tf.contrib.rnn.LSTMBlockFusedCell

Class LSTMBlockFusedCell

Inherits From: LSTMBlockWrapper

Defined in tensorflow/contrib/rnn/python/ops/lstm_ops.py.

See the guide: RNN and Cells (contrib) > Core RNN Cell wrappers (RNNCells that wrap other RNNCells)

FusedRNNCell implementation of LSTM.

This is an extremely efficient LSTM implementation, that uses a single TF op for the entire LSTM. It should be both faster and more memory-efficient than LSTMBlockCell defined above.

The implementation is based on: http://arxiv.org/abs/1409.2329.

We add forget_bias (default: 1) to the biases of the forget gate in order to reduce the scale of forgetting in the beginning of the training.

The variable naming is consistent with rnn_cell_impl.LSTMCell.

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.

losses

Losses which are associated with this Layer.

Note that when executing eagerly, getting this property 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

num_units

Number of units in this cell (output dimension).

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_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__(
    num_units,
    forget_bias=1.0,
    cell_clip=None,
    use_peephole=False,
    reuse=None,
    name='lstm_fused_cell'
)

Initialize the LSTM cell.

Args:

• num_units: int, The number of units in the LSTM cell.
• forget_bias: float, The bias added to forget gates (see above).
• cell_clip: clip the cell to this value. Default is no cell clipping.
• use_peephole: Whether to use peephole connections or not.
• reuse: (optional) boolean describing whether to reuse variables in an existing scope. If not True, and the existing scope already has the given variables, an error is raised.
• name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. By default this is "lstm_cell", for variable-name compatibility with tf.nn.rnn_cell.LSTMCell.

__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(
    *args,
    **kwargs
)

Alias for add_weight.

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)

call

call(
    inputs,
    initial_state=None,
    dtype=None,
    sequence_length=None
)

Run this LSTM on inputs, starting from the given state.

Args:

• inputs: 3-D tensor with shape [time_len, batch_size, input_size].
• initial_state: a tuple (initial_cell_state, initial_output) with tensors of shape [batch_size, self._num_units]. If this is not provided, the cell is expected to create a zero initial state of type dtype.
• dtype: The data type for the initial state and expected output. Required if initial_state is not provided or RNN state has a heterogeneous dtype.
• sequence_length: Specifies the length of each sequence in inputs. An int32 or int64 vector (tensor) size [batch_size], values in [0, time_len). Defaults to time_len for each element.

Returns:

A pair containing:

• Output: A 3-D tensor of shape [time_len, batch_size, output_size] or a list of time_len tensors of shape [batch_size, output_size], to match the type of the inputs.
• Final state: a tuple (cell_state, output) matching initial_state.

Raises:

• ValueError: in case of shape mismatches

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_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.