Torch Model Training

quora_model_training.py

def train(nn_model, nn_optimizer, nn_criterion, data_loader, val_loader = None, num_epochs = 5, print_ratio = 0.1, verbose=True):
    for epoch in range(num_epochs):
        # Enable Training for the model
        nn_model.train()
        running_loss = 0;

        for ite, (x, y, l) in enumerate(data_loader):
            init_time = time.time();

            # Convert our tensors to GPU tensors
            x = x.cuda()
            y = y.cuda()

            # Clear gradients
            nn_optimizer.zero_grad()

            # Forward Propagation and compute predictions
            preds = nn_model.forward(x, l)

            # Compute loss against actual values
            loss = nn_criterion(preds, y)
            
            # Back Propagation and Updating weights
            loss.backward()
            nn_optimizer.step()

            running_loss = running_loss + loss.item();
        
        print('Epoch %d done in %.2f min'%(epoch+1, (time.time() - epoch_time)/60 ));        
        running_loss = 0.0;