In this guide, we will discuss Introduction to Convents in PyTorch. Convents is all about building the CNN model from scratch. The network architecture will contain a combination of following steps −
- Conv2d
- MaxPool2d
- Rectified Linear Unit
- View
- Linear Layer
Training the Model
Training the model is the same process like image classification problems. The following code snippet completes the procedure of a training model on the provided dataset −
def fit(epoch,model,data_loader,phase
= 'training',volatile = False):
if phase == 'training':
model.train()
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]
running_correct + =
preds.eq(target.data.view_as(preds)).cpu().sum()
if phase == 'training':
loss.backward()
optimizer.step()
loss = running_loss/len(data_loader.dataset)
accuracy = 100. * running_correct/len(data_loader.dataset)
print(f'{phase} loss is {loss:{5}.{2}} and {phase} accuracy is {running_correct}/{len(data_loader.dataset)}{accuracy:{return loss,accuracy}})
The method includes different logic for training and validation. There are two primary reasons for using different modes −
- In train mode, dropout removes a percentage of values, which should not happen in the validation or testing phase.
- For training mode, we calculate gradients and change the model’s parameters value, but back propagation is not required during the testing or validation phases.
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