feat(Pytorch): added custom model to pytorch-forecasting (#8)

* feat: Refractored and created new model. Pipeline not ready yet.

* feat: Implemented and refactored a data pipeline.

* ref: Refractored to make more sense.

* feat: Training works now with models that you can change.

* feat: Added predict function but without working instructions.

* feat: gitignore.

archive/model_lightning.py

@@ -0,0 +1,60 @@
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.utils.data import DataLoader, random_split
+import pytorch_lightning as pl
+
+
+class LitManualAutoEncoder(pl.LightningModule):
+
+    def __init__(self):
+        super().__init__()
+        self.encoder = nn.Sequential(nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128, 3))
+        self.decoder = nn.Sequential(nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28))
+        print("success")
+
+    def training_step(self, batch, batch_idx):
+        # --------------------------
+        # REPLACE WITH YOUR OWN
+        opt_a = self.optimizers()
+        
+        x, y = batch
+        x = x.view(x.size(0), -1)
+        z = self.encoder(x)
+        x_hat = self.decoder(z)
+        loss = F.mse_loss(x_hat, x)
+
+        # backward acts like normal backward
+        self.manual_backward(loss, opt_a, retain_graph=True)
+        self.manual_backward(loss, opt_a)
+        opt_a.step()
+        opt_a.zero_grad()
+
+        # --------------------------
+
+    def validation_step(self, batch, batch_idx):
+        # --------------------------
+        # REPLACE WITH YOUR OWN
+        x, y = batch
+        x = x.view(x.size(0), -1)
+        z = self.encoder(x)
+        x_hat = self.decoder(z)
+        loss = F.mse_loss(x_hat, x)
+        self.log('val_loss', loss)
+        # --------------------------
+
+    def test_step(self, batch, batch_idx):
+        # --------------------------
+        # REPLACE WITH YOUR OWN
+        x, y = batch
+        x = x.view(x.size(0), -1)
+        z = self.encoder(x)
+        x_hat = self.decoder(z)
+        loss = F.mse_loss(x_hat, x)
+        self.log('test_loss', loss)
+        # --------------------------
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
+        return optimizer