View source: R/layersconvolutional.R
layer_conv_lstm_1d  R Documentation 
1D Convolutional LSTM
layer_conv_lstm_1d(
object,
filters,
kernel_size,
strides = 1L,
padding = "valid",
data_format = NULL,
dilation_rate = 1L,
activation = "tanh",
recurrent_activation = "hard_sigmoid",
use_bias = TRUE,
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,
go_backwards = FALSE,
stateful = FALSE,
dropout = 0,
recurrent_dropout = 0,
...
)
object 
What to compose the new

filters 
Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). 
kernel_size 
An integer or list of n integers, specifying the dimensions of the convolution window. 
strides 
An integer or list of n integers, specifying the strides of
the convolution. Specifying any stride value != 1 is incompatible with
specifying any 
padding 
One of 
data_format 
A string, one of 
dilation_rate 
An integer or list of n integers, specifying the
dilation rate to use for dilated convolution. Currently, specifying any

activation 
Activation function to use. By default hyperbolic tangent
activation function is applied ( 
recurrent_activation 
Activation function to use for the recurrent step. 
use_bias 
Boolean, whether the layer uses a bias vector. 
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. Use in combination with 
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. 
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. (default FALSE) 
return_state 
Boolean Whether to return the last state in addition to the output. (default FALSE) 
go_backwards 
Boolean (default FALSE). If TRUE, process the input sequence backwards. 
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. 
dropout 
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. 
recurrent_dropout 
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. 
... 
standard layer arguments. 
Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.
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