Update generated Python Op docs.
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A. Unique TensorFlower
TensorFlower Gardener
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@ -7,10 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
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[TOC]
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## Control Flow Operations
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TensorFlow provides several operations and classes that you can use to control
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the execution of operations and add conditional dependencies to your graph.
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Control Flow Operations. See the @{python/control_flow_ops} guide.
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- - -
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@ -390,12 +387,6 @@ Example using shape_invariants:
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```
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## Logical Operators
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TensorFlow provides several operations that you can use to add logical operators
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to your graph.
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- - -
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### `tf.logical_and(x, y, name=None)` {#logical_and}
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@ -462,12 +453,6 @@ Returns the truth value of x OR y element-wise.
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x ^ y = (x | y) & ~(x & y).
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## Comparison Operators
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TensorFlow provides several operations that you can use to add comparison
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operators to your graph.
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- - -
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### `tf.equal(x, y, name=None)` {#equal}
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@ -644,12 +629,6 @@ has the same shape as `x` and `y`, then it chooses which element to copy from
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* <b>`ValueError`</b>: When exactly one of `x` or `y` is non-None.
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## Debugging Operations
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TensorFlow provides several operations that you can use to validate values and
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debug your graph.
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- - -
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### `tf.is_finite(x, name=None)` {#is_finite}
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@ -7,10 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
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[TOC]
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## Script Language Operators.
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TensorFlow provides allows you to wrap python/numpy functions as
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TensorFlow operators.
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Script Language Operators. See the @{python/script_ops} guide.
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- - -
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@ -7,10 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
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[TOC]
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## Tensor Handle Operations.
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TensorFlow provides several operators that allows the user to keep tensors
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"in-place" across run calls.
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Tensor Handle Operations. See the @{python/session_ops} guide.
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- - -
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@ -7,12 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
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[TOC]
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## Sparse Tensor Representation
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TensorFlow supports a `SparseTensor` representation for data that is sparse
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in multiple dimensions. Contrast this representation with `IndexedSlices`,
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which is efficient for representing tensors that are sparse in their first
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dimension, and dense along all other dimensions.
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Sparse Tensor Representation. See the @{python/sparse_ops} guide.
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- - -
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@ -322,9 +317,6 @@ Alias for field number 1
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## Conversion
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- - -
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### `tf.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value=0, validate_indices=True, name=None)` {#sparse_to_dense}
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@ -575,9 +567,6 @@ In this case the resulting `SparseTensor` has the following properties:
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* <b>`ValueError`</b>: If `sp_ids` and `vocab_size` are lists of different lengths.
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## Manipulation
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- - -
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### `tf.sparse_concat(axis, sp_inputs, name=None, expand_nonconcat_dim=False, concat_dim=None)` {#sparse_concat}
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@ -1026,8 +1015,6 @@ then the output will be a `SparseTensor` of shape `[5, 4]` and
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* <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
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## Reduction
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- - -
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### `tf.sparse_reduce_sum(sp_input, axis=None, keep_dims=False, reduction_axes=None)` {#sparse_reduce_sum}
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@ -1107,8 +1094,6 @@ which are interpreted according to the indexing rules in Python.
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The reduced SparseTensor.
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## Math Operations
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- - -
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### `tf.sparse_add(a, b, thresh=0)` {#sparse_add}
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@ -7,7 +7,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
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[TOC]
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## Variables
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Variables. See the @{python/state_ops} guide.
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- - -
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@ -1170,12 +1170,6 @@ is on a different device it will get a copy of the variable.
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## Variable helper functions
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TensorFlow provides a set of functions to help manage the set of variables
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collected in the graph.
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- - -
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### `tf.global_variables()` {#global_variables}
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@ -1261,7 +1255,6 @@ This convenience function returns the contents of that collection.
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A list of Variable objects.
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- - -
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### `tf.global_variables_initializer()` {#global_variables_initializer}
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@ -1381,7 +1374,6 @@ logged by the C++ runtime. This is expected.
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An Op, or None if there are no variables.
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- - -
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### `tf.assign(ref, value, validate_shape=None, use_locking=None, name=None)` {#assign}
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@ -1471,9 +1463,6 @@ This makes it easier to chain operations that need to use the reset value.
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to use the new value after the variable has been updated.
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## Saving and Restoring Variables
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- - -
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### `class tf.train.Saver` {#Saver}
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@ -1851,7 +1840,6 @@ Converts this `Saver` to a `SaverDef` protocol buffer.
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- - -
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### `tf.train.latest_checkpoint(checkpoint_dir, latest_filename=None)` {#latest_checkpoint}
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@ -1871,7 +1859,6 @@ Finds the filename of latest saved checkpoint file.
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The full path to the latest checkpoint or `None` if no checkpoint was found.
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- - -
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### `tf.train.get_checkpoint_state(checkpoint_dir, latest_filename=None)` {#get_checkpoint_state}
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@ -1926,12 +1913,6 @@ proto.
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* <b>`RuntimeError`</b>: If the save paths conflict.
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## Sharing Variables
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TensorFlow provides several classes and operations that you can use to
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create variables contingent on certain conditions.
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- - -
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### `tf.get_variable(name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, use_resource=None, custom_getter=None)` {#get_variable}
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@ -2506,7 +2487,6 @@ reduce the likelihood of collisions with kwargs.
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* <b>`ValueError`</b>: if the name is None.
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- - -
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### `tf.no_regularizer(_)` {#no_regularizer}
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@ -2514,7 +2494,6 @@ reduce the likelihood of collisions with kwargs.
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Use this function to prevent regularization of variables.
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- - -
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### `class tf.constant_initializer` {#constant_initializer}
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@ -2820,9 +2799,6 @@ Args:
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## Variable Partitioners for Sharding
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- - -
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### `tf.fixed_size_partitioner(num_shards, axis=0)` {#fixed_size_partitioner}
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@ -2908,21 +2884,6 @@ variable. The maximum number of such partitions (upper bound) is given by
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`variable_scope`, `get_variable`, and `get_partitioned_variable_list`.
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## Sparse Variable Updates
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The sparse update ops modify a subset of the entries in a dense `Variable`,
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either overwriting the entries or adding / subtracting a delta. These are
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useful for training embedding models and similar lookup-based networks, since
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only a small subset of embedding vectors change in any given step.
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Since a sparse update of a large tensor may be generated automatically during
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gradient computation (as in the gradient of
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[`tf.gather`](../../api_docs/python/array_ops.md#gather)),
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an [`IndexedSlices`](#IndexedSlices) class is provided that encapsulates a set
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of sparse indices and values. `IndexedSlices` objects are detected and handled
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automatically by the optimizers in most cases.
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- - -
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### `tf.scatter_update(ref, indices, updates, use_locking=None, name=None)` {#scatter_update}
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@ -3499,9 +3460,6 @@ A `Tensor` containing the values of the slices.
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### Read-only Lookup Tables
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- - -
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### `tf.initialize_all_tables(*args, **kwargs)` {#initialize_all_tables}
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@ -3540,10 +3498,6 @@ Returns an Op that initializes all tables of the default graph.
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not tables the returned Op is a NoOp.
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## Exporting and Importing Meta Graphs
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- - -
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### `tf.train.export_meta_graph(filename=None, meta_info_def=None, graph_def=None, saver_def=None, collection_list=None, as_text=False, graph=None, export_scope=None, clear_devices=False, **kwargs)` {#export_meta_graph}
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@ -3657,9 +3611,6 @@ device assignments have not changed.
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(i.e., there are no variables to restore).
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# Deprecated functions (removed after 2017-03-02). Please don't use them.
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- - -
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### `tf.all_variables(*args, **kwargs)` {#all_variables}
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