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 )
object  What to call the new 

units  Positive integer, dimensionality of the output space. 
kernel_initializer  Initializer for the 
recurrent_initializer  Initializer for the 
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

kernel_regularizer  Regularizer function applied to the 
recurrent_regularizer  Regularizer function applied to the

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 
recurrent_constraint  Constraint function applied to the

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_size  Fixed batch size for layer 
dtype  The data type expected by the input, as a string ( 
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. 
Long shortterm memory (original 1997 paper)
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Other recurrent layers:
layer_cudnn_gru()
,
layer_gru()
,
layer_lstm()
,
layer_rnn()
,
layer_simple_rnn()