tf.keras.layers.LSTM

Class LSTM

Inherits From: RNN

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

Long Short-Term Memory layer - Hochreiter 1997.

Note that this cell is not optimized for performance on GPU. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.

Arguments:

  • units: Positive integer, dimensionality of the output space.
  • activation: Activation function to use. Default: hyperbolic tangent (tanh). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).
  • recurrent_activation: Activation function to use for the recurrent step. Default: hard sigmoid (hard_sigmoid). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).
  • use_bias: Boolean, whether the layer uses a bias vector.
  • kernel_initializer: Initializer for the kernel weights matrix, used for the linear transformation of the inputs..
  • recurrent_initializer: Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state..
  • bias_initializer: Initializer for the bias vector.
  • unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force bias_initializer="zeros". This is recommended in Jozefowicz et al.
  • kernel_regularizer: Regularizer function applied to the kernel weights matrix.
  • recurrent_regularizer: Regularizer function applied to the recurrent_kernel weights matrix.
  • bias_regularizer: Regularizer function applied to the bias vector.
  • activity_regularizer: Regularizer function applied to the output of the layer (its "activation")..
  • kernel_constraint: Constraint function applied to the kernel weights matrix.
  • recurrent_constraint: Constraint function applied to the recurrent_kernel weights matrix.
  • bias_constraint: Constraint function applied to the bias vector.
  • dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
  • recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
  • implementation: Implementation mode, either 1 or 2. Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications.
  • return_sequences: Boolean. Whether to return the last output. in the output sequence, or the full sequence.
  • return_state: Boolean. Whether to return the last state in addition to the output.
  • go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence.
  • stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
  • unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.

__init__

__init__(
    units,
    activation='tanh',
    recurrent_activation='hard_sigmoid',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    recurrent_initializer='orthogonal',
    bias_initializer='zeros',
    unit_forget_bias=True,
    kernel_regularizer=None,
    recurrent_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    recurrent_constraint=None,
    bias_constraint=None,
    dropout=0.0,
    recurrent_dropout=0.0,
    implementation=1,
    return_sequences=False,
    return_state=False,
    go_backwards=False,
    stateful=False,
    unroll=False,
    **kwargs
)

Properties

activation

activity_regularizer

Optional regularizer function for the output of this layer.

bias_constraint

bias_initializer

bias_regularizer

dropout

dtype

implementation

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.

kernel_constraint

kernel_initializer

kernel_regularizer

losses

name

non_trainable_variables

non_trainable_weights

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.

recurrent_activation

recurrent_constraint

recurrent_dropout

recurrent_initializer

recurrent_regularizer

states

trainable_variables

trainable_weights

unit_forget_bias

units

updates

use_bias

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

__call__

__call__(
    inputs,
    initial_state=None,
    constants=None,
    **kwargs
)

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(
    instance,
    input_shape
)

compute_mask

compute_mask(
    inputs,
    mask
)

compute_output_shape

compute_output_shape(
    instance,
    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

@classmethod
from_config(
    cls,
    config
)

get_config

get_config()

get_initial_state

get_initial_state(inputs)

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.

reset_states

reset_states(states=None)

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.