Deep Learning with PyTorch

Training and validating the model

We need to make some minor changes to the fit method to accommodate the three input-values generated from the data loader. The following code implements the new fit function:

def fit(epoch,model,data_loader,phase='training',volatile=False):    if phase == 'training':        model.train()    if phase == 'validation':        model.eval()        volatile=True    running_loss = 0.0    running_correct = 0    for batch_idx , (data1,data2,data3,target) in enumerate(data_loader):        if is_cuda:            data1,data2,data3,target = data1.cuda(),data2.cuda(),data3.cuda(),target.cuda()        data1,data2,data3,target = Variable(data1,volatile),Variable(data2,volatile),Variable(data3,volatile),Variable(target)        if phase == 'training':            optimizer.zero_grad() output = model(data1,data2,data3) ...