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tf.keras.layers.CuDNNGRU

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Fast GRU implementation backed by cuDNN.

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

units Positive integer, dimensionality of the output space.
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.
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.

cell

states

Methods

get_initial_state

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reset_states

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