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()

def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x training slayer v740 by bokundev high quality

# Define a custom dataset class class MyDataset(Dataset): def __init__(self, data, labels): self.data = data self.labels = labels labels) eval_loss += loss.item() _