Save the date! Google I/O returns May 18-20 Register now

tf.compat.v2.keras.layers.LSTMCell

View source on GitHub

Cell class for the LSTM layer.

Inherits From: LSTMCell

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: sigmoid (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.
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 (default) will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications.

Call arguments:

  • inputs: A 2D tensor.
  • states: List of state tensors corresponding to the previous timestep.
  • training: Python boolean indicating whether the layer should behave in training mode or in inference mode. Only relevant when dropout or recurrent_dropout is used.

Methods

get_dropout_mask_for_cell

View source

Get the dropout mask for RNN cell's input.

It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.

Args
inputs the input tensor whose shape will be used to generate dropout mask.
training boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.
count int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.

Returns
List of mask tensor, generated or cached mask based on context.

get_initial_state

View source

get_recurrent_dropout_mask_for_cell

View source

Get the recurrent dropout mask for RNN cell.

It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.

Args
inputs the input tensor whose shape will be used to generate dropout mask.
training boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.
count int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.

Returns
List of mask tensor, generated or cached mask based on context.

reset_dropout_mask

View source

Reset the cached dropout masks if any.

This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch.

reset_recurrent_dropout_mask

View source

Reset the cached recurrent dropout masks if any.

This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch.