tf.keras.layers.SimpleRNNCell

TensorFlow 1 version View source on GitHub

Class SimpleRNNCell

Cell class for SimpleRNN.

Inherits From: Layer

See the Keras RNN API guide for details about the usage of RNN API.

This class processes one step within the whole time sequence input, whereas tf.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 uses 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.

Call arguments:

  • inputs: A 2D tensor, with shape of [batch, feature].
  • states: A 2D tensor with shape of [batch, units], which is the state from the previous time step. For timestep 0, the initial state provided by user will be feed to cell.
  • 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.

Examples:

inputs = np.random.random([32, 10, 8]).astype(np.float32)
rnn = tf.keras.layers.RNN(tf.keras.layers.SimpleRNNCell(4))

output = rnn(inputs)  # The output has shape `[32, 4]`.

rnn = tf.keras.layers.RNN(
    tf.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)

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