tf.compat.v1.keras.layers.CuDNNLSTM

Fast LSTM implementation backed by cuDNN.

Inherits From: RNN, Layer, Module

tf.compat.v1.keras.layers.CuDNNLSTM(
    units, 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, return_sequences=False, return_state=False,
    go_backwards=False, stateful=False, **kwargs
)

More information about cuDNN can be found on the NVIDIA developer website. Can only be run on GPU.

Arguments
units	Positive integer, dimensionality of the output space.
kernel_initializer	Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
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.
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.
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.
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.

Attributes
cell
states

Methods

reset_states

View source

reset_states(
    states=None
)

Reset the recorded states for the stateful RNN layer.

Can only be used when RNN layer is constructed with stateful = True. Args: states: Numpy arrays that contains the value for the initial state, which will be feed to cell at the first time step. When the value is None, zero filled numpy array will be created based on the cell state size.

Raises
AttributeError	When the RNN layer is not stateful.
ValueError	When the batch size of the RNN layer is unknown.
ValueError	When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise.