tf.compat.v1.keras.layers.CuDNNGRU

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Fast GRU implementation backed by cuDNN.

tf.compat.v1.keras.layers.CuDNNGRU(
    units, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal',
    bias_initializer='zeros', 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.

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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get_initial_state(
    inputs
)

reset_states

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reset_states(
    states=None
)