45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
from torch.optim import Adam
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from .modules import *
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from torch.nn.functional import mse_loss
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from torch import Tensor
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#######################
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# Basic AE-Implementation
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class AutoEncoder(AbstractNeuralNetwork, ABC):
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def __init__(self, latent_dim: int=0, features: int = 0, use_norm=True,
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train_on_predictions=False, **kwargs):
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assert latent_dim and features
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super(AutoEncoder, self).__init__()
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self.train_on_predictions = train_on_predictions
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self.latent_dim = latent_dim
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self.features = features
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self.encoder = Encoder(self.latent_dim, use_norm=use_norm)
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self.decoder = Decoder(self.latent_dim, self.features, use_norm=use_norm)
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def forward(self, batch: Tensor):
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# Encoder
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# outputs, hidden (Batch, Timesteps aka. Size, Features / Latent Dim Size)
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z = self.encoder(batch)
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# Decoder
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# First repeat the data accordingly to the batch size
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z_repeatet = Repeater((batch.shape[0], batch.shape[1], -1))(z)
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x_hat = self.decoder(z_repeatet)
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return z, x_hat
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def training_step(self, batch, batch_nb):
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x, y = batch
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# z, x_hat
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_, x_hat = self.forward(x)
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loss = mse_loss(y, x_hat) if self.train_on_predictions else mse_loss(x, x_hat)
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return {'loss': loss}
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def configure_optimizers(self):
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return [Adam(self.parameters(), lr=0.02)]
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if __name__ == '__main__':
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raise PermissionError('Get out of here - never run this module')
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