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.

Args
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).
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.
seed	Random seed for dropout.

Call arguments
sequence	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]).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)

Attributes
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

View source

@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

View source

get_dropout_mask(
    step_input
)

get_initial_state

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get_initial_state(
    batch_size=None
)

get_recurrent_dropout_mask

View source

get_recurrent_dropout_mask(
    step_input
)

reset_dropout_mask

View source

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

View source

reset_recurrent_dropout_mask()

symbolic_call

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symbolic_call(
    *args, **kwargs
)