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-rw-r--r--tensorflow/g3doc/api_docs/python/contrib.distributions.md317
-rw-r--r--tensorflow/g3doc/api_docs/python/contrib.framework.md87
-rw-r--r--tensorflow/g3doc/api_docs/python/contrib.learn.md3
-rw-r--r--tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.ContinuousTransformedDistribution.md309
-rw-r--r--tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.safe_embedding_lookup_sparse.md87
-rw-r--r--tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.read_batch_features.md3
-rw-r--r--tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.Coordinator.md13
-rw-r--r--tensorflow/g3doc/api_docs/python/index.md1
-rw-r--r--tensorflow/g3doc/api_docs/python/train.md13
-rw-r--r--tensorflow/g3doc/get_started/os_setup.md26
-rw-r--r--tensorflow/g3doc/images/wide_n_deep.svg1540
11 files changed, 756 insertions, 1643 deletions
diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
index c2e67db7cb..7bea8d72dd 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
@@ -4022,6 +4022,323 @@ Variance of the distribution.
+### Transformed distributions
+
+- - -
+
+### `class tf.contrib.distributions.ContinuousTransformedDistribution` {#ContinuousTransformedDistribution}
+
+A Transformed Distribution.
+
+A Transformed Distribution models `p(y)` given a base distribution `p(x)`,
+an invertible transform, `y = f(x)`, and the determinant of the Jacobian of
+`f(x)`.
+
+Shapes, type, and reparameterization are taken from the base distribution.
+
+#### Mathematical details
+
+* `p(x)` - probability distribution for random variable X
+* `p(y)` - probability distribution for random variable Y
+* `f` - transform
+* `g` - inverse transform, `f(g(x)) = x`
+* `J(x)` - Jacobian of f(x)
+
+A Transformed Distribution exposes `sample` and `pdf`:
+
+ * `sample`: `y = f(x)`, after drawing a sample of X.
+ * `pdf`: `p(y) = p(x) / det|J(x)| = p(g(y)) / det|J(g(y))|`
+
+A simple example constructing a Log-Normal distribution from a Normal
+distribution:
+
+```
+logit_normal = ContinuousTransformedDistribution(
+ base_dist=Normal(mu, sigma),
+ transform=lambda x: tf.sigmoid(x),
+ inverse=lambda y: tf.log(y) - tf.log(1. - y),
+ log_det_jacobian=(lambda x:
+ tf.reduce_sum(tf.log(tf.sigmoid(x)) + tf.log(1. - tf.sigmoid(x)),
+ reduction_indices=[-1])))
+ name="LogitNormalTransformedDistribution"
+)
+```
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.__init__(base_dist_cls, transform, inverse, log_det_jacobian, name='ContinuousTransformedDistribution', **base_dist_args)` {#ContinuousTransformedDistribution.__init__}
+
+Construct a Transformed Distribution.
+
+##### Args:
+
+
+* <b>`base_dist_cls`</b>: the base distribution class to transform. Must be a
+ subclass of `ContinuousDistribution`.
+* <b>`transform`</b>: a callable that takes a `Tensor` sample from `base_dist` and
+ returns a `Tensor` of the same shape and type. `x => y`.
+* <b>`inverse`</b>: a callable that computes the inverse of transform. `y => x`. If
+ None, users can only call `log_pdf` on values returned by `sample`.
+* <b>`log_det_jacobian`</b>: a callable that takes a `Tensor` sample from `base_dist`
+ and returns the log of the determinant of the Jacobian of `transform`.
+* <b>`name`</b>: The name for the distribution.
+* <b>`**base_dist_args`</b>: kwargs to pass on to dist_cls on construction.
+
+##### Raises:
+
+
+* <b>`TypeError`</b>: if `base_dist_cls` is not a subclass of
+ `ContinuousDistribution`.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.base_distribution` {#ContinuousTransformedDistribution.base_distribution}
+
+Base distribution, p(x).
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.batch_shape(name='batch_shape')` {#ContinuousTransformedDistribution.batch_shape}
+
+Batch dimensions of this instance as a 1-D int32 `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+* <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+ `Tensor` `batch_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.cdf(value, name='cdf')` {#ContinuousTransformedDistribution.cdf}
+
+Cumulative distribution function.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.dtype` {#ContinuousTransformedDistribution.dtype}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.entropy(name='entropy')` {#ContinuousTransformedDistribution.entropy}
+
+Entropy of the distribution in nats.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.event_shape(name='event_shape')` {#ContinuousTransformedDistribution.event_shape}
+
+Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+* <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+ `Tensor` `event_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.get_batch_shape()` {#ContinuousTransformedDistribution.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+ batch shape
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.get_event_shape()` {#ContinuousTransformedDistribution.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+ event shape
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.inverse` {#ContinuousTransformedDistribution.inverse}
+
+Inverse function of transform, y => x.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.is_reparameterized` {#ContinuousTransformedDistribution.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_cdf(value, name='log_cdf')` {#ContinuousTransformedDistribution.log_cdf}
+
+Log CDF.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_det_jacobian` {#ContinuousTransformedDistribution.log_det_jacobian}
+
+Function computing the log determinant of the Jacobian of transform.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_likelihood(value, name='log_likelihood')` {#ContinuousTransformedDistribution.log_likelihood}
+
+Log likelihood of this distribution (same as log_pdf).
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_pdf(y, name='log_pdf')` {#ContinuousTransformedDistribution.log_pdf}
+
+Log pdf of observations in `y`.
+
+`log ( p(g(y)) / det|J(g(y))| )`, where `g` is the inverse of `transform`.
+
+##### Args:
+
+
+* <b>`y`</b>: tensor of dtype `dtype`.
+* <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+* <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `y`.
+
+##### Raises:
+
+
+* <b>`ValueError`</b>: if `inverse` was not provided to the distribution and `y` was
+ not returned from `sample`.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.mean(name='mean')` {#ContinuousTransformedDistribution.mean}
+
+Mean of the distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.mode(name='mode')` {#ContinuousTransformedDistribution.mode}
+
+Mode of the distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.name` {#ContinuousTransformedDistribution.name}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.pdf(y, name='pdf')` {#ContinuousTransformedDistribution.pdf}
+
+The PDF of observations in `y`.
+
+`p(g(y)) / det|J(g(y))|`, where `g` is the inverse of `transform`.
+
+##### Args:
+
+
+* <b>`y`</b>: `Tensor` of dtype `dtype`.
+* <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+* <b>`pdf`</b>: `Tensor` of dtype `dtype`, the pdf values of `y`.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.sample(n, seed=None, name='sample')` {#ContinuousTransformedDistribution.sample}
+
+Sample `n` observations.
+
+Samples from the base distribution and then passes through the transform.
+
+##### Args:
+
+
+* <b>`n`</b>: scalar, type int32, the number of observations to sample.
+* <b>`seed`</b>: Python integer, the random seed.
+* <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+* <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.std(name='std')` {#ContinuousTransformedDistribution.std}
+
+Standard deviation of the distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.strict` {#ContinuousTransformedDistribution.strict}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.strict_statistics` {#ContinuousTransformedDistribution.strict_statistics}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.transform` {#ContinuousTransformedDistribution.transform}
+
+Function transforming x => y.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.variance(name='variance')` {#ContinuousTransformedDistribution.variance}
+
+Variance of the distribution.
+
+
+
+
## Operators allowing for matrix-free methods
### Positive definite operators
diff --git a/tensorflow/g3doc/api_docs/python/contrib.framework.md b/tensorflow/g3doc/api_docs/python/contrib.framework.md
index 4c23455597..df4df30d19 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.framework.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.framework.md
@@ -227,50 +227,49 @@ adds them via `tf.add_n`.
- - -
-### `tf.contrib.framework.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights=None, combiner='mean', default_id=None, name=None, partition_strategy='div')` {#safe_embedding_lookup_sparse}
-
-Lookup embedding results, accounting for invalid IDs and empty features.
-
-The partitioned embedding in `embedding_weights` must all be the same shape
-except for the first dimension. The first dimension is allowed to vary as the
-vocabulary size is not necessarily a multiple of `P`.
-
-Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
-with non-positive weight. For an entry with no features, the embedding vector
-for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
-
-The ids and weights may be multi-dimensional. Embeddings are always aggregated
-along the last dimension.
-
-##### Args:
-
-
-* <b>`embedding_weights`</b>: A list of `P` float tensors or values representing
- partitioned embedding tensors. The total unpartitioned shape should be
- `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
- `e_1, ..., e_m` are the embedding dimensions.
-* <b>`sparse_ids`</b>: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
- ids. `d_0` is typically batch size.
-* <b>`sparse_weights`</b>: `SparseTensor` of same shape as `sparse_ids`, containing
- float weights corresponding to `sparse_ids`, or `None` if all weights
- are be assumed to be 1.0.
-* <b>`combiner`</b>: A string specifying how to combine embedding results for each
- entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
- the default.
-* <b>`default_id`</b>: The id to use for an entry with no features.
-* <b>`name`</b>: A name for this operation (optional).
-* <b>`partition_strategy`</b>: A string specifying the partitioning strategy.
- Currently `"div"` and `"mod"` are supported. Default is `"div"`.
-
-
-##### Returns:
-
- Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
-
-##### Raises:
-
-
-* <b>`ValueError`</b>: if `embedding_weights` is empty.
+### `tf.contrib.framework.safe_embedding_lookup_sparse(*args, **kwargs)` {#safe_embedding_lookup_sparse}
+
+Lookup embedding results, accounting for invalid IDs and empty features. (deprecated)
+
+THIS FUNCTION IS DEPRECATED. It will be removed after 2016-09-01.
+Instructions for updating:
+Please use tf.contrib.layers.safe_embedding_lookup_sparse.
+
+ The partitioned embedding in `embedding_weights` must all be the same shape
+ except for the first dimension. The first dimension is allowed to vary as the
+ vocabulary size is not necessarily a multiple of `P`.
+
+ Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
+ with non-positive weight. For an entry with no features, the embedding vector
+ for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
+
+ The ids and weights may be multi-dimensional. Embeddings are always aggregated
+ along the last dimension.
+
+ Args:
+ embedding_weights: A list of `P` float tensors or values representing
+ partitioned embedding tensors. The total unpartitioned shape should be
+ `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
+ `e_1, ..., e_m` are the embedding dimensions.
+ sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
+ ids. `d_0` is typically batch size.
+ sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing
+ float weights corresponding to `sparse_ids`, or `None` if all weights
+ are be assumed to be 1.0.
+ combiner: A string specifying how to combine embedding results for each
+ entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
+ the default.
+ default_id: The id to use for an entry with no features.
+ name: A name for this operation (optional).
+ partition_strategy: A string specifying the partitioning strategy.
+ Currently `"div"` and `"mod"` are supported. Default is `"div"`.
+
+
+ Returns:
+ Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
+
+ Raises:
+ ValueError: if `embedding_weights` is empty.
- - -
diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md
index b573b02bd7..25bfe07e7a 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.learn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md
@@ -5083,7 +5083,7 @@ Use `parse_fn` if you need to do parsing / processing on single examples.
- - -
-### `tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, read_batch_size=1, name=None)` {#read_batch_features}
+### `tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name=None)` {#read_batch_features}
Adds operations to read, queue, batch and parse `Example` protos.
@@ -5115,7 +5115,6 @@ All ops are added to the default graph.
* <b>`queue_capacity`</b>: Capacity for input queue.
* <b>`reader_num_threads`</b>: The number of threads to read examples.
* <b>`parser_num_threads`</b>: The number of threads to parse examples.
-* <b>`read_batch_size`</b>: An int or scalar `Tensor` specifying the number of
records to read at once
* <b>`name`</b>: Name of resulting op.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.ContinuousTransformedDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.ContinuousTransformedDistribution.md
new file mode 100644
index 0000000000..1cda4145d6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.ContinuousTransformedDistribution.md
@@ -0,0 +1,309 @@
+A Transformed Distribution.
+
+A Transformed Distribution models `p(y)` given a base distribution `p(x)`,
+an invertible transform, `y = f(x)`, and the determinant of the Jacobian of
+`f(x)`.
+
+Shapes, type, and reparameterization are taken from the base distribution.
+
+#### Mathematical details
+
+* `p(x)` - probability distribution for random variable X
+* `p(y)` - probability distribution for random variable Y
+* `f` - transform
+* `g` - inverse transform, `f(g(x)) = x`
+* `J(x)` - Jacobian of f(x)
+
+A Transformed Distribution exposes `sample` and `pdf`:
+
+ * `sample`: `y = f(x)`, after drawing a sample of X.
+ * `pdf`: `p(y) = p(x) / det|J(x)| = p(g(y)) / det|J(g(y))|`
+
+A simple example constructing a Log-Normal distribution from a Normal
+distribution:
+
+```
+logit_normal = ContinuousTransformedDistribution(
+ base_dist=Normal(mu, sigma),
+ transform=lambda x: tf.sigmoid(x),
+ inverse=lambda y: tf.log(y) - tf.log(1. - y),
+ log_det_jacobian=(lambda x:
+ tf.reduce_sum(tf.log(tf.sigmoid(x)) + tf.log(1. - tf.sigmoid(x)),
+ reduction_indices=[-1])))
+ name="LogitNormalTransformedDistribution"
+)
+```
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.__init__(base_dist_cls, transform, inverse, log_det_jacobian, name='ContinuousTransformedDistribution', **base_dist_args)` {#ContinuousTransformedDistribution.__init__}
+
+Construct a Transformed Distribution.
+
+##### Args:
+
+
+* <b>`base_dist_cls`</b>: the base distribution class to transform. Must be a
+ subclass of `ContinuousDistribution`.
+* <b>`transform`</b>: a callable that takes a `Tensor` sample from `base_dist` and
+ returns a `Tensor` of the same shape and type. `x => y`.
+* <b>`inverse`</b>: a callable that computes the inverse of transform. `y => x`. If
+ None, users can only call `log_pdf` on values returned by `sample`.
+* <b>`log_det_jacobian`</b>: a callable that takes a `Tensor` sample from `base_dist`
+ and returns the log of the determinant of the Jacobian of `transform`.
+* <b>`name`</b>: The name for the distribution.
+* <b>`**base_dist_args`</b>: kwargs to pass on to dist_cls on construction.
+
+##### Raises:
+
+
+* <b>`TypeError`</b>: if `base_dist_cls` is not a subclass of
+ `ContinuousDistribution`.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.base_distribution` {#ContinuousTransformedDistribution.base_distribution}
+
+Base distribution, p(x).
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.batch_shape(name='batch_shape')` {#ContinuousTransformedDistribution.batch_shape}
+
+Batch dimensions of this instance as a 1-D int32 `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+* <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+ `Tensor` `batch_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.cdf(value, name='cdf')` {#ContinuousTransformedDistribution.cdf}
+
+Cumulative distribution function.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.dtype` {#ContinuousTransformedDistribution.dtype}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.entropy(name='entropy')` {#ContinuousTransformedDistribution.entropy}
+
+Entropy of the distribution in nats.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.event_shape(name='event_shape')` {#ContinuousTransformedDistribution.event_shape}
+
+Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+* <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+ `Tensor` `event_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.get_batch_shape()` {#ContinuousTransformedDistribution.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+ batch shape
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.get_event_shape()` {#ContinuousTransformedDistribution.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+ event shape
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.inverse` {#ContinuousTransformedDistribution.inverse}
+
+Inverse function of transform, y => x.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.is_reparameterized` {#ContinuousTransformedDistribution.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_cdf(value, name='log_cdf')` {#ContinuousTransformedDistribution.log_cdf}
+
+Log CDF.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_det_jacobian` {#ContinuousTransformedDistribution.log_det_jacobian}
+
+Function computing the log determinant of the Jacobian of transform.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_likelihood(value, name='log_likelihood')` {#ContinuousTransformedDistribution.log_likelihood}
+
+Log likelihood of this distribution (same as log_pdf).
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.log_pdf(y, name='log_pdf')` {#ContinuousTransformedDistribution.log_pdf}
+
+Log pdf of observations in `y`.
+
+`log ( p(g(y)) / det|J(g(y))| )`, where `g` is the inverse of `transform`.
+
+##### Args:
+
+
+* <b>`y`</b>: tensor of dtype `dtype`.
+* <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+* <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `y`.
+
+##### Raises:
+
+
+* <b>`ValueError`</b>: if `inverse` was not provided to the distribution and `y` was
+ not returned from `sample`.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.mean(name='mean')` {#ContinuousTransformedDistribution.mean}
+
+Mean of the distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.mode(name='mode')` {#ContinuousTransformedDistribution.mode}
+
+Mode of the distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.name` {#ContinuousTransformedDistribution.name}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.pdf(y, name='pdf')` {#ContinuousTransformedDistribution.pdf}
+
+The PDF of observations in `y`.
+
+`p(g(y)) / det|J(g(y))|`, where `g` is the inverse of `transform`.
+
+##### Args:
+
+
+* <b>`y`</b>: `Tensor` of dtype `dtype`.
+* <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+* <b>`pdf`</b>: `Tensor` of dtype `dtype`, the pdf values of `y`.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.sample(n, seed=None, name='sample')` {#ContinuousTransformedDistribution.sample}
+
+Sample `n` observations.
+
+Samples from the base distribution and then passes through the transform.
+
+##### Args:
+
+
+* <b>`n`</b>: scalar, type int32, the number of observations to sample.
+* <b>`seed`</b>: Python integer, the random seed.
+* <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+* <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.std(name='std')` {#ContinuousTransformedDistribution.std}
+
+Standard deviation of the distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.strict` {#ContinuousTransformedDistribution.strict}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.strict_statistics` {#ContinuousTransformedDistribution.strict_statistics}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.transform` {#ContinuousTransformedDistribution.transform}
+
+Function transforming x => y.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousTransformedDistribution.variance(name='variance')` {#ContinuousTransformedDistribution.variance}
+
+Variance of the distribution.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.safe_embedding_lookup_sparse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.safe_embedding_lookup_sparse.md
index f56043cc0d..3d5491c98a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.safe_embedding_lookup_sparse.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.safe_embedding_lookup_sparse.md
@@ -1,45 +1,44 @@
-### `tf.contrib.framework.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights=None, combiner='mean', default_id=None, name=None, partition_strategy='div')` {#safe_embedding_lookup_sparse}
-
-Lookup embedding results, accounting for invalid IDs and empty features.
-
-The partitioned embedding in `embedding_weights` must all be the same shape
-except for the first dimension. The first dimension is allowed to vary as the
-vocabulary size is not necessarily a multiple of `P`.
-
-Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
-with non-positive weight. For an entry with no features, the embedding vector
-for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
-
-The ids and weights may be multi-dimensional. Embeddings are always aggregated
-along the last dimension.
-
-##### Args:
-
-
-* <b>`embedding_weights`</b>: A list of `P` float tensors or values representing
- partitioned embedding tensors. The total unpartitioned shape should be
- `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
- `e_1, ..., e_m` are the embedding dimensions.
-* <b>`sparse_ids`</b>: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
- ids. `d_0` is typically batch size.
-* <b>`sparse_weights`</b>: `SparseTensor` of same shape as `sparse_ids`, containing
- float weights corresponding to `sparse_ids`, or `None` if all weights
- are be assumed to be 1.0.
-* <b>`combiner`</b>: A string specifying how to combine embedding results for each
- entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
- the default.
-* <b>`default_id`</b>: The id to use for an entry with no features.
-* <b>`name`</b>: A name for this operation (optional).
-* <b>`partition_strategy`</b>: A string specifying the partitioning strategy.
- Currently `"div"` and `"mod"` are supported. Default is `"div"`.
-
-
-##### Returns:
-
- Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
-
-##### Raises:
-
-
-* <b>`ValueError`</b>: if `embedding_weights` is empty.
+### `tf.contrib.framework.safe_embedding_lookup_sparse(*args, **kwargs)` {#safe_embedding_lookup_sparse}
+
+Lookup embedding results, accounting for invalid IDs and empty features. (deprecated)
+
+THIS FUNCTION IS DEPRECATED. It will be removed after 2016-09-01.
+Instructions for updating:
+Please use tf.contrib.layers.safe_embedding_lookup_sparse.
+
+ The partitioned embedding in `embedding_weights` must all be the same shape
+ except for the first dimension. The first dimension is allowed to vary as the
+ vocabulary size is not necessarily a multiple of `P`.
+
+ Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
+ with non-positive weight. For an entry with no features, the embedding vector
+ for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
+
+ The ids and weights may be multi-dimensional. Embeddings are always aggregated
+ along the last dimension.
+
+ Args:
+ embedding_weights: A list of `P` float tensors or values representing
+ partitioned embedding tensors. The total unpartitioned shape should be
+ `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
+ `e_1, ..., e_m` are the embedding dimensions.
+ sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
+ ids. `d_0` is typically batch size.
+ sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing
+ float weights corresponding to `sparse_ids`, or `None` if all weights
+ are be assumed to be 1.0.
+ combiner: A string specifying how to combine embedding results for each
+ entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
+ the default.
+ default_id: The id to use for an entry with no features.
+ name: A name for this operation (optional).
+ partition_strategy: A string specifying the partitioning strategy.
+ Currently `"div"` and `"mod"` are supported. Default is `"div"`.
+
+
+ Returns:
+ Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
+
+ Raises:
+ ValueError: if `embedding_weights` is empty.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.read_batch_features.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.read_batch_features.md
index 6327760ca0..d18c316080 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.read_batch_features.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.read_batch_features.md
@@ -1,4 +1,4 @@
-### `tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, read_batch_size=1, name=None)` {#read_batch_features}
+### `tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name=None)` {#read_batch_features}
Adds operations to read, queue, batch and parse `Example` protos.
@@ -30,7 +30,6 @@ All ops are added to the default graph.
* <b>`queue_capacity`</b>: Capacity for input queue.
* <b>`reader_num_threads`</b>: The number of threads to read examples.
* <b>`parser_num_threads`</b>: The number of threads to parse examples.
-* <b>`read_batch_size`</b>: An int or scalar `Tensor` specifying the number of
records to read at once
* <b>`name`</b>: Name of resulting op.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.Coordinator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.Coordinator.md
index f51c0721ff..bebc34754f 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.Coordinator.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.Coordinator.md
@@ -93,10 +93,21 @@ except Exception:
```
- - -
-#### `tf.train.Coordinator.__init__()` {#Coordinator.__init__}
+#### `tf.train.Coordinator.__init__(clean_stop_exception_types=None)` {#Coordinator.__init__}
Create a new Coordinator.
+##### Args:
+
+
+* <b>`clean_stop_exception_types`</b>: Optional tuple of Exception types that should
+ cause a clean stop of the coordinator. If an exception of one of these
+ types is reported to `request_stop(ex)` the coordinator will behave as
+ if `request_stop(None)` was called. Defaults to
+ `(tf.errors.OutOfRangeError,)` which is used by input queues to signal
+ the end of input. When feeding training data from a Python iterator it
+ is common to add `StopIteration` to this list.
+
- - -
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 5c4f7107d1..53a971e694 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -584,6 +584,7 @@
* [`Categorical`](../../api_docs/python/contrib.distributions.md#Categorical)
* [`Chi2`](../../api_docs/python/contrib.distributions.md#Chi2)
* [`ContinuousDistribution`](../../api_docs/python/contrib.distributions.md#ContinuousDistribution)
+ * [`ContinuousTransformedDistribution`](../../api_docs/python/contrib.distributions.md#ContinuousTransformedDistribution)
* [`DirichletMultinomial`](../../api_docs/python/contrib.distributions.md#DirichletMultinomial)
* [`DiscreteDistribution`](../../api_docs/python/contrib.distributions.md#DiscreteDistribution)
* [`Exponential`](../../api_docs/python/contrib.distributions.md#Exponential)
diff --git a/tensorflow/g3doc/api_docs/python/train.md b/tensorflow/g3doc/api_docs/python/train.md
index 64c8085c87..4f63fa0b7c 100644
--- a/tensorflow/g3doc/api_docs/python/train.md
+++ b/tensorflow/g3doc/api_docs/python/train.md
@@ -1214,10 +1214,21 @@ except Exception:
```
- - -
-#### `tf.train.Coordinator.__init__()` {#Coordinator.__init__}
+#### `tf.train.Coordinator.__init__(clean_stop_exception_types=None)` {#Coordinator.__init__}
Create a new Coordinator.
+##### Args:
+
+
+* <b>`clean_stop_exception_types`</b>: Optional tuple of Exception types that should
+ cause a clean stop of the coordinator. If an exception of one of these
+ types is reported to `request_stop(ex)` the coordinator will behave as
+ if `request_stop(None)` was called. Defaults to
+ `(tf.errors.OutOfRangeError,)` which is used by input queues to signal
+ the end of input. When feeding training data from a Python iterator it
+ is common to add `StopIteration` to this list.
+
- - -
diff --git a/tensorflow/g3doc/get_started/os_setup.md b/tensorflow/g3doc/get_started/os_setup.md
index 158c84b4ef..e1cece4faa 100644
--- a/tensorflow/g3doc/get_started/os_setup.md
+++ b/tensorflow/g3doc/get_started/os_setup.md
@@ -63,7 +63,7 @@ Then, select the correct binary to install:
# Ubuntu/Linux 64-bit, CPU only, Python 2.7
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl
-# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
+# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl
@@ -73,14 +73,14 @@ $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/tensorflow-
# Ubuntu/Linux 64-bit, CPU only, Python 3.4
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl
-# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
+# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.5
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp35-cp35m-linux_x86_64.whl
-# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
+# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp35-cp35m-linux_x86_64.whl
@@ -153,7 +153,7 @@ Now, install TensorFlow just as you would for a regular Pip installation. First
# Ubuntu/Linux 64-bit, CPU only, Python 2.7
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl
-# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
+# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl
@@ -163,14 +163,14 @@ Now, install TensorFlow just as you would for a regular Pip installation. First
# Ubuntu/Linux 64-bit, CPU only, Python 3.4
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl
-# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
+# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.5
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp35-cp35m-linux_x86_64.whl
-# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
+# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp35-cp35m-linux_x86_64.whl
@@ -277,7 +277,7 @@ Now, install TensorFlow just as you would for a regular Pip installation. First
# Ubuntu/Linux 64-bit, CPU only, Python 2.7
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl
-# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
+# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl
@@ -287,14 +287,14 @@ Now, install TensorFlow just as you would for a regular Pip installation. First
# Ubuntu/Linux 64-bit, CPU only, Python 3.4
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl
-# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
+# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.5
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp35-cp35m-linux_x86_64.whl
-# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
+# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp35-cp35m-linux_x86_64.whl
@@ -952,6 +952,14 @@ SyntaxError: invalid syntax
Solution: make sure you are using Python 2.7.
+#### Ubuntu build issue on Linux 16.04 when building with --config=cuda: build fail with cuda: identifier "__builtin_ia32_mwaitx" is undefined.
+GitHub issue: https://github.com/tensorflow/tensorflow/issues/1066
+
+Solution: Add the following compiler flags to third_party/gpus/crosstool/CROSSTOOL
+
+cxx_flag: "-D_MWAITXINTRIN_H_INCLUDED"
+cxx_flag: "-D_FORCE_INLINES"
+
### Mac OS X: ImportError: No module named copyreg
On Mac OS X, you may encounter the following when importing tensorflow.
diff --git a/tensorflow/g3doc/images/wide_n_deep.svg b/tensorflow/g3doc/images/wide_n_deep.svg
deleted file mode 100644
index 6dfe9e7f10..0000000000
--- a/tensorflow/g3doc/images/wide_n_deep.svg
+++ /dev/null
@@ -1,1540 +0,0 @@
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- style="fill:none;stroke:none;stroke-linecap:square;stroke-miterlimit:10">
- <metadata
- id="metadata5374">
- <rdf:RDF>
- <cc:Work
- rdf:about="">
- <dc:format>image/svg+xml</dc:format>
- <dc:type
- rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
- <dc:title></dc:title>
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