tf.keras.layers.SimpleRNN

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Fully-connected RNN where the output is to be fed back to input.

Inherits From: RNN

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.SimpleRNN, `tf.compat.v2.keras.layers.SimpleRNN`

tf.keras.layers.SimpleRNN(
    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,
    activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None,
    bias_constraint=None, dropout=0.0, recurrent_dropout=0.0,
    return_sequences=False, return_state=False, go_backwards=False, stateful=False,
    unroll=False, **kwargs
)

Arguments
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, whether the layer uses a bias vector.
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.
dropout	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
recurrent_dropout	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
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.
unroll	Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.

Call arguments:

• inputs: A 3D tensor.
• mask: Binary tensor of shape (samples, timesteps) indicating whether a given timestep should be masked.
• training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if dropout or recurrent_dropout is used.
• initial_state: List of initial state tensors to be passed to the first call of the cell.

Attributes
activation
bias_constraint
bias_initializer
bias_regularizer
dropout
kernel_constraint
kernel_initializer
kernel_regularizer
recurrent_constraint
recurrent_dropout
recurrent_initializer
recurrent_regularizer
states
units
use_bias

Methods

get_initial_state

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get_initial_state(
    inputs
)

reset_states

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reset_states(
    states=None
)