2D Convolutional LSTM layer.

Inherits From: RNN, Layer, Module

A convolutional LSTM is similar to an LSTM, but the input transformations and recurrent transformations are both convolutional. This layer is typically used to process timeseries of images (i.e. video-like data).

It is known to perform well for weather data forecasting, using inputs that are timeseries of 2D grids of sensor values. It isn't usually applied to regular video data, due to its high computational cost.

filters Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
kernel_size An integer or tuple/list of n integers, specifying the dimensions of the convolution window.
strides An integer or tuple/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). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
data_format A string, one of channels_last (default) or