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December 25, 2020 18:34
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PyTorch implementation of a vanilla autoencoder model.
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class AE(nn.Module): | |
def __init__(self, **kwargs): | |
super().__init__() | |
self.encoder_hidden_layer = nn.Linear( | |
in_features=kwargs["input_shape"], out_features=128 | |
) | |
self.encoder_output_layer = nn.Linear( | |
in_features=128, out_features=128 | |
) | |
self.decoder_hidden_layer = nn.Linear( | |
in_features=128, out_features=128 | |
) | |
self.decoder_output_layer = nn.Linear( | |
in_features=128, out_features=kwargs["input_shape"] | |
) | |
def forward(self, features): | |
activation = self.encoder_hidden_layer(features) | |
activation = torch.relu(activation) | |
code = self.encoder_output_layer(activation) | |
code = torch.relu(code) | |
activation = self.decoder_hidden_layer(code) | |
activation = torch.relu(activation) | |
activation = self.decoder_output_layer(activation) | |
reconstructed = torch.relu(activation) | |
return reconstructed |
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