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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 @@ -<?xml version="1.0" encoding="UTF-8" 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