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

ML/Pytorch/pytorch_lightning/2. LightningModule/simple_fc.py

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+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
+from torch.utils.data import random_split
+import pytorch_lightning as pl 
+
+class NN(nn.Module):
+    def __init__(self, input_size, num_classes):
+        super().__init__()
+        self.fc1 = nn.Linear(input_size, 50)
+        self.fc2 = nn.Linear(50, num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+class NN(pl.LightningModule):
+    def __init__(self, input_size, num_classes):
+        super().__init__()
+        self.fc1 = nn.Linear(input_size, 50)
+        self.fc2 = nn.Linear(50, num_classes)
+        self.loss_fn = nn.CrossEntropyLoss()
+
+    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)
+        self.log('train_loss', loss)
+        return loss
+    
+    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=0.001)
+
+# Set device cuda for GPU if it's available otherwise run on the CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Hyperparameters
+
+input_size = 784
+num_classes = 10
+learning_rate = 0.001
+batch_size = 64
+num_epochs = 3
+
+# Load Data
+entire_dataset = datasets.MNIST(
+    root="dataset/", train=True, transform=transforms.ToTensor(), download=True
+)
+train_ds, val_ds = random_split(entire_dataset, [50000, 10000])
+test_ds = datasets.MNIST(
+    root="dataset/", train=False, transform=transforms.ToTensor(), download=True
+)
+train_loader = DataLoader(dataset=train_ds, batch_size=batch_size, shuffle=True)
+val_loader = DataLoader(dataset=train_ds, batch_size=batch_size, shuffle=True)
+test_loader = DataLoader(dataset=test_ds, batch_size=batch_size, shuffle=False)
+
+# Initialize network
+model = NN(input_size=input_size, num_classes=num_classes).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+
+# Train Network
+for epoch in range(num_epochs):
+    for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):
+        # Get data to cuda if possible
+        data = data.to(device=device)
+        targets = targets.to(device=device)
+
+        # Get to correct shape
+        data = data.reshape(data.shape[0], -1)
+
+        # Forward
+        scores = model(data)
+        loss = criterion(scores, targets)
+
+        # Backward
+        optimizer.zero_grad()
+        loss.backward()
+
+        # Gradient descent or adam step
+        optimizer.step()
+
+
+# Check accuracy on training & test to see how good our model
+def check_accuracy(loader, model):
+    num_correct = 0
+    num_samples = 0
+    model.eval()
+
+    # We don't need to keep track of gradients here so we wrap it in torch.no_grad()
+    with torch.no_grad():
+        # Loop through the data
+        for x, y in loader:
+
+            # Move data to device
+            x = x.to(device=device)
+            y = y.to(device=device)
+
+            # Get to correct shape
+            x = x.reshape(x.shape[0], -1)
+
+            # Forward pass
+            scores = model(x)
+            _, predictions = scores.max(1)
+
+            # Check how many we got correct
+            num_correct += (predictions == y).sum()
+
+            # Keep track of number of samples
+            num_samples += predictions.size(0)
+
+    model.train()
+    return num_correct / num_samples
+
+
+# Check accuracy on training & test to see how good our model
+model.to(device)
+print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:.2f}")
+print(f"Accuracy on validation set: {check_accuracy(val_loader, model)*100:.2f}")
+print(f"Accuracy on test set: {check_accuracy(test_loader, model)*100:.2f}")