tf.keras.layers.LSTMCell

TensorFlow 1 version View source on GitHub

Cell class for the LSTM layer.

Inherits From: LSTMCell

tf.keras.layers.LSTMCell(
    units, activation='tanh', recurrent_activation='sigmoid', use_bias=True,
    kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal',
    bias_initializer='zeros', unit_forget_bias=True, 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, implementation=2, **kwargs
)

Used in the notebooks

Used in the guide

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.LSTM processes the whole sequence.

For example:

inputs = tf.random.normal([32, 10, 8]) 
rnn = tf.keras.layers.RNN(tf.keras.layers.LSTMCell(4)) 
output = rnn(inputs) 
print(output.shape) 
(32, 4) 
rnn = tf.keras.layers.RNN( 
   tf.keras.layers.LSTMCell(4), 
   return_sequences=True, 
   return_state=True) 
whole_seq_output, final_memory_state, final_carry_state = rnn(inputs) 
print(whole_seq_output.shape) 
(32, 10, 4) 
print(final_memory_state.shape) 
(32, 4) 
print(final_carry_state.shape) 
(32, 4) 

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).
  • recurrent_activation: Activation function to use for the recurrent step. Default: sigmoid (sigmoid). 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.
  • unit_forget_bias: Boolean (default True). If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force bias_initializer="zeros". This is recommended in Jozefowicz et al.
  • 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.
  • implementation: Implementation mode, either 1 or 2. Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 (default) will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications. Default: 2.

Call arguments:

  • inputs: A 2D tensor, with shape of [batch, feature].
  • states: List of 2 tensors that corresponding to the cell's units. Both of them have shape [batch, units], the first tensor is the memory state from previous time step, the second tensor is the carry state from 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.

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.