Slayer V740 By Bokundev High Quality | Training

# Load dataset and create data loader dataset = MyDataset(data, labels) data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

Slayer V7.4.0 Developer: Bokundev Task: Training a high-quality model training slayer v740 by bokundev high quality

model.eval() eval_loss = 0 correct = 0 with torch.no_grad(): for batch in data_loader: data = batch['data'].to(device) labels = batch['label'].to(device) outputs = model(data) loss = criterion(outputs, labels) eval_loss += loss.item() _, predicted = torch.max(outputs, dim=1) correct += (predicted == labels).sum().item() # Load dataset and create data loader dataset

# Train the model for epoch in range(epochs): model.train() total_loss = 0 for batch in data_loader: data = batch['data'].to(device) labels = batch['label'].to(device) optimizer.zero_grad() outputs = model(data) loss = criterion(outputs, labels) loss.backward() optimizer.step() total_loss += loss.item() print(f'Epoch {epoch+1}, Loss: {total_loss / len(data_loader)}') labels) data_loader = DataLoader(dataset

def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x