arg (examples). |