tf.contrib.eager.Network

Class Network

Inherits From: Layer

Defined in tensorflow/contrib/eager/python/network.py.

Represents the composition of a set of Layers.

Deprecated. Please inherit from tf.keras.Model, and see its documentation for details. tf.keras.Model should be a drop-in replacement for tfe.Network in most cases, but note that track_layer is no longer necessary or supported. Instead, Layer instances are tracked on attribute assignment (see the section of tf.keras.Model's documentation on subclassing). Since the output of track_layer is often assigned to an attribute anyway, most code can be ported by simply removing the track_layer calls.

tf.keras.Model works with all TensorFlow Layer instances, including those from tf.layers, but switching to the tf.keras.layers versions along with the migration to tf.keras.Model is recommended, since it will preserve variable names. Feel free to import it with an alias to avoid excess typing :).

Network implements the Layer interface and adds convenience methods for managing sub-Layers, such as listing variables.

Layers (including other Networks) should be added via track_layer. They can then be used when overriding the Network.call method:

class TwoLayerNetwork(tfe.Network):

  def __init__(self, name):
    super(TwoLayerNetwork, self).__init__(name=name)
    self.layer_one = self.track_layer(tf.layers.Dense(16, input_shape=(8,)))
    self.layer_two = self.track_layer(tf.layers.Dense(1, input_shape=(16,)))

  def call(self, inputs):
    return self.layer_two(self.layer_one(inputs))

After constructing an object and calling the Network, a list of variables created by tracked Layers is available via Network.variables:

net = TwoLayerNetwork(name="net")
output = net(tf.ones([1, 8]))
print([v.name for v in net.variables])

This example prints variable names, one kernel and one bias per tf.layers.Dense layer:

['net/dense/kernel:0',
 'net/dense/bias:0',
 'net/dense_1/kernel:0',
 'net/dense_1/bias:0']

These variables can be passed to a Saver (tf.train.Saver, or tf.contrib.eager.Saver when executing eagerly) to save or restore the Network, typically alongside a global step and tf.train.Optimizer variables when checkpointing during training.

Note that the semantics of calling a Network with graph execution (i.e. not executing eagerly) may change slightly in the future. Currently stateful ops are pruned from the graph unless they or something that depends on them is executed in a session, but this behavior is not consistent with eager execution (where stateful ops are executed eagerly). Layers from tf.layers do not depend on this pruning and so will not be affected, but Networks which rely on stateful ops being added to the graph but not executed (e.g. via custom Layers which manage stateful ops) may break with this change.

Properties

activity_regularizer

Optional regularizer function for the output of this layer.

dtype

graph

inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns:

Input tensor or list of input tensors.

Raises:

  • AttributeError: if the layer is connected to more than one incoming layers.

Raises:

  • RuntimeError: If called in Eager mode.
  • AttributeError: If no inbound nodes are found.

input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Input mask tensor (potentially None) or list of input mask tensors.

Raises:

  • AttributeError: if the layer is connected to more than one incoming layers.

input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns:

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

Raises:

  • AttributeError: if the layer has no defined input_shape.
  • RuntimeError: if called in Eager mode.

layers

losses

Gather losses from Layers in the Network.

Note that when executing eagerly, Layer.losses evaluates regularizers. When using graph execution, variable regularization ops have already been created and are simply returned here.

Returns:

A list of tensors.

name

non_trainable_variables

non_trainable_weights

outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns:

Output tensor or list of output tensors.

Raises:

  • AttributeError: if the layer is connected to more than one incoming layers.
  • RuntimeError: if called in Eager mode.

output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Output mask tensor (potentially None) or list of output mask tensors.

Raises:

  • AttributeError: if the layer is connected to more than one incoming layers.

output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns:

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

Raises:

  • AttributeError: if the layer has no defined output shape.
  • RuntimeError: if called in Eager mode.

scope_name

trainable

trainable_variables

trainable_weights

updates

variables

Returns the list of all layer variables/weights.

Returns:

A list of variables.

weights

Methods

__init__

__init__(name=None)

Configure the Network. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please inherit from tf.keras.Model, and see its documentation for details. tf.keras.Model should be a drop-in replacement for tfe.Network in most cases, but note that track_layer is no longer necessary or supported. Instead, Layer instances are tracked on attribute assignment (see the section of tf.keras.Model's documentation on subclassing). Since the output of track_layer is often assigned to an attribute anyway, most code can be ported by simply removing the track_layer calls.

tf.keras.Model works with all TensorFlow Layer instances, including those from tf.layers, but switching to the tf.keras.layers versions along with the migration to tf.keras.Model is recommended, since it will preserve variable names. Feel free to import it with an alias to avoid excess typing :).

Args:

  • name: The name to use for this Network. If specified, it must be unique in the context where this Network is first (1) added to another Network (in which case it must not share a name with other Layers added to that Network), or (2) built/called (in which case no other 'top-level' Networks may share this name). If unspecified or None, the Network will be named using its class name, with a number appended if necessary for uniqueness (e.g. MyNetwork -> 'my_network_1').

Raises:

  • ValueError: If name is not valid. Note that some naming errors will instead be raised when the Network is called.

__call__

__call__(
    inputs,
    *args,
    **kwargs
)

Wraps call, applying pre- and post-processing steps.

Arguments:

  • inputs: input tensor(s).
  • *args: additional positional arguments to be passed to self.call.
  • **kwargs: additional keyword arguments to be passed to self.call. Note: kwarg scope is reserved for use by the layer.

Returns:

Output tensor(s).

Raises:

  • ValueError: if the layer's call method returns None (an invalid value).

__deepcopy__

__deepcopy__(memo)

add_loss

add_loss(
    losses,
    inputs=None
)

add_update

add_update(
    updates,
    inputs=None
)

Add update op(s), potentially dependent on layer inputs.

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Arguments:

  • updates: Update op, or list/tuple of update ops.
  • inputs: If anything other than None is passed, it signals the updates are conditional on some of the layer's inputs, and thus they should only be run where these inputs are available. This is the case for BatchNormalization updates, for instance. If None, the updates will be taken into account unconditionally, and you are responsible for making sure that any dependency they might have is available at runtime. A step counter might fall into this category.

add_variable

add_variable(
    name,
    shape,
    dtype=None,
    initializer=None,
    regularizer=None,
    trainable=True,
    constraint=None
)

add_weight

add_weight(
    name,
    shape,
    dtype=None,
    initializer=None,
    regularizer=None,
    trainable=None,
    constraint=None,
    use_resource=None,
    synchronization=tf.VariableSynchronization.AUTO,
    aggregation=tf.VariableAggregation.NONE,
    partitioner=None
)

Adds a new variable to the layer, or gets an existing one; returns it.

Arguments:

  • name: variable name.
  • shape: variable shape.
  • dtype: The type of the variable. Defaults to self.dtype or float32.
  • initializer: initializer instance (callable).
  • regularizer: regularizer instance (callable).
  • trainable: whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean, stddev). Note, if the current variable scope is marked as non-trainable then this parameter is ignored and any added variables are also marked as non-trainable. trainable defaults to True unless synchronization is set to ON_READ.
  • constraint: constraint instance (callable).
  • use_resource: Whether to use ResourceVariable.
  • synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True.
  • aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation.
  • partitioner: (optional) partitioner instance (callable). If provided, when the requested variable is created it will be split into multiple partitions according to partitioner. In this case, an instance of PartitionedVariable is returned. Available partitioners include tf.fixed_size_partitioner and tf.variable_axis_size_partitioner. For more details, see the documentation of tf.get_variable and the "Variable Partitioners and Sharding" section of the API guide.

Returns:

The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned.

Raises:

  • RuntimeError: If called with partioned variable regularization and eager execution is enabled.
  • ValueError: When trainable has been set to True with synchronization set as ON_READ.

apply

apply(
    inputs,
    *args,
    **kwargs
)

Apply the layer on a input.

This simply wraps self.__call__.

Arguments:

  • inputs: Input tensor(s).
  • *args: additional positional arguments to be passed to self.call.
  • **kwargs: additional keyword arguments to be passed to self.call.

Returns:

Output tensor(s).

build

build(input_shape)

Creates the variables of the layer.

call

call(
    inputs,
    **kwargs
)

This is where the layer's logic lives.

Arguments:

  • inputs: Input tensor, or list/tuple of input tensors.
  • **kwargs: Additional keyword arguments.

Returns:

A tensor or list/tuple of tensors.

compute_mask

compute_mask(
    inputs,
    mask=None
)

Computes an output mask tensor.

Arguments:

  • inputs: Tensor or list of tensors.
  • mask: Tensor or list of tensors.

Returns:

None or a tensor (or list of tensors, one per output tensor of the layer).

compute_output_shape

compute_output_shape(input_shape)

Computes the output shape of the layer.

Assumes that the layer will be built to match that input shape provided.

Arguments:

  • input_shape: Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns:

An input shape tuple.

count_params

count_params()

Count the total number of scalars composing the weights.

Returns:

An integer count.

Raises:

  • ValueError: if the layer isn't yet built (in which case its weights aren't yet defined).

from_config

from_config(
    cls,
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Arguments:

  • config: A Python dictionary, typically the output of get_config.

Returns:

A layer instance.

get_config

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns:

Python dictionary.

get_input_at

get_input_at(node_index)

Retrieves the input tensor(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple inputs).

Raises:

  • RuntimeError: If called in Eager mode.

get_input_mask_at

get_input_mask_at(node_index)

Retrieves the input mask tensor(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A mask tensor (or list of tensors if the layer has multiple inputs).

get_input_shape_at

get_input_shape_at(node_index)

Retrieves the input shape(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A shape tuple (or list of shape tuples if the layer has multiple inputs).

Raises:

  • RuntimeError: If called in Eager mode.

get_layer

get_layer(
    name=None,
    index=None
)

Get a contained tf.layers.Layer either by name or index.

Args:

  • name: String matching one of the names of a contained Layer. Note that the names of Layers added to Networks may not be unique when doing layer sharing (i.e. adding a Layer to this Network which was already added to another Network). The lowest index Layer with a matching name will be returned.
  • index: Integer in [0, number of layers). Layers are assigned an index by the order they are added.

Returns:

A tf.layers.Layer object.

Raises:

  • ValueError: If neither or both of 'index' or 'name' is specified, or the lookup failed.

get_losses_for

get_losses_for(inputs)

Retrieves losses relevant to a specific set of inputs.

Arguments:

  • inputs: Input tensor or list/tuple of input tensors.

Returns:

List of loss tensors of the layer that depend on inputs.

Raises:

  • RuntimeError: If called in Eager mode.

get_output_at

get_output_at(node_index)

Retrieves the output tensor(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple outputs).

Raises:

  • RuntimeError: If called in Eager mode.

get_output_mask_at

get_output_mask_at(node_index)

Retrieves the output mask tensor(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A mask tensor (or list of tensors if the layer has multiple outputs).

get_output_shape_at

get_output_shape_at(node_index)

Retrieves the output shape(s) of a layer at a given node.

Arguments:

  • node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns:

A shape tuple (or list of shape tuples if the layer has multiple outputs).

Raises:

  • RuntimeError: If called in Eager mode.

get_updates_for

get_updates_for(inputs)

Retrieves updates relevant to a specific set of inputs.

Arguments:

  • inputs: Input tensor or list/tuple of input tensors.

Returns:

List of update ops of the layer that depend on inputs.

Raises:

  • RuntimeError: If called in Eager mode.

get_weights

get_weights()

Returns the current weights of the layer.

Returns:

Weights values as a list of numpy arrays.

set_weights

set_weights(weights)

Sets the weights of the layer, from Numpy arrays.

Arguments:

  • weights: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights).

Raises:

  • ValueError: If the provided weights list does not match the layer's specifications.

track_layer

track_layer(layer)

Track a Layer in this Network.

Network requires that all Layers used in call() be tracked so that the Network can export a complete list of variables.

Args:

Returns:

The passed in layer.

Raises:

  • RuntimeError: If init has not been called.
  • TypeError: If layer is the wrong type.
  • ValueError: If a Layer with the same name has already been added.