tf.keras.layers.TimeDistributed

Class TimeDistributed

Inherits From: Wrapper

Defined in tensorflow/python/keras/layers/wrappers.py.

This wrapper allows to apply a layer to every temporal slice of an input.

The input should be at least 3D, and the dimension of index one will be considered to be the temporal dimension.

Consider a batch of 32 samples, where each sample is a sequence of 10 vectors of 16 dimensions. The batch input shape of the layer is then (32, 10, 16), and the input_shape, not including the samples dimension, is (10, 16).

You can then use TimeDistributed to apply a Dense layer to each of the 10 timesteps, independently:

    # as the first layer in a model
    model = Sequential()
    model.add(TimeDistributed(Dense(8), input_shape=(10, 16)))
    # now model.output_shape == (None, 10, 8)

The output will then have shape (32, 10, 8).

In subsequent layers, there is no need for the input_shape:

    model.add(TimeDistributed(Dense(32)))
    # now model.output_shape == (None, 10, 32)

The output will then have shape (32, 10, 32).

TimeDistributed can be used with arbitrary layers, not just Dense, for instance with a Conv2D layer:

    model = Sequential()
    model.add(TimeDistributed(Conv2D(64, (3, 3)),
                              input_shape=(10, 299, 299, 3)))

Arguments:

  • layer: a layer instance.

Raises:

  • ValueError: If not initialized with a Layer instance.

Properties

activity_regularizer

dtype

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.

losses

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.

trainable

trainable_variables

trainable_weights

updates

variables

Returns the list of all layer variables/weights.

Returns:

A list of variables.

weights

Returns the list of all layer variables/weights.

Returns:

A list of variables.

Methods

__init__

__init__(
    layer,
    **kwargs
)

__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.

Returns:

Output tensor(s).

Raises:

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

add_loss

add_loss(
    losses,
    inputs=None
)

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) 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.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Note that add_loss is not supported when executing eagerly. Instead, variable regularizers may be added through add_variable. Activity regularization is not supported directly (but such losses may be returned from Layer.call()).

Arguments:

  • losses: Loss tensor, or list/tuple of tensors.
  • inputs: If anything other than None is passed, it signals the losses 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 activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).

Raises:

  • RuntimeError: If called in Eager mode.

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(
    *args,
    **kwargs
)

Alias for add_weight.

add_weight

add_weight(
    name,
    shape,
    dtype=None,
    initializer=None,
    regularizer=None,
    trainable=None,
    constraint=None,
    partitioner=None,
    use_resource=None,
    synchronization=tf.VariableSynchronization.AUTO,
    aggregation=tf.VariableAggregation.NONE,
    getter=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).
  • partitioner: Partitioner to be passed to the Checkpointable API.
  • 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.
  • getter: Variable getter argument to be passed to the Checkpointable API.

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 giving unsupported dtype and no initializer or 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)

call

call(
    inputs,
    training=None,
    mask=None
)

compute_mask

compute_mask(
    inputs,
    mask=None
)

Computes an output mask tensor for Embedding layer.

This is based on the inputs, mask, and the inner layer. If batch size is specified: Simply return the input mask. (An rnn-based implementation with more than one rnn inputs is required but not supported in tf.keras yet.) Otherwise we call compute_mask of the inner layer at each time step. If the output mask at each time step is not None: (E.g., inner layer is Masking or RNN) Concatenate all of them and return the concatenation. If the output mask at each time step is None and the input mask is not None:(E.g., inner layer is Dense) Reduce the input_mask to 2 dimensions and return it. Otherwise (both the output mask and the input mask are None): (E.g., mask is not used at all) Return None.

Arguments:

  • inputs: Tensor with shape [batch size, timesteps, ...] indicating the input to TimeDistributed. If static shape information is available for "batch size", mask is returned unmodified.
  • mask: Either None (indicating no masking) or a Tensor indicating the input mask for TimeDistributed. The shape can be static or dynamic.

Returns:

Either None (no masking), or a [batch size, timesteps, ...] Tensor with an output mask for the TimeDistributed layer with the shape beyond the second dimension being the value of the input mask shape(if the computed output mask is none), an output mask with the shape beyond the first dimension being the value of the mask shape(if mask is not None) or output mask with the shape beyond the first dimension being the value of the computed output shape.

compute_output_shape

compute_output_shape(input_shape)

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,
    custom_objects=None
)

get_config

get_config()

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_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()

set_weights

set_weights(weights)