tf.keras.layers.LSTM

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Class LSTM

Long Short-Term Memory layer - Hochreiter 1997.

Inherits From: RNN

Aliases:

Note that this cell is not optimized for performance on GPU. Please use tf.compat.v1.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.
  • time_major: The shape format of the inputs and outputs tensors. If True, the inputs and outputs will be in shape (timesteps, batch, ...), whereas in the False case, it will be (batch, timesteps, ...). Using time_major = True is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form.

Call arguments:

  • inputs: A 3D tensor.
  • mask: Binary tensor of shape (samples, timesteps) indicating whether a given timestep should be masked.
  • training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if dropout or recurrent_dropout is used.
  • initial_state: List of initial state tensors to be passed to the first call of the cell.

__init__

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__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

bias_constraint

bias_initializer

bias_regularizer

dropout

implementation

kernel_constraint

kernel_initializer

kernel_regularizer

recurrent_activation

recurrent_constraint

recurrent_dropout

recurrent_initializer

recurrent_regularizer

states

unit_forget_bias

units

use_bias

Methods

tf.keras.layers.LSTM.get_initial_state

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get_initial_state(inputs)

tf.keras.layers.LSTM.reset_states

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reset_states(states=None)