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 Positive integer, dimensionality of the output space. Initializer for the kernel weights matrix, used for the linear transformation of the inputs. Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state. Initializer for the bias vector. 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. Regularizer function applied to the kernel weights matrix. Regularizer function applied to the recurrent_kernel weights matrix. Regularizer function applied to the bias vector. Regularizer function applied to the output of the layer (its "activation").. Constraint function applied to the kernel weights matrix. Constraint function applied to the recurrent_kernel weights matrix. Constraint function applied to the bias vector. Boolean. Whether to return the last output in the output sequence, or the full sequence. Boolean (default FALSE). Whether to return the last state in addition to the output. 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. 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. 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. Fixed batch size for layer The data type expected by the input, as a string (float32, float64, int32...) 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. Whether the layer weights will be updated during training. Initial weights for layer.

## References

Other recurrent layers: layer_cudnn_gru(), layer_gru(), layer_lstm(), layer_simple_rnn()