tf.keras.layers.SimpleRNNCell

TensorFlow 1 version View source on GitHub

Cell class for SimpleRNN.

Inherits From: Layer

tf.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, **kwargs
)

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)

Methods

get_dropout_mask_for_cell

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get_dropout_mask_for_cell(
    inputs, training, count=1
)

Get the dropout mask for RNN cell's input.

It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.

Args:

  • inputs: The input tensor whose shape will be used to generate dropout mask.
  • training: Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.
  • count: Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.

Returns:

List of mask tensor, generated or cached mask based on context.

get_initial_state

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get_initial_state(
    inputs=None, batch_size=None, dtype=None
)

get_recurrent_dropout_mask_for_cell

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get_recurrent_dropout_mask_for_cell(
    inputs, training, count=1
)

Get the recurrent dropout mask for RNN cell.

It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.

Args:

  • inputs: The input tensor whose shape will be used to generate dropout mask.
  • training: Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.
  • count: Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.

Returns:

List of mask tensor, generated or cached mask based on context.

reset_dropout_mask

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reset_dropout_mask()

Reset the cached dropout masks if any.

This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch.

reset_recurrent_dropout_mask

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reset_recurrent_dropout_mask()

Reset the cached recurrent dropout masks if any.

This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch.