43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
from networks.basic_vae import BasicVAE, VAELightningOverrides
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from networks.modules import LightningModule
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import pytorch_lightning as pl
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from torch.nn.functional import mse_loss
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from torch.optim import Adam
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import torch
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from torch.nn import BatchNorm1d
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from torch.utils.data import DataLoader
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from dataset import DataContainer
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from pytorch_lightning import Trainer
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class AEModel(VAELightningOverrides, LightningModule):
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def __init__(self, dataParams: dict):
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super(AEModel, self).__init__()
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self.dataParams = dataParams
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# noinspection PyUnresolvedReferences
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self.network = BasicVAE(self.dataParams)
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def forward(self, x):
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return self.network.forward(x)
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def configure_optimizers(self):
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# ToDo: Where do i get the Paramers from?
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return [Adam(self.parameters(), lr=0.02)]
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@pl.data_loader
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def tng_dataloader(self):
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return DataLoader(DataContainer('data', **self.dataParams), shuffle=True, batch_size=100)
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if __name__ == '__main__':
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features = 6
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ae = AEModel(
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dataParams=dict(refresh=False, size=5, step=5, features=features, transforms=[BatchNorm1d(features)])
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)
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trainer = Trainer()
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trainer.fit(ae)
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