Description Usage Arguments References See Also
View source: R/layersrecurrent.R
Can only be run on GPU, with the TensorFlow backend.
1 2 3 4 5 6 7 8 9  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 
Model or layer object 
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_simple_rnn
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.