add lightning code, finetuning whisper, recommender system neural collaborative filtering

ML/Pytorch/pytorch_lightning/7. Callbacks/model.py

@@ -0,0 +1,71 @@
+import torch
+import torch.nn.functional as F
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+from torch import nn, optim
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+import pytorch_lightning as pl
+import torchmetrics
+from torchmetrics import Metric
+
+
+class NN(pl.LightningModule):
+    def __init__(self, input_size, learning_rate, num_classes):
+        super().__init__()
+        self.lr = learning_rate
+        self.fc1 = nn.Linear(input_size, 50)
+        self.fc2 = nn.Linear(50, num_classes)
+        self.loss_fn = nn.CrossEntropyLoss()
+        self.accuracy = torchmetrics.Accuracy(
+            task="multiclass", num_classes=num_classes
+        )
+        self.f1_score = torchmetrics.F1Score(task="multiclass", num_classes=num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+    def training_step(self, batch, batch_idx):
+        loss, scores, y = self._common_step(batch, batch_idx)
+        accuracy = self.accuracy(scores, y)
+        f1_score = self.f1_score(scores, y)
+        self.log_dict(
+            {
+                "train_loss": loss,
+                "train_accuracy": accuracy,
+                "train_f1_score": f1_score,
+            },
+            on_step=False,
+            on_epoch=True,
+            prog_bar=True,
+        )
+        return {"loss": loss, "scores": scores, "y": y}
+
+    def validation_step(self, batch, batch_idx):
+        loss, scores, y = self._common_step(batch, batch_idx)
+        self.log("val_loss", loss)
+        return loss
+
+    def test_step(self, batch, batch_idx):
+        loss, scores, y = self._common_step(batch, batch_idx)
+        self.log("test_loss", loss)
+        return loss
+
+    def _common_step(self, batch, batch_idx):
+        x, y = batch
+        x = x.reshape(x.size(0), -1)
+        scores = self.forward(x)
+        loss = self.loss_fn(scores, y)
+        return loss, scores, y
+
+    def predict_step(self, batch, batch_idx):
+        x, y = batch
+        x = x.reshape(x.size(0), -1)
+        scores = self.forward(x)
+        preds = torch.argmax(scores, dim=1)
+        return preds
+
+    def configure_optimizers(self):
+        return optim.Adam(self.parameters(), lr=self.lr)