calculate accuracy for each class using CNN and pytorch
I Can calculate accuracy after each epoch using this code . But, I want to calculate the accuracy for each class at the end . how can i do that?
I have two folders train and val . each folder has 7 folders of 7 different classes. the train folder is used for training .otherwise val folder is used for testing
  def train_model(model, criterion, optimizer, lr_scheduler, num_epochs=25):
    since = time.time()

    best_model = model
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                mode='train'
                optimizer = lr_scheduler(optimizer, epoch)
                model.train()  # Set model to training mode
            else:
                model.eval()
                mode='val'

            running_loss = 0.0
            running_corrects = 0

            counter=0
            # Iterate over data.
            for data in dset_loaders[phase]:
                inputs, labels = data
                print(inputs.size())
                # wrap them in Variable
                if use_gpu:
                    try:
                        inputs, labels = Variable(inputs.float().cuda()),                             
                        Variable(labels.long().cuda())
                    except:
                        print(inputs,labels)
                else:
                    inputs, labels = Variable(inputs), Variable(labels)

                # Set gradient to zero to delete history of computations in previous epoch. Track operations so that differentiation can be done automatically.
                optimizer.zero_grad()
                outputs = model(inputs)
                _, preds = torch.max(outputs.data, 1)
                
                loss = criterion(outputs, labels)
                # print('loss done')                
                # Just so that you can keep track that something's happening and don't feel like the program isn't running.
                # if counter%10==0:
                #     print(&quot;Reached iteration &quot;,counter)
                counter+=1

                # backward + optimize only if in training phase
                if phase == 'train':
                    # print('loss backward')
                    loss.backward()
                    # print('done loss backward')
                    optimizer.step()
                    # print('done optim')
                # print evaluation statistics
                try:
                    # running_loss += loss.data[0]
                    running_loss += loss.item()
                    # print(labels.data)
                    # print(preds)
                    running_corrects += torch.sum(preds == labels.data)
                    # print('running correct =',running_corrects)
                except:
                    print('unexpected error, could not calculate loss or do a sum.')
            print('trying epoch loss')
            epoch_loss = running_loss / dset_sizes[phase]
            epoch_acc = running_corrects.item() / float(dset_sizes[phase])
            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))


            # deep copy the model
            if phase == 'val':
                if USE_TENSORBOARD:
                    foo.add_scalar_value('epoch_loss',epoch_loss,step=epoch)
                    foo.add_scalar_value('epoch_acc',epoch_acc,step=epoch)
                if epoch_acc &gt; best_acc:
                    best_acc = epoch_acc
                    best_model = copy.deepcopy(model)
                    print('new best accuracy = ',best_acc)
    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))
    print('returning and looping back')
    return best_model


def exp_lr_scheduler(optimizer, epoch, init_lr=BASE_LR, lr_decay_epoch=EPOCH_DECAY):
    &quot;&quot;&quot;Decay learning rate by a factor of DECAY_WEIGHT every lr_decay_epoch epochs.&quot;&quot;&quot;
    lr = init_lr * (DECAY_WEIGHT**(epoch // lr_decay_epoch))

    if epoch % lr_decay_epoch == 0:
        print('LR is set to {}'.format(lr))

    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

    return optimizer

 


Calculating overall accuracy is rather straight forward:
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1)

acc_all = (preds == labels).float().mean()


To calculate it per class requires a few more lines of code:
acc = [0 for c in list_of_classes]
for c in list_of_classes:
    acc[c] = ((preds == labels) * (labels == c)).float() / (max(labels == c).sum(), 1))

