Description Usage Arguments Input shape References See Also
View source: R/layersconvolutional.R
It is similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  layer_conv_lstm_2d(
object,
filters,
kernel_size,
strides = c(1L, 1L),
padding = "valid",
data_format = NULL,
dilation_rate = c(1L, 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,
go_backwards = FALSE,
stateful = FALSE,
dropout = 0,
recurrent_dropout = 0,
batch_size = NULL,
name = NULL,
trainable = NULL,
weights = NULL,
input_shape = NULL
)

object 
Model or layer object 
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. If you don't specify anything,
no activation is applied (ie. "linear" activation: 
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 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. 
go_backwards 
Boolean (default FALSE). If TRUE, rocess 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. 
batch_size 
Fixed batch size for layer 
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. 
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. 
if data_format='channels_first' 5D tensor with shape:
(samples,time, channels, rows, cols)
if data_format='channels_last' 5D
tensor with shape: (samples,time, rows, cols, channels)
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting The current implementation does not include the feedback loop on the cells output
Other convolutional layers:
layer_conv_1d_transpose()
,
layer_conv_1d()
,
layer_conv_2d_transpose()
,
layer_conv_2d()
,
layer_conv_3d_transpose()
,
layer_conv_3d()
,
layer_cropping_1d()
,
layer_cropping_2d()
,
layer_cropping_3d()
,
layer_depthwise_conv_2d()
,
layer_separable_conv_1d()
,
layer_separable_conv_2d()
,
layer_upsampling_1d()
,
layer_upsampling_2d()
,
layer_upsampling_3d()
,
layer_zero_padding_1d()
,
layer_zero_padding_2d()
,
layer_zero_padding_3d()
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