tf.keras.layers.GRUCell

Cell class for the GRU layer.

Inherits From: Layer, Operation

tf.keras.layers.GRUCell( units, activation='tanh', recurrent_activation='sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', 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, reset_after=True, seed=None, **kwargs )

This class processes one step within the whole time sequence input, whereas keras.layer.GRU processes the whole sequence.

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, (default True), whether the layer should use a bias vector.
kernel_initializer Initializer for the kernel weights matrix, used for the linear transformation of the inputs. Default: "glorot_uniform".
recurrent_initializer Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state. Default: "orthogonal".
bias_initializer Initializer for the bias vector. Default: "zeros".
kernel_regularizer Regularizer function applied to the kernel weights matrix. Default: None.
recurrent_regularizer Regularizer function applied to the recurrent_kernel weights matrix. Default: None.
bias_regularizer Regularizer function applied to the bias vector. Default: None.
kernel_constraint Constraint function applied to the kernel weights matrix. Default: None.
recurrent_constraint Constraint function applied to the recurrent_kernel weights matrix. Default: None.
bias_constraint Constraint function applied to the bias vector. Default: None.
dropout Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0.
recurrent_dropout Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0.
reset_after GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before", True = "after" (default and cuDNN compatible).
seed Random seed for dropout.

inputs A 2D tensor, with shape (batch, features).
states A 2D tensor with shape (batch, units), which is the state from the previous time step.
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.

Example:

inputs = np.random.random((32, 10, 8)) rnn = keras.layers.RNN(keras.layers.GRUCell(4)) output = rnn(inputs) output.shape (32, 4) rnn = keras.layers.RNN(  keras.layers.GRUCell(4),  return_sequences=True,  return_state=True) whole_sequence_output, final_state = rnn(inputs) whole_sequence_output.shape (32, 10, 4) final_state.shape (32, 4)

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@classmethod from_config( config )

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config A Python dictionary, typically the output of get_config.

Returns
A layer instance.

get_dropout_mask

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get_dropout_mask( step_input )

get_initial_state

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get_initial_state( batch_size=None )

get_recurrent_dropout_mask

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get_recurrent_dropout_mask( step_input )

reset_dropout_mask

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reset_dropout_mask()

Reset the cached dropout mask if any.

The RNN layer invokes this in the call() method so that the cached mask is cleared after calling cell.call(). The mask should be cached across all timestep within the same batch, but shouldn't be cached between batches.

reset_recurrent_dropout_mask

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reset_recurrent_dropout_mask()

symbolic_call

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symbolic_call( *args, **kwargs )