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Fully-connected RNN where the output is to be fed back to input.
Inherits From: RNN
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. |
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 |
get_initial_state
get_initial_state( inputs )
reset_states
reset_states( states=None )
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/layers/SimpleRNN