Keras 3 API documentation
Models API Layers API The base Layer class Layer activations Layer weight initializers Layer weight regularizers Layer weight constraints Core layers Convolution layers Pooling layers Recurrent layers Preprocessing layers Normalization layers Regularization layers Attention layers Reshaping layers Merging layers Activation layers Backend-specific layers Callbacks API Ops API Optimizers Metrics Losses Data loading Built-in small datasets Keras Applications Mixed precision Multi-device distribution RNG API Rematerialization Utilities
Keras 2 API documentation
► Keras 3 API documentation / Layers API / Recurrent layers / Simple RNN cell layer

Simple RNN cell layer

[source]

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)
Simple RNN cell layer
SimpleRNNCell class