Deep Learning with PyTorch

Training the model

Training the model is the same process as for our previous dogs and cats image classification problems. The following code snippet does the training of our model on the provided dataset:

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 , (data,target) in enumerate(data_loader):        if is_cuda:            data,target = data.cuda(),target.cuda()        data , target = Variable(data,volatile),Variable(target)        if phase == 'training':            optimizer.zero_grad()        output = model(data)        loss = F.nll_loss(output,target)                running_loss += F.nll_loss(output,target,size_average=False).data[0] preds = output.data.max(dim=1,keepdim=True)[1] ...