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tf.keras.layers.ConvLSTM2D

2D Convolutional LSTM.

Inherits From: RNN, Layer, Module

Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.

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 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 tuple/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. By default hyperbolic tangent activation function is applied (tanh(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., 2015
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.
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.