Keras
Keras 3 API documentation
Layers API
Recurrent layers
Simple RNN cell layer

Simple RNN cell layer

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SimpleRNNCell class 

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
)

Cell class for SimpleRNN.

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

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, (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)