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► Keras 3 API documentation / Layers API / Recurrent layers / LSTM cell layer

LSTM cell layer

[source]

LSTMCell class

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, seed=None, **kwargs )

Cell class for the LSTM layer.

This class processes one step within the whole time sequence input, whereas keras.layer.LSTM 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).
  • 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 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".
  • 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.
  • seed: Random seed for dropout.

Call arguments

  • inputs: 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)) >>> rnn = keras.layers.RNN(keras.layers.LSTMCell(4)) >>> output = rnn(inputs) >>> output.shape (32, 4) >>> rnn = keras.layers.RNN( ... keras.layers.LSTMCell(4), ... return_sequences=True, ... return_state=True) >>> whole_sequence_output, final_state = rnn(inputs) >>> whole_sequence_output.shape (32, 10, 4) >>> final_state.shape (32, 4)
LSTM cell layer
LSTMCell class