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View source on GitHub
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Cell class for the LSTM layer.
Inherits From: Layer
tf.compat.v1.keras.layers.LSTMCell(
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, kernel_constraint=None,
recurrent_constraint=None, bias_constraint=None, dropout=0.0,
recurrent_dropout=0.0, implementation=1, **kwargs
)
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.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.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.get_dropout_mask_for_cellget_dropout_mask_for_cell(
inputs, training, count=1
)
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.
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.List of mask tensor, generated or cached mask based on context.
get_initial_stateget_initial_state(
inputs=None, batch_size=None, dtype=None
)
get_recurrent_dropout_mask_for_cellget_recurrent_dropout_mask_for_cell(
inputs, training, count=1
)
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.
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.List of mask tensor, generated or cached mask based on context.
reset_dropout_maskreset_dropout_mask()
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_maskreset_recurrent_dropout_mask()
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.