trw.layers.autoencoder_convolutional_variational_conditional

Module Contents

Classes

AutoencoderConvolutionalVariationalConditional

Conditional Variational convolutional auto-encoder implementation

class trw.layers.autoencoder_convolutional_variational_conditional.AutoencoderConvolutionalVariationalConditional(input_shape: Union[torch.Size, List[int], Tuple[int, Ellipsis]], encoder: torch.nn.Module, decoder: torch.nn.Module, z_size: int, y_size: int, input_type=torch.float32)

Bases: torch.nn.Module

Conditional Variational convolutional auto-encoder implementation

Most of the implementation is shared with regular variational convolutional auto-encoder.

The main difference if the auto-encoder is conditioned on a variable y. The model learns a latent given y. In this implementation, the encoder is not using y, only the decoder is aware of it. This is done by concatenating the latent variable calculated by the encoder and y.

encode(self, x)

decode(self, mu, logvar, y, sample_parameters=None)

sample_given_y(self, y)

forward(self, x, y)