diff options
author | A. Unique TensorFlower <gardener@tensorflow.org> | 2017-02-11 15:48:33 -0800 |
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committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-02-11 16:08:16 -0800 |
commit | b40775aa7a87b51d97994edb27fe3ac5d7a5449a (patch) | |
tree | 0e0d9d4bd92903fa3e4cf3cdad75f010317149df /tensorflow/g3doc | |
parent | b0f76d112be9190ac03f5b6083afb12aa6bdbc35 (diff) |
Update generated Python Op docs.
Change: 147257715
Diffstat (limited to 'tensorflow/g3doc')
5 files changed, 26 insertions, 30 deletions
diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md index 746adfcf4c..e1f4518456 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md +++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md @@ -15073,8 +15073,7 @@ arguments. The `event_shape` is given by the last dimension of `loc` or the last dimension of the matrix implied by `scale`. -Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix -is: +Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix is: ```none scale = diag(scale_diag + scale_identity_multiplier * ones(k)) @@ -15095,7 +15094,7 @@ If both `scale_diag` and `scale_identity_multiplier` are `None`, then * <b>`loc`</b>: Floating-point `Tensor`. If this is set to `None`, `loc` is implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where - `b >= 0` and `k` represents the event size. + `b >= 0` and `k` is the event size. * <b>`scale_diag`</b>: Non-zero, floating-point `Tensor` representing a diagonal matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`, and characterizes `b`-batches of `k x k` diagonal matrices added to @@ -15430,7 +15429,7 @@ Log of determinant of covariance matrix. Log probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: @@ -15576,7 +15575,7 @@ Dictionary of parameters used to instantiate this `Distribution`. Probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: @@ -15784,7 +15783,7 @@ X ~ MultivariateNormal(loc=0, scale=1) # Identity scale, zero shift. Y = scale @ X + loc ``` -Trainable (batch) Cholesky matrices can be created with +Trainable (batch) lower-triangular matrices can be created with `ds.matrix_diag_transform()` and/or `ds.fill_lower_triangular()` #### Examples @@ -15843,8 +15842,7 @@ arguments. The `event_shape` is given by the last dimension of `loc` or the last dimension of the matrix implied by `scale`. -Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix -is: +Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix is: ```none scale = scale_tril @@ -16187,7 +16185,7 @@ Log of determinant of covariance matrix. Log probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: @@ -16333,7 +16331,7 @@ Dictionary of parameters used to instantiate this `Distribution`. Probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: @@ -16640,7 +16638,7 @@ If both `scale_diag` and `scale_identity_multiplier` are `None`, then * <b>`loc`</b>: Floating-point `Tensor`. If this is set to `None`, `loc` is implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where - `b >= 0` and `k` represents the event size. + `b >= 0` and `k` is the event size. * <b>`scale_diag`</b>: Non-zero, floating-point `Tensor` representing a diagonal matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`, and characterizes `b`-batches of `k x k` diagonal matrices added to @@ -16985,7 +16983,7 @@ Log of determinant of covariance matrix. Log probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: @@ -17131,7 +17129,7 @@ Dictionary of parameters used to instantiate this `Distribution`. Probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: @@ -17610,7 +17608,7 @@ Log of determinant of covariance matrix. Log probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: @@ -17756,7 +17754,7 @@ Dictionary of parameters used to instantiate this `Distribution`. Probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: 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 442f79c9f1..6724502112 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 @@ -109,8 +109,7 @@ arguments. The `event_shape` is given by the last dimension of `loc` or the last dimension of the matrix implied by `scale`. -Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix -is: +Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix is: ```none scale = diag(scale_diag + scale_identity_multiplier * ones(k)) @@ -131,7 +130,7 @@ If both `scale_diag` and `scale_identity_multiplier` are `None`, then * <b>`loc`</b>: Floating-point `Tensor`. If this is set to `None`, `loc` is implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where - `b >= 0` and `k` represents the event size. + `b >= 0` and `k` is the event size. * <b>`scale_diag`</b>: Non-zero, floating-point `Tensor` representing a diagonal matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`, and characterizes `b`-batches of `k x k` diagonal matrices added to @@ -466,7 +465,7 @@ Log of determinant of covariance matrix. Log probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: @@ -612,7 +611,7 @@ Dictionary of parameters used to instantiate this `Distribution`. Probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScale.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScale.md index 7eb5d654e1..fcab6b60bd 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScale.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScale.md @@ -313,7 +313,7 @@ Log of determinant of covariance matrix. Log probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: @@ -459,7 +459,7 @@ Dictionary of parameters used to instantiate this `Distribution`. Probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalDiagPlusLowRank.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalDiagPlusLowRank.md index 69b4552d54..4c70d93a55 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalDiagPlusLowRank.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalDiagPlusLowRank.md @@ -141,7 +141,7 @@ If both `scale_diag` and `scale_identity_multiplier` are `None`, then * <b>`loc`</b>: Floating-point `Tensor`. If this is set to `None`, `loc` is implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where - `b >= 0` and `k` represents the event size. + `b >= 0` and `k` is the event size. * <b>`scale_diag`</b>: Non-zero, floating-point `Tensor` representing a diagonal matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`, and characterizes `b`-batches of `k x k` diagonal matrices added to @@ -486,7 +486,7 @@ Log of determinant of covariance matrix. Log probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: @@ -632,7 +632,7 @@ Dictionary of parameters used to instantiate this `Distribution`. Probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalTriL.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalTriL.md index f7e8da4bd8..5c79f551bf 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalTriL.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.MultivariateNormalTriL.md @@ -42,7 +42,7 @@ X ~ MultivariateNormal(loc=0, scale=1) # Identity scale, zero shift. Y = scale @ X + loc ``` -Trainable (batch) Cholesky matrices can be created with +Trainable (batch) lower-triangular matrices can be created with `ds.matrix_diag_transform()` and/or `ds.fill_lower_triangular()` #### Examples @@ -101,8 +101,7 @@ arguments. The `event_shape` is given by the last dimension of `loc` or the last dimension of the matrix implied by `scale`. -Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix -is: +Recall that `covariance = scale @ scale.T`. A (non-batch) `scale` matrix is: ```none scale = scale_tril @@ -445,7 +444,7 @@ Log of determinant of covariance matrix. Log probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: @@ -591,7 +590,7 @@ Dictionary of parameters used to instantiate this `Distribution`. Probability density/mass function (depending on `is_continuous`). -Additional documentation from `_MultivariateNormalLinearOperator`: +Additional documentation from `MultivariateNormalLinearOperator`: `value` is a batch vector with compatible shape if `value` is a `Tensor` whose shape can be broadcast up to either: |