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@ -150,6 +150,10 @@ class LitAutoEncoder(pl.LightningModule):
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def configure_optimizers(self):
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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return optimizer
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return optimizer
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# def forward(self, x):
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# in lightning this is optional and mostly used to say
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# how your LightningModule should work for inference/predictions
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```
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```
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#### Step 2: Train!
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#### Step 2: Train!
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@ -111,6 +111,10 @@ Step 1: Define LightningModule
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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return optimizer
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return optimizer
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# def forward(self, x):
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# in lightning this is optional and mostly used to say
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# how your LightningModule should work for inference/predictions
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A :class:`~pytorch_lightning.core.LightningModule` defines a *system* such as:
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A :class:`~pytorch_lightning.core.LightningModule` defines a *system* such as:
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- `Autoencoder <https://github.com/PyTorchLightning/pytorch-lightning-bolts/blob/master/pl_bolts/models/autoencoders/basic_ae/basic_ae_module.py>`_
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- `Autoencoder <https://github.com/PyTorchLightning/pytorch-lightning-bolts/blob/master/pl_bolts/models/autoencoders/basic_ae/basic_ae_module.py>`_
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