ml4gw.nn.autoencoder.convolutional

Classes

ConvBlock(in_channels, encode_channels, ...)

ConvolutionalAutoencoder(in_channels, ...[, ...])

Build a stack of convolutional autoencoder layer blocks.
class ml4gw.nn.autoencoder.convolutional.ConvBlock(in_channels, encode_channels, kernel_size, stride=1, groups=1, activation=<class 'torch.nn.modules.activation.ReLU'>, norm=<class 'torch.nn.modules.batchnorm.BatchNorm1d'>, decode_channels=None, output_activation=None, skip_connection=None)

Bases: Autoencoder

Parameters:

  • in_channels (int)

  • encode_channels (int)

  • kernel_size (int)

  • stride (int)

  • groups (int)

  • activation (Module)

  • norm (Callable[[...], Module])

  • decode_channels (int | None)

  • output_activation (Module | None)

  • skip_connection (SkipConnection | None)

decode(X)
Return type:

Tensor

Parameters:

X (Tensor)

encode(X)
Return type:

Tensor

Parameters:

X (Tensor)

class ml4gw.nn.autoencoder.convolutional.ConvolutionalAutoencoder(in_channels, encode_channels, kernel_size, stride=1, groups=1, activation=<class 'torch.nn.modules.activation.ReLU'>, output_activation=None, norm=<class 'torch.nn.modules.batchnorm.BatchNorm1d'>, decode_channels=None, skip_connection=None)

Bases: Autoencoder

Build a stack of convolutional autoencoder layer blocks. The output of each decoder layer will match the shape of the input to its corresponding encoder layer, except for the last decoder which can have an arbitrary number of channels specified by decode_channels.

All layers also share the same activation except for the last decoder layer, which can have an arbitrary output_activation.

Parameters:

  • in_channels (int)

  • encode_channels (Sequence[int])

  • kernel_size (int)

  • stride (int)

  • groups (int)

  • activation (Module)

  • output_activation (Module | None)

  • norm (Callable[[...], Module])

  • decode_channels (int | None)

  • skip_connection (SkipConnection | None)

decode(*X, states=None, input_size=None)
Return type:

Tensor

Parameters:

input_size (int | None)

forward(X)
Return type:

Tensor

Parameters:

X (Tensor)