Merken Sie den Termin vor! Google I / O kehrt vom 18. bis 20. Mai zurück Registrieren Sie sich jetzt


View source on GitHub

Convolutional LSTM recurrent network cell.

Inherits From: RNNCell

conv_ndims Convolution dimensionality (1, 2 or 3).
input_shape Shape of the input as int tuple, excluding the batch size.
output_channels int, number of output channels of the conv LSTM.
kernel_shape Shape of kernel as an int tuple (of size 1, 2 or 3).
use_bias (bool) Use bias in convolutions.
skip_connection If set to True, concatenate the input to the output of the conv LSTM. Default: False.
forget_bias Forget bias.
initializers Unused.
name Name of the module.

ValueError If skip_connection is True and stride is different from 1 or if input_shape is incompatible with conv_ndims.


output_size Integer or TensorShape: size of outputs produced by this cell.

state_size size(s) of state(s) used by this cell.

It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.



View source


View source

Return zero-filled state tensor(s).

batch_size int, float, or unit Tensor representing the batch size.
dtype the data type to use for the state.

If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size, state_size] filled with zeros.

If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [batch_size, s] for each s in state_size.