Fast LSTM implementation backed by CuDNN.

Can only be run on GPU, with the TensorFlow backend.

layer_cudnn_lstm(object, units, kernel_initializer = "glorot_uniform",
  recurrent_initializer = "orthogonal", bias_initializer = "zeros",
  unit_forget_bias = TRUE, kernel_regularizer = NULL,
  recurrent_regularizer = NULL, bias_regularizer = NULL,
  activity_regularizer = NULL, kernel_constraint = NULL,
  recurrent_constraint = NULL, bias_constraint = NULL,
  return_sequences = FALSE, return_state = FALSE, stateful = FALSE,
  input_shape = NULL, batch_input_shape = NULL, batch_size = NULL,
  dtype = NULL, name = NULL, trainable = NULL, weights = NULL)

Arguments

object	Model or layer object
units	Positive integer, dimensionality of the output space.
kernel_initializer	Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
recurrent_initializer	Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state.
bias_initializer	Initializer for the bias vector.
unit_forget_bias	Boolean. 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.
recurrent_regularizer	Regularizer function applied to the recurrent_kernel weights matrix.
bias_regularizer	Regularizer function applied to the bias vector.
activity_regularizer	Regularizer function applied to the output of the layer (its "activation")..
kernel_constraint	Constraint function applied to the kernel weights matrix.
recurrent_constraint	Constraint function applied to the recurrent_kernel weights matrix.
bias_constraint	Constraint function applied to the bias vector.
return_sequences	Boolean. Whether to return the last output in the output sequence, or the full sequence.
return_state	Boolean (default FALSE). Whether to return the last state in addition to the output.
stateful	Boolean (default FALSE). If TRUE, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
input_shape	Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.
batch_input_shape	Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.
batch_size	Fixed batch size for layer
dtype	The data type expected by the input, as a string (float32, float64, int32...)
name	An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
trainable	Whether the layer weights will be updated during training.
weights	Initial weights for layer.

References

• Long short-term memory (original 1997 paper)

• Supervised sequence labeling with recurrent neural networks

• A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

See also

Other recurrent layers: layer_cudnn_gru, layer_gru, layer_lstm, layer_simple_rnn