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diff --git a/tensorflow/g3doc/api_docs/python/framework.md b/tensorflow/g3doc/api_docs/python/framework.md index eab4ec0152..42b02852c7 100644 --- a/tensorflow/g3doc/api_docs/python/framework.md +++ b/tensorflow/g3doc/api_docs/python/framework.md @@ -1,46 +1,15 @@ <!-- This file is machine generated: DO NOT EDIT! --> -# Building Graphs <a class="md-anchor" id="AUTOGENERATED-building-graphs"></a> -<!-- TOC-BEGIN This section is generated by neural network: DO NOT EDIT! --> -## Contents -### [Building Graphs](#AUTOGENERATED-building-graphs) -* [Core graph data structures](#AUTOGENERATED-core-graph-data-structures) - * [`class tf.Graph`](#Graph) - * [`class tf.Operation`](#Operation) - * [`class tf.Tensor`](#Tensor) -* [Tensor types](#AUTOGENERATED-tensor-types) - * [`class tf.DType`](#DType) - * [`tf.as_dtype(type_value)`](#as_dtype) -* [Utility functions](#AUTOGENERATED-utility-functions) - * [`tf.device(dev)`](#device) - * [`tf.name_scope(name)`](#name_scope) - * [`tf.control_dependencies(control_inputs)`](#control_dependencies) - * [`tf.convert_to_tensor(value, dtype=None, name=None)`](#convert_to_tensor) - * [`tf.get_default_graph()`](#get_default_graph) - * [`tf.import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None)`](#import_graph_def) -* [Graph collections](#AUTOGENERATED-graph-collections) - * [`tf.add_to_collection(name, value)`](#add_to_collection) - * [`tf.get_collection(key, scope=None)`](#get_collection) - * [`class tf.GraphKeys`](#GraphKeys) -* [Defining new operations](#AUTOGENERATED-defining-new-operations) - * [`class tf.RegisterGradient`](#RegisterGradient) - * [`tf.NoGradient(op_type)`](#NoGradient) - * [`class tf.RegisterShape`](#RegisterShape) - * [`class tf.TensorShape`](#TensorShape) - * [`class tf.Dimension`](#Dimension) - * [`tf.op_scope(values, name, default_name)`](#op_scope) - * [`tf.get_seed(op_seed)`](#get_seed) - - -<!-- TOC-END This section was generated by neural network, THANKS FOR READING! --> +# Building Graphs +[TOC] Classes and functions for building TensorFlow graphs. -## Core graph data structures <a class="md-anchor" id="AUTOGENERATED-core-graph-data-structures"></a> +## Core graph data structures - - - -### `class tf.Graph` <a class="md-anchor" id="Graph"></a> +### `class tf.Graph` {#Graph} A TensorFlow computation, represented as a dataflow graph. @@ -80,14 +49,14 @@ are not thread-safe. - - - -#### `tf.Graph.__init__()` <a class="md-anchor" id="Graph.__init__"></a> +#### `tf.Graph.__init__()` {#Graph.__init__} Creates a new, empty Graph. - - - -#### `tf.Graph.as_default()` <a class="md-anchor" id="Graph.as_default"></a> +#### `tf.Graph.as_default()` {#Graph.as_default} Returns a context manager that makes this `Graph` the default graph. @@ -118,14 +87,14 @@ with tf.Graph().as_default() as g: assert c.graph is g ``` -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A context manager for using this graph as the default graph. - - - -#### `tf.Graph.as_graph_def(from_version=None)` <a class="md-anchor" id="Graph.as_graph_def"></a> +#### `tf.Graph.as_graph_def(from_version=None)` {#Graph.as_graph_def} Returns a serialized `GraphDef` representation of this graph. @@ -135,19 +104,19 @@ The serialized `GraphDef` can be imported into another `Graph` This method is thread-safe. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`from_version`</b>: Optional. If this is set, returns a `GraphDef` containing only the nodes that were added to this graph since its `version` property had the given value. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A [`GraphDef`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/graph.proto) protocol buffer. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If the graph_def would be too large. @@ -155,7 +124,7 @@ This method is thread-safe. - - - -#### `tf.Graph.finalize()` <a class="md-anchor" id="Graph.finalize"></a> +#### `tf.Graph.finalize()` {#Graph.finalize} Finalizes this graph, making it read-only. @@ -167,14 +136,14 @@ when using a [`QueueRunner`](../../api_docs/python/train.md#QueueRunner). - - - -#### `tf.Graph.finalized` <a class="md-anchor" id="Graph.finalized"></a> +#### `tf.Graph.finalized` {#Graph.finalized} True if this graph has been finalized. - - - -#### `tf.Graph.control_dependencies(control_inputs)` <a class="md-anchor" id="Graph.control_dependencies"></a> +#### `tf.Graph.control_dependencies(control_inputs)` {#Graph.control_dependencies} Returns a context manager that specifies control dependencies. @@ -222,19 +191,19 @@ def my_func(pred, tensor): return tf.matmul(tensor, tensor) ``` -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`control_inputs`</b>: A list of `Operation` or `Tensor` objects, which must be executed or computed before running the operations defined in the context. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A context manager that specifies control dependencies for all operations constructed within the context. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: If `control_inputs` is not a list of `Operation` or @@ -243,7 +212,7 @@ def my_func(pred, tensor): - - - -#### `tf.Graph.device(device_name_or_function)` <a class="md-anchor" id="Graph.device"></a> +#### `tf.Graph.device(device_name_or_function)` {#Graph.device} Returns a context manager that specifies the default device to use. @@ -281,13 +250,13 @@ with g.device(matmul_on_gpu): # on CPU 0. ``` -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`device_name_or_function`</b>: The device name or function to use in the context. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A context manager that specifies the default device to use for newly created ops. @@ -295,7 +264,7 @@ with g.device(matmul_on_gpu): - - - -#### `tf.Graph.name_scope(name)` <a class="md-anchor" id="Graph.name_scope"></a> +#### `tf.Graph.name_scope(name)` {#Graph.name_scope} Returns a context manager that creates hierarchical names for operations. @@ -365,12 +334,12 @@ with g.name_scope('my_layer') as scope: ``` -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`name`</b>: A name for the scope. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A context manager that installs `name` as a new name scope. @@ -386,11 +355,11 @@ may define additional collections by specifying a new name. - - - -#### `tf.Graph.add_to_collection(name, value)` <a class="md-anchor" id="Graph.add_to_collection"></a> +#### `tf.Graph.add_to_collection(name, value)` {#Graph.add_to_collection} Stores `value` in the collection with the given `name`. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`name`</b>: The key for the collection. For example, the `GraphKeys` class @@ -400,11 +369,11 @@ Stores `value` in the collection with the given `name`. - - - -#### `tf.Graph.get_collection(name, scope=None)` <a class="md-anchor" id="Graph.get_collection"></a> +#### `tf.Graph.get_collection(name, scope=None)` {#Graph.get_collection} Returns a list of values in the collection with the given `name`. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`key`</b>: The key for the collection. For example, the `GraphKeys` class @@ -412,7 +381,7 @@ Returns a list of values in the collection with the given `name`. * <b>`scope`</b>: (Optional.) If supplied, the resulting list is filtered to include only items whose name begins with this string. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: The list of values in the collection with the given `name`, or an empty list if no value has been added to that collection. The @@ -423,7 +392,7 @@ Returns a list of values in the collection with the given `name`. - - - -#### `tf.Graph.as_graph_element(obj, allow_tensor=True, allow_operation=True)` <a class="md-anchor" id="Graph.as_graph_element"></a> +#### `tf.Graph.as_graph_element(obj, allow_tensor=True, allow_operation=True)` {#Graph.as_graph_element} Returns the object referred to by `obj`, as an `Operation` or `Tensor`. @@ -436,7 +405,7 @@ Session API. This method may be called concurrently from multiple threads. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`obj`</b>: A `Tensor`, an `Operation`, or the name of a tensor or operation. @@ -445,11 +414,11 @@ This method may be called concurrently from multiple threads. * <b>`allow_tensor`</b>: If true, `obj` may refer to a `Tensor`. * <b>`allow_operation`</b>: If true, `obj` may refer to an `Operation`. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: The `Tensor` or `Operation` in the Graph corresponding to `obj`. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: If `obj` is not a type we support attempting to convert @@ -461,22 +430,22 @@ This method may be called concurrently from multiple threads. - - - -#### `tf.Graph.get_operation_by_name(name)` <a class="md-anchor" id="Graph.get_operation_by_name"></a> +#### `tf.Graph.get_operation_by_name(name)` {#Graph.get_operation_by_name} Returns the `Operation` with the given `name`. This method may be called concurrently from multiple threads. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`name`</b>: The name of the `Operation` to return. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: The `Operation` with the given `name`. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: If `name` is not a string. @@ -485,22 +454,22 @@ This method may be called concurrently from multiple threads. - - - -#### `tf.Graph.get_tensor_by_name(name)` <a class="md-anchor" id="Graph.get_tensor_by_name"></a> +#### `tf.Graph.get_tensor_by_name(name)` {#Graph.get_tensor_by_name} Returns the `Tensor` with the given `name`. This method may be called concurrently from multiple threads. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`name`</b>: The name of the `Tensor` to return. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: The `Tensor` with the given `name`. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: If `name` is not a string. @@ -509,7 +478,7 @@ This method may be called concurrently from multiple threads. - - - -#### `tf.Graph.get_operations()` <a class="md-anchor" id="Graph.get_operations"></a> +#### `tf.Graph.get_operations()` {#Graph.get_operations} Return the list of operations in the graph. @@ -519,7 +488,7 @@ list of operations known to the graph. This method may be called concurrently from multiple threads. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A list of Operations. @@ -527,24 +496,24 @@ This method may be called concurrently from multiple threads. - - - -#### `tf.Graph.get_default_device()` <a class="md-anchor" id="Graph.get_default_device"></a> +#### `tf.Graph.get_default_device()` {#Graph.get_default_device} Returns the default device. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A string. - - - -#### `tf.Graph.seed` <a class="md-anchor" id="Graph.seed"></a> +#### `tf.Graph.seed` {#Graph.seed} - - - -#### `tf.Graph.unique_name(name)` <a class="md-anchor" id="Graph.unique_name"></a> +#### `tf.Graph.unique_name(name)` {#Graph.unique_name} Return a unique Operation name for "name". @@ -557,12 +526,12 @@ to help identify Operations when debugging a Graph. Operation names are displayed in error messages reported by the TensorFlow runtime, and in various visualization tools such as TensorBoard. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`name`</b>: The name for an `Operation`. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A string to be passed to `create_op()` that will be used to name the operation being created. @@ -570,14 +539,14 @@ and in various visualization tools such as TensorBoard. - - - -#### `tf.Graph.version` <a class="md-anchor" id="Graph.version"></a> +#### `tf.Graph.version` {#Graph.version} Returns a version number that increases as ops are added to the graph. - - - -#### `tf.Graph.create_op(op_type, inputs, dtypes, input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True)` <a class="md-anchor" id="Graph.create_op"></a> +#### `tf.Graph.create_op(op_type, inputs, dtypes, input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True)` {#Graph.create_op} Creates an `Operation` in this graph. @@ -586,7 +555,7 @@ programs will not call this method directly, and instead use the Python op constructors, such as `tf.constant()`, which add ops to the default graph. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`op_type`</b>: The `Operation` type to create. This corresponds to the @@ -607,19 +576,19 @@ the default graph. * <b>`compute_shapes`</b>: (Optional.) If True, shape inference will be performed to compute the shapes of the outputs. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: if any of the inputs is not a `Tensor`. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: An `Operation` object. - - - -#### `tf.Graph.gradient_override_map(op_type_map)` <a class="md-anchor" id="Graph.gradient_override_map"></a> +#### `tf.Graph.gradient_override_map(op_type_map)` {#Graph.gradient_override_map} EXPERIMENTAL: A context manager for overriding gradient functions. @@ -641,18 +610,18 @@ with tf.Graph().as_default() as g: # gradient of s_2. ``` -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`op_type_map`</b>: A dictionary mapping op type strings to alternative op type strings. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A context manager that sets the alternative op type to be used for one or more ops created in that context. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: If `op_type_map` is not a dictionary mapping strings to @@ -662,7 +631,7 @@ with tf.Graph().as_default() as g: - - - -### `class tf.Operation` <a class="md-anchor" id="Operation"></a> +### `class tf.Operation` {#Operation} Represents a graph node that performs computation on tensors. @@ -684,25 +653,25 @@ be executed by passing it to - - - -#### `tf.Operation.name` <a class="md-anchor" id="Operation.name"></a> +#### `tf.Operation.name` {#Operation.name} The full name of this operation. - - - -#### `tf.Operation.type` <a class="md-anchor" id="Operation.type"></a> +#### `tf.Operation.type` {#Operation.type} The type of the op (e.g. `"MatMul"`). - - - -#### `tf.Operation.inputs` <a class="md-anchor" id="Operation.inputs"></a> +#### `tf.Operation.inputs` {#Operation.inputs} The list of `Tensor` objects representing the data inputs of this op. - - - -#### `tf.Operation.control_inputs` <a class="md-anchor" id="Operation.control_inputs"></a> +#### `tf.Operation.control_inputs` {#Operation.control_inputs} The `Operation` objects on which this op has a control dependency. @@ -712,37 +681,37 @@ mechanism can be used to run ops sequentially for performance reasons, or to ensure that the side effects of an op are observed in the correct order. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A list of `Operation` objects. - - - -#### `tf.Operation.outputs` <a class="md-anchor" id="Operation.outputs"></a> +#### `tf.Operation.outputs` {#Operation.outputs} The list of `Tensor` objects representing the outputs of this op. - - - -#### `tf.Operation.device` <a class="md-anchor" id="Operation.device"></a> +#### `tf.Operation.device` {#Operation.device} The name of the device to which this op has been assigned, if any. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: The string name of the device to which this op has been assigned, or None if it has not been assigned to a device. - - - -#### `tf.Operation.graph` <a class="md-anchor" id="Operation.graph"></a> +#### `tf.Operation.graph` {#Operation.graph} The `Graph` that contains this operation. - - - -#### `tf.Operation.run(feed_dict=None, session=None)` <a class="md-anchor" id="Operation.run"></a> +#### `tf.Operation.run(feed_dict=None, session=None)` {#Operation.run} Runs this operation in a `Session`. @@ -753,7 +722,7 @@ produce the inputs needed for this operation. launched in a session, and either a default session must be available, or `session` must be specified explicitly. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values. @@ -766,20 +735,20 @@ available, or `session` must be specified explicitly. - - - -#### `tf.Operation.get_attr(name)` <a class="md-anchor" id="Operation.get_attr"></a> +#### `tf.Operation.get_attr(name)` {#Operation.get_attr} Returns the value of the attr of this op with the given `name`. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`name`</b>: The name of the attr to fetch. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: The value of the attr, as a Python object. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If this op does not have an attr with the given `name`. @@ -787,15 +756,15 @@ Returns the value of the attr of this op with the given `name`. - - - -#### `tf.Operation.traceback` <a class="md-anchor" id="Operation.traceback"></a> +#### `tf.Operation.traceback` {#Operation.traceback} Returns the call stack from when this operation was constructed. -#### Other Methods <a class="md-anchor" id="AUTOGENERATED-other-methods"></a> +#### Other Methods - - - -#### `tf.Operation.__init__(node_def, g, inputs=None, output_types=None, control_inputs=None, input_types=None, original_op=None, op_def=None)` <a class="md-anchor" id="Operation.__init__"></a> +#### `tf.Operation.__init__(node_def, g, inputs=None, output_types=None, control_inputs=None, input_types=None, original_op=None, op_def=None)` {#Operation.__init__} Creates an `Operation`. @@ -805,7 +774,7 @@ regular expression: [A-Za-z0-9.][A-Za-z0-9_.\-/]* -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`node_def`</b>: graph_pb2.NodeDef. NodeDef for the Operation. @@ -829,7 +798,7 @@ regular expression: * <b>`op_def`</b>: Optional. The op_def_pb2.OpDef proto that describes the op type that this Operation represents. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: if control inputs are not Operations or Tensors, @@ -842,11 +811,11 @@ regular expression: - - - -#### `tf.Operation.node_def` <a class="md-anchor" id="Operation.node_def"></a> +#### `tf.Operation.node_def` {#Operation.node_def} Returns a serialized `NodeDef` representation of this operation. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A [`NodeDef`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/graph.proto) @@ -854,11 +823,11 @@ Returns a serialized `NodeDef` representation of this operation. - - - -#### `tf.Operation.op_def` <a class="md-anchor" id="Operation.op_def"></a> +#### `tf.Operation.op_def` {#Operation.op_def} Returns the `OpDef` proto that represents the type of this op. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: An [`OpDef`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/op_def.proto) @@ -866,7 +835,7 @@ Returns the `OpDef` proto that represents the type of this op. - - - -#### `tf.Operation.values()` <a class="md-anchor" id="Operation.values"></a> +#### `tf.Operation.values()` {#Operation.values} DEPRECATED: Use outputs. @@ -874,7 +843,7 @@ DEPRECATED: Use outputs. - - - -### `class tf.Tensor` <a class="md-anchor" id="Tensor"></a> +### `class tf.Tensor` {#Tensor} Represents a value produced by an `Operation`. @@ -915,41 +884,41 @@ result = sess.run(e) - - - -#### `tf.Tensor.dtype` <a class="md-anchor" id="Tensor.dtype"></a> +#### `tf.Tensor.dtype` {#Tensor.dtype} The `DType` of elements in this tensor. - - - -#### `tf.Tensor.name` <a class="md-anchor" id="Tensor.name"></a> +#### `tf.Tensor.name` {#Tensor.name} The string name of this tensor. - - - -#### `tf.Tensor.value_index` <a class="md-anchor" id="Tensor.value_index"></a> +#### `tf.Tensor.value_index` {#Tensor.value_index} The index of this tensor in the outputs of its `Operation`. - - - -#### `tf.Tensor.graph` <a class="md-anchor" id="Tensor.graph"></a> +#### `tf.Tensor.graph` {#Tensor.graph} The `Graph` that contains this tensor. - - - -#### `tf.Tensor.op` <a class="md-anchor" id="Tensor.op"></a> +#### `tf.Tensor.op` {#Tensor.op} The `Operation` that produces this tensor as an output. - - - -#### `tf.Tensor.consumers()` <a class="md-anchor" id="Tensor.consumers"></a> +#### `tf.Tensor.consumers()` {#Tensor.consumers} Returns a list of `Operation`s that consume this tensor. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A list of `Operation`s. @@ -957,7 +926,7 @@ Returns a list of `Operation`s that consume this tensor. - - - -#### `tf.Tensor.eval(feed_dict=None, session=None)` <a class="md-anchor" id="Tensor.eval"></a> +#### `tf.Tensor.eval(feed_dict=None, session=None)` {#Tensor.eval} Evaluates this tensor in a `Session`. @@ -969,7 +938,7 @@ tensor. launched in a session, and either a default session must be available, or `session` must be specified explicitly. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values. @@ -978,7 +947,7 @@ available, or `session` must be specified explicitly. * <b>`session`</b>: (Optional.) The `Session` to be used to evaluate this tensor. If none, the default session will be used. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A numpy array corresponding to the value of this tensor. @@ -986,7 +955,7 @@ available, or `session` must be specified explicitly. - - - -#### `tf.Tensor.get_shape()` <a class="md-anchor" id="Tensor.get_shape"></a> +#### `tf.Tensor.get_shape()` {#Tensor.get_shape} Returns the `TensorShape` that represents the shape of this tensor. @@ -1026,14 +995,14 @@ the caller has additional information about the values of these dimensions, `Tensor.set_shape()` can be used to augment the inferred shape. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `TensorShape` representing the shape of this tensor. - - - -#### `tf.Tensor.set_shape(shape)` <a class="md-anchor" id="Tensor.set_shape"></a> +#### `tf.Tensor.set_shape(shape)` {#Tensor.set_shape} Updates the shape of this tensor. @@ -1058,12 +1027,12 @@ print image.get_shape() ==> TensorShape([Dimension(28), Dimension(28), Dimension(3)]) ``` -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`shape`</b>: A `TensorShape` representing the shape of this tensor. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `shape` is not compatible with the current shape of @@ -1071,14 +1040,14 @@ print image.get_shape() -#### Other Methods <a class="md-anchor" id="AUTOGENERATED-other-methods"></a> +#### Other Methods - - - -#### `tf.Tensor.__init__(op, value_index, dtype)` <a class="md-anchor" id="Tensor.__init__"></a> +#### `tf.Tensor.__init__(op, value_index, dtype)` {#Tensor.__init__} Creates a new `Tensor`. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`op`</b>: An `Operation`. `Operation` that computes this tensor. @@ -1086,7 +1055,7 @@ Creates a new `Tensor`. this tensor. * <b>`dtype`</b>: A `types.DType`. Type of data stored in this tensor. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: If the op is not an `Operation`. @@ -1094,17 +1063,17 @@ Creates a new `Tensor`. - - - -#### `tf.Tensor.device` <a class="md-anchor" id="Tensor.device"></a> +#### `tf.Tensor.device` {#Tensor.device} The name of the device on which this tensor will be produced, or None. -## Tensor types <a class="md-anchor" id="AUTOGENERATED-tensor-types"></a> +## Tensor types - - - -### `class tf.DType` <a class="md-anchor" id="DType"></a> +### `class tf.DType` {#DType} Represents the type of the elements in a `Tensor`. @@ -1136,7 +1105,7 @@ names to a `DType` object. - - - -#### `tf.DType.is_compatible_with(other)` <a class="md-anchor" id="DType.is_compatible_with"></a> +#### `tf.DType.is_compatible_with(other)` {#DType.is_compatible_with} Returns True if the `other` DType will be converted to this DType. @@ -1149,12 +1118,12 @@ DType(T).as_ref.is_compatible_with(DType(T)) == False DType(T).as_ref.is_compatible_with(DType(T).as_ref) == True ``` -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`other`</b>: A `DType` (or object that may be converted to a `DType`). -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: True if a Tensor of the `other` `DType` will be implicitly converted to this `DType`. @@ -1162,58 +1131,58 @@ DType(T).as_ref.is_compatible_with(DType(T).as_ref) == True - - - -#### `tf.DType.name` <a class="md-anchor" id="DType.name"></a> +#### `tf.DType.name` {#DType.name} Returns the string name for this `DType`. - - - -#### `tf.DType.base_dtype` <a class="md-anchor" id="DType.base_dtype"></a> +#### `tf.DType.base_dtype` {#DType.base_dtype} Returns a non-reference `DType` based on this `DType`. - - - -#### `tf.DType.is_ref_dtype` <a class="md-anchor" id="DType.is_ref_dtype"></a> +#### `tf.DType.is_ref_dtype` {#DType.is_ref_dtype} Returns `True` if this `DType` represents a reference type. - - - -#### `tf.DType.as_ref` <a class="md-anchor" id="DType.as_ref"></a> +#### `tf.DType.as_ref` {#DType.as_ref} Returns a reference `DType` based on this `DType`. - - - -#### `tf.DType.is_integer` <a class="md-anchor" id="DType.is_integer"></a> +#### `tf.DType.is_integer` {#DType.is_integer} Returns whether this is a (non-quantized) integer type. - - - -#### `tf.DType.is_quantized` <a class="md-anchor" id="DType.is_quantized"></a> +#### `tf.DType.is_quantized` {#DType.is_quantized} Returns whether this is a quantized data type. - - - -#### `tf.DType.as_numpy_dtype` <a class="md-anchor" id="DType.as_numpy_dtype"></a> +#### `tf.DType.as_numpy_dtype` {#DType.as_numpy_dtype} Returns a `numpy.dtype` based on this `DType`. - - - -#### `tf.DType.as_datatype_enum` <a class="md-anchor" id="DType.as_datatype_enum"></a> +#### `tf.DType.as_datatype_enum` {#DType.as_datatype_enum} Returns a `types_pb2.DataType` enum value based on this `DType`. -#### Other Methods <a class="md-anchor" id="AUTOGENERATED-other-methods"></a> +#### Other Methods - - - -#### `tf.DType.__init__(type_enum)` <a class="md-anchor" id="DType.__init__"></a> +#### `tf.DType.__init__(type_enum)` {#DType.__init__} Creates a new `DataType`. @@ -1221,12 +1190,12 @@ NOTE(mrry): In normal circumstances, you should not need to construct a DataType object directly. Instead, use the types.as_dtype() function. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`type_enum`</b>: A `types_pb2.DataType` enum value. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: If `type_enum` is not a value `types_pb2.DataType`. @@ -1234,28 +1203,28 @@ types.as_dtype() function. - - - -#### `tf.DType.is_floating` <a class="md-anchor" id="DType.is_floating"></a> +#### `tf.DType.is_floating` {#DType.is_floating} Returns whether this is a (real) floating point type. - - - -#### `tf.DType.max` <a class="md-anchor" id="DType.max"></a> +#### `tf.DType.max` {#DType.max} Returns the maximum representable value in this data type. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: if this is a non-numeric, unordered, or quantized type. - - - -#### `tf.DType.min` <a class="md-anchor" id="DType.min"></a> +#### `tf.DType.min` {#DType.min} Returns the minimum representable value in this data type. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: if this is a non-numeric, unordered, or quantized type. @@ -1263,11 +1232,11 @@ Returns the minimum representable value in this data type. - - - -### `tf.as_dtype(type_value)` <a class="md-anchor" id="as_dtype"></a> +### `tf.as_dtype(type_value)` {#as_dtype} Converts the given `type_value` to a `DType`. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`type_value`</b>: A value that can be converted to a `tf.DType` @@ -1275,22 +1244,22 @@ Converts the given `type_value` to a `DType`. [`DataType` enum](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/types.proto), a string type name, or a `numpy.dtype`. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `DType` corresponding to `type_value`. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: If `type_value` cannot be converted to a `DType`. -## Utility functions <a class="md-anchor" id="AUTOGENERATED-utility-functions"></a> +## Utility functions - - - -### `tf.device(dev)` <a class="md-anchor" id="device"></a> +### `tf.device(dev)` {#device} Wrapper for `Graph.device()` using the default graph. @@ -1298,13 +1267,13 @@ See [`Graph.name_scope()`](../../api_docs/python/framework.md#Graph.name_scope) for more details. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`device_name_or_function`</b>: The device name or function to use in the context. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A context manager that specifies the default device to use for newly created ops. @@ -1312,7 +1281,7 @@ for more details. - - - -### `tf.name_scope(name)` <a class="md-anchor" id="name_scope"></a> +### `tf.name_scope(name)` {#name_scope} Wrapper for `Graph.name_scope()` using the default graph. @@ -1320,12 +1289,12 @@ See [`Graph.name_scope()`](../../api_docs/python/framework.md#Graph.name_scope) for more details. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`name`</b>: A name for the scope. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A context manager that installs `name` as a new name scope in the default graph. @@ -1333,21 +1302,21 @@ for more details. - - - -### `tf.control_dependencies(control_inputs)` <a class="md-anchor" id="control_dependencies"></a> +### `tf.control_dependencies(control_inputs)` {#control_dependencies} Wrapper for `Graph.control_dependencies()` using the default graph. See [`Graph.control_dependencies()`](../../api_docs/python/framework.md#Graph.control_dependencies) for more details. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`control_inputs`</b>: A list of `Operation` or `Tensor` objects, which must be executed or computed before running the operations defined in the context. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A context manager that specifies control dependencies for all operations constructed within the context. @@ -1355,7 +1324,7 @@ for more details. - - - -### `tf.convert_to_tensor(value, dtype=None, name=None)` <a class="md-anchor" id="convert_to_tensor"></a> +### `tf.convert_to_tensor(value, dtype=None, name=None)` {#convert_to_tensor} Converts the given `value` to a `Tensor`. @@ -1383,7 +1352,7 @@ constructors apply this function to each of their Tensor-valued inputs, which allows those ops to accept numpy arrays, Python lists, and scalars in addition to `Tensor` objects. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`value`</b>: An object whose type has a registered `Tensor` conversion function. @@ -1391,11 +1360,11 @@ and scalars in addition to `Tensor` objects. type is inferred from the type of `value`. * <b>`name`</b>: Optional name to use if a new `Tensor` is created. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` based on `value`. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: If no conversion function is registered for `value`. @@ -1404,7 +1373,7 @@ and scalars in addition to `Tensor` objects. - - - -### `tf.get_default_graph()` <a class="md-anchor" id="get_default_graph"></a> +### `tf.get_default_graph()` {#get_default_graph} Returns the default graph for the current thread. @@ -1417,14 +1386,14 @@ create a new thread, and wish to use the default graph in that thread, you must explicitly add a `with g.as_default():` in that thread's function. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: The default `Graph` being used in the current thread. - - - -### `tf.import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None)` <a class="md-anchor" id="import_graph_def"></a> +### `tf.import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None)` {#import_graph_def} Imports the TensorFlow graph in `graph_def` into the Python `Graph`. @@ -1435,7 +1404,7 @@ protocol buffer, and extract individual objects in the `GraphDef` as [`Graph.as_graph_def()`](#Graph.as_graph_def) for a way to create a `GraphDef` proto. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`graph_def`</b>: A `GraphDef` proto containing operations to be imported into @@ -1452,12 +1421,12 @@ protocol buffer, and extract individual objects in the `GraphDef` as Must contain an `OpDef` proto for each op type named in `graph_def`. If omitted, uses the `OpDef` protos registered in the global registry. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A list of `Operation` and/or `Tensor` objects from the imported graph, corresponding to the names in `return_elements'. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: If `graph_def` is not a `GraphDef` proto, @@ -1469,18 +1438,18 @@ protocol buffer, and extract individual objects in the `GraphDef` as -## Graph collections <a class="md-anchor" id="AUTOGENERATED-graph-collections"></a> +## Graph collections - - - -### `tf.add_to_collection(name, value)` <a class="md-anchor" id="add_to_collection"></a> +### `tf.add_to_collection(name, value)` {#add_to_collection} Wrapper for `Graph.add_to_collection()` using the default graph. See [`Graph.add_to_collection()`](../../api_docs/python/framework.md#Graph.add_to_collection) for more details. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`name`</b>: The key for the collection. For example, the `GraphKeys` class @@ -1490,14 +1459,14 @@ for more details. - - - -### `tf.get_collection(key, scope=None)` <a class="md-anchor" id="get_collection"></a> +### `tf.get_collection(key, scope=None)` {#get_collection} Wrapper for `Graph.get_collection()` using the default graph. See [`Graph.get_collection()`](../../api_docs/python/framework.md#Graph.get_collection) for more details. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`key`</b>: The key for the collection. For example, the `GraphKeys` class @@ -1505,7 +1474,7 @@ for more details. * <b>`scope`</b>: (Optional.) If supplied, the resulting list is filtered to include only items whose name begins with this string. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: The list of values in the collection with the given `name`, or an empty list if no value has been added to that collection. The @@ -1515,7 +1484,7 @@ for more details. - - - -### `class tf.GraphKeys` <a class="md-anchor" id="GraphKeys"></a> +### `class tf.GraphKeys` {#GraphKeys} Standard names to use for graph collections. @@ -1546,11 +1515,11 @@ The following standard keys are defined: for more details. -## Defining new operations <a class="md-anchor" id="AUTOGENERATED-defining-new-operations"></a> +## Defining new operations - - - -### `class tf.RegisterGradient` <a class="md-anchor" id="RegisterGradient"></a> +### `class tf.RegisterGradient` {#RegisterGradient} A decorator for registering the gradient function for an op type. @@ -1577,11 +1546,11 @@ that defines the operation. - - - -#### `tf.RegisterGradient.__init__(op_type)` <a class="md-anchor" id="RegisterGradient.__init__"></a> +#### `tf.RegisterGradient.__init__(op_type)` {#RegisterGradient.__init__} Creates a new decorator with `op_type` as the Operation type. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`op_type`</b>: The string type of an operation. This corresponds to the @@ -1591,7 +1560,7 @@ Creates a new decorator with `op_type` as the Operation type. - - - -### `tf.NoGradient(op_type)` <a class="md-anchor" id="NoGradient"></a> +### `tf.NoGradient(op_type)` {#NoGradient} Specifies that ops of type `op_type` do not have a defined gradient. @@ -1603,13 +1572,13 @@ example: tf.NoGradient("Size") ``` -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`op_type`</b>: The string type of an operation. This corresponds to the `OpDef.name` field for the proto that defines the operation. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`TypeError`</b>: If `op_type` is not a string. @@ -1617,7 +1586,7 @@ tf.NoGradient("Size") - - - -### `class tf.RegisterShape` <a class="md-anchor" id="RegisterShape"></a> +### `class tf.RegisterShape` {#RegisterShape} A decorator for registering the shape function for an op type. @@ -1641,7 +1610,7 @@ operation. This corresponds to the `OpDef.name` field for the proto that defines the operation. - - - -#### `tf.RegisterShape.__init__(op_type)` <a class="md-anchor" id="RegisterShape.__init__"></a> +#### `tf.RegisterShape.__init__(op_type)` {#RegisterShape.__init__} Saves the "op_type" as the Operation type. @@ -1649,7 +1618,7 @@ Saves the "op_type" as the Operation type. - - - -### `class tf.TensorShape` <a class="md-anchor" id="TensorShape"></a> +### `class tf.TensorShape` {#TensorShape} Represents the shape of a `Tensor`. @@ -1672,24 +1641,24 @@ explicitly using [`Tensor.set_shape()`](../../api_docs/python/framework.md#Tenso - - - -#### `tf.TensorShape.merge_with(other)` <a class="md-anchor" id="TensorShape.merge_with"></a> +#### `tf.TensorShape.merge_with(other)` {#TensorShape.merge_with} Returns a `TensorShape` combining the information in `self` and `other`. The dimensions in `self` and `other` are merged elementwise, according to the rules defined for `Dimension.merge_with()`. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`other`</b>: Another `TensorShape`. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `TensorShape` containing the combined information of `self` and `other`. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `self` and `other` are not compatible. @@ -1697,7 +1666,7 @@ according to the rules defined for `Dimension.merge_with()`. - - - -#### `tf.TensorShape.concatenate(other)` <a class="md-anchor" id="TensorShape.concatenate"></a> +#### `tf.TensorShape.concatenate(other)` {#TensorShape.concatenate} Returns the concatenation of the dimension in `self` and `other`. @@ -1706,12 +1675,12 @@ concatenation will discard information about the other shape. In future, we might support concatenation that preserves this information for use with slicing. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`other`</b>: Another `TensorShape`. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `TensorShape` whose dimensions are the concatenation of the dimensions in `self` and `other`. @@ -1720,26 +1689,26 @@ information for use with slicing. - - - -#### `tf.TensorShape.ndims` <a class="md-anchor" id="TensorShape.ndims"></a> +#### `tf.TensorShape.ndims` {#TensorShape.ndims} Returns the rank of this shape, or None if it is unspecified. - - - -#### `tf.TensorShape.dims` <a class="md-anchor" id="TensorShape.dims"></a> +#### `tf.TensorShape.dims` {#TensorShape.dims} Returns a list of Dimensions, or None if the shape is unspecified. - - - -#### `tf.TensorShape.as_list()` <a class="md-anchor" id="TensorShape.as_list"></a> +#### `tf.TensorShape.as_list()` {#TensorShape.as_list} Returns a list of integers or None for each dimension. - - - -#### `tf.TensorShape.is_compatible_with(other)` <a class="md-anchor" id="TensorShape.is_compatible_with"></a> +#### `tf.TensorShape.is_compatible_with(other)` {#TensorShape.is_compatible_with} Returns True iff `self` is compatible with `other`. @@ -1771,19 +1740,19 @@ TensorShape(None), and TensorShape(None) is compatible with TensorShape([4, 4]), but TensorShape([32, 784]) is not compatible with TensorShape([4, 4]). -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`other`</b>: Another TensorShape. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: True iff `self` is compatible with `other`. - - - -#### `tf.TensorShape.is_fully_defined()` <a class="md-anchor" id="TensorShape.is_fully_defined"></a> +#### `tf.TensorShape.is_fully_defined()` {#TensorShape.is_fully_defined} Returns True iff `self` is fully defined in every dimension. @@ -1791,23 +1760,23 @@ Returns True iff `self` is fully defined in every dimension. - - - -#### `tf.TensorShape.with_rank(rank)` <a class="md-anchor" id="TensorShape.with_rank"></a> +#### `tf.TensorShape.with_rank(rank)` {#TensorShape.with_rank} Returns a shape based on `self` with the given rank. This method promotes a completely unknown shape to one with a known rank. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`rank`</b>: An integer. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A shape that is at least as specific as `self` with the given rank. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `self` does not represent a shape with the given `rank`. @@ -1815,21 +1784,21 @@ known rank. - - - -#### `tf.TensorShape.with_rank_at_least(rank)` <a class="md-anchor" id="TensorShape.with_rank_at_least"></a> +#### `tf.TensorShape.with_rank_at_least(rank)` {#TensorShape.with_rank_at_least} Returns a shape based on `self` with at least the given rank. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`rank`</b>: An integer. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A shape that is at least as specific as `self` with at least the given rank. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `self` does not represent a shape with at least the given @@ -1838,21 +1807,21 @@ Returns a shape based on `self` with at least the given rank. - - - -#### `tf.TensorShape.with_rank_at_most(rank)` <a class="md-anchor" id="TensorShape.with_rank_at_most"></a> +#### `tf.TensorShape.with_rank_at_most(rank)` {#TensorShape.with_rank_at_most} Returns a shape based on `self` with at most the given rank. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`rank`</b>: An integer. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A shape that is at least as specific as `self` with at most the given rank. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `self` does not represent a shape with at most the given @@ -1862,16 +1831,16 @@ Returns a shape based on `self` with at most the given rank. - - - -#### `tf.TensorShape.assert_has_rank(rank)` <a class="md-anchor" id="TensorShape.assert_has_rank"></a> +#### `tf.TensorShape.assert_has_rank(rank)` {#TensorShape.assert_has_rank} Raises an exception if `self` is not compatible with the given `rank`. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`rank`</b>: An integer. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `self` does not represent a shape with the given `rank`. @@ -1879,16 +1848,16 @@ Raises an exception if `self` is not compatible with the given `rank`. - - - -#### `tf.TensorShape.assert_same_rank(other)` <a class="md-anchor" id="TensorShape.assert_same_rank"></a> +#### `tf.TensorShape.assert_same_rank(other)` {#TensorShape.assert_same_rank} Raises an exception if `self` and `other` do not have compatible ranks. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`other`</b>: Another `TensorShape`. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `self` and `other` do not represent shapes with the @@ -1897,19 +1866,19 @@ Raises an exception if `self` and `other` do not have compatible ranks. - - - -#### `tf.TensorShape.assert_is_compatible_with(other)` <a class="md-anchor" id="TensorShape.assert_is_compatible_with"></a> +#### `tf.TensorShape.assert_is_compatible_with(other)` {#TensorShape.assert_is_compatible_with} Raises exception if `self` and `other` do not represent the same shape. This method can be used to assert that there exists a shape that both `self` and `other` represent. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`other`</b>: Another TensorShape. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `self` and `other` do not represent the same shape. @@ -1917,25 +1886,25 @@ This method can be used to assert that there exists a shape that both - - - -#### `tf.TensorShape.assert_is_fully_defined()` <a class="md-anchor" id="TensorShape.assert_is_fully_defined"></a> +#### `tf.TensorShape.assert_is_fully_defined()` {#TensorShape.assert_is_fully_defined} Raises an exception if `self` is not fully defined in every dimension. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `self` does not have a known value for every dimension. -#### Other Methods <a class="md-anchor" id="AUTOGENERATED-other-methods"></a> +#### Other Methods - - - -#### `tf.TensorShape.__init__(dims)` <a class="md-anchor" id="TensorShape.__init__"></a> +#### `tf.TensorShape.__init__(dims)` {#TensorShape.__init__} Creates a new TensorShape with the given dimensions. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`dims`</b>: A list of Dimensions, or None if the shape is unspecified. @@ -1944,14 +1913,14 @@ Creates a new TensorShape with the given dimensions. - - - -#### `tf.TensorShape.as_dimension_list()` <a class="md-anchor" id="TensorShape.as_dimension_list"></a> +#### `tf.TensorShape.as_dimension_list()` {#TensorShape.as_dimension_list} DEPRECATED: use as_list(). - - - -#### `tf.TensorShape.num_elements()` <a class="md-anchor" id="TensorShape.num_elements"></a> +#### `tf.TensorShape.num_elements()` {#TensorShape.num_elements} Returns the total number of elements, or none for incomplete shapes. @@ -1959,28 +1928,28 @@ Returns the total number of elements, or none for incomplete shapes. - - - -### `class tf.Dimension` <a class="md-anchor" id="Dimension"></a> +### `class tf.Dimension` {#Dimension} Represents the value of one dimension in a TensorShape. - - - -#### `tf.Dimension.__init__(value)` <a class="md-anchor" id="Dimension.__init__"></a> +#### `tf.Dimension.__init__(value)` {#Dimension.__init__} Creates a new Dimension with the given value. - - - -#### `tf.Dimension.assert_is_compatible_with(other)` <a class="md-anchor" id="Dimension.assert_is_compatible_with"></a> +#### `tf.Dimension.assert_is_compatible_with(other)` {#Dimension.assert_is_compatible_with} Raises an exception if `other` is not compatible with this Dimension. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`other`</b>: Another Dimension. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `self` and `other` are not compatible (see @@ -1989,26 +1958,26 @@ Raises an exception if `other` is not compatible with this Dimension. - - - -#### `tf.Dimension.is_compatible_with(other)` <a class="md-anchor" id="Dimension.is_compatible_with"></a> +#### `tf.Dimension.is_compatible_with(other)` {#Dimension.is_compatible_with} Returns true if `other` is compatible with this Dimension. Two known Dimensions are compatible if they have the same value. An unknown Dimension is compatible with all other Dimensions. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`other`</b>: Another Dimension. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: True if this Dimension and `other` are compatible. - - - -#### `tf.Dimension.merge_with(other)` <a class="md-anchor" id="Dimension.merge_with"></a> +#### `tf.Dimension.merge_with(other)` {#Dimension.merge_with} Returns a Dimension that combines the information in `self` and `other`. @@ -2020,17 +1989,17 @@ Dimensions are combined as follows: Dimension(None).merge_with(Dimension(None)) == Dimension(None) Dimension(n) .merge_with(Dimension(m)) raises ValueError for n != m -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`other`</b>: Another Dimension. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A Dimension containing the combined information of `self` and `other`. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `self` and `other` are not compatible (see @@ -2039,14 +2008,14 @@ Dimensions are combined as follows: - - - -#### `tf.Dimension.value` <a class="md-anchor" id="Dimension.value"></a> +#### `tf.Dimension.value` {#Dimension.value} The value of this dimension, or None if it is unknown. - - - -### `tf.op_scope(values, name, default_name)` <a class="md-anchor" id="op_scope"></a> +### `tf.op_scope(values, name, default_name)` {#op_scope} Returns a context manager for use when defining a Python op. @@ -2066,21 +2035,21 @@ def my_op(a, b, c, name=None): return foo_op(..., name=scope) ``` -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`values`</b>: The list of `Tensor` arguments that are passed to the op function. * <b>`name`</b>: The name argument that is passed to the op function. * <b>`default_name`</b>: The default name to use if the `name` argument is `None`. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A context manager for use in defining a Python op. - - - -### `tf.get_seed(op_seed)` <a class="md-anchor" id="get_seed"></a> +### `tf.get_seed(op_seed)` {#get_seed} Returns the local seeds an operation should use given an op-specific seed. @@ -2092,12 +2061,12 @@ graph, or for only specific operations. For details on how the graph-level seed interacts with op seeds, see [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed). -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`op_seed`</b>: integer. -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A tuple of two integers that should be used for the local seed of this operation. |