# Convolutional LSTM.

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),
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
)

## Arguments

 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 dilation_rate value != 1. padding One of "valid" or "same" (case-insensitive). data_format A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, time, ..., channels) while channels_first corresponds to inputs with shape (batch, time, channels, ...). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last". dilation_rate An integer or list of n integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides value != 1. activation Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x). 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 kernel weights matrix, used for the linear transformation of the inputs.. recurrent_initializer Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state.. 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 bias_initializer="zeros". This is recommended in Jozefowicz et al. kernel_regularizer Regularizer function applied to the kernel weights matrix. recurrent_regularizer Regularizer function applied to the recurrent_kernel weights matrix. 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 kernel weights matrix. recurrent_constraint Constraint function applied to the recurrent_kernel weights matrix. 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.

## Input shape

• 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)

## References

Other convolutional layers: 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()