tf.keras.layers.SimpleRNNCell

Cell class for SimpleRNN.

Inherits From: Layer, Operation

tf.keras.layers.SimpleRNNCell(
    units,
    activation='tanh',
    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,
    seed=None,
    **kwargs
)

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

unitsPositive integer, dimensionality of the output space.
activationActivation function to use. Default: hyperbolic tangent (tanh). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).
use_biasBoolean, (default True), whether the layer should use a bias vector.
kernel_initializerInitializer for the kernel weights matrix, used for the linear transformation of the inputs. Default: "glorot_uniform".
recurrent_initializerInitializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state. Default: "orthogonal".
bias_initializerInitializer for the bias vector. Default: "zeros".
kernel_regularizerRegularizer function applied to the kernel weights matrix. Default: None.
recurrent_regularizerRegularizer function applied to the recurrent_kernel weights matrix. Default: None.
bias_regularizerRegularizer function applied to the bias vector. Default: None.
kernel_constraintConstraint function applied to the kernel weights matrix. Default: None.
recurrent_constraintConstraint function applied to the recurrent_kernel weights matrix. Default: None.
bias_constraintConstraint function applied to the bias vector. Default: None.
dropoutFloat between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0.
recurrent_dropoutFloat between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0.
seedRandom seed for dropout.

sequenceA 2D tensor, with shape (batch, features).
statesA 2D tensor with shape (batch, units), which is the state from the previous time step.
trainingPython 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]).astype(np.float32)
rnn = keras.layers.RNN(keras.layers.SimpleRNNCell(4))
output = rnn(inputs)  # The output has shape `(32, 4)`.
rnn = keras.layers.RNN(
    keras.layers.SimpleRNNCell(4),
    return_sequences=True,
    return_state=True
)
# whole_sequence_output has shape `(32, 10, 4)`.
# final_state has shape `(32, 4)`.
whole_sequence_output, final_state = rnn(inputs)

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

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

outputRetrieves 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
configA 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
)