It is similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.
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, return_state = 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. 
return_state  Boolean. Whether to return the last state in addition to the output. 
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()