I’m trying to make a custom vgg16 and the architecture is as follows:
class CustomVGG16(torch.nn.Module):
    def __init__(self):
        super(CustomVGG16, self).__init__()
        self.vgg = torchvision.models.vgg16_bn(pretrained = True)
        self.vgg.classifier[-1].out_features = 25
        self.softmax = torch.nn.Softmax()
        

    def forward(self, x):
        x = self.vgg(x)
        x = self.softmax(x)
        return x

As it can be seen that I have changed the output features in last layer to 25 from 1000. as it is verified by the model architecture:
Screenshot_2020-12-03 Google Colaboratory718×249 6.26 KB
So the model is supposed to give 25 outputs ( n x 25 ) right?
But on the following, outputs = model(inputs)
the shape of outputs is (6, 1000). 6 because of batch size.
but as mentioned earlier, I changed this 1000 to 25, so this should be (6, 25).
what could be the reason?
my training loop:
    running_loss = 0.0
    accuracy = []
    val_accuracy = []

    model = model.to(device)

    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 20, gamma = 0.1)

    for ep in range(epoch):
        correct = 0
        total = 0
        start_time = time.time()
        for i, data in enumerate(train_loader):
            inputs, labels = data

            inputs, labels = inputs.to(device), labels.to(device)

            optimizer.zero_grad()

            outputs = model(inputs)
            print(inputs.shape)
            
            loss = criterion(labels, outputs)
            loss.backward()
            optimizer.step()

            running_loss += loss.item()
            correct += (outputs.argmax(1) == labels).float().sum()
            total += len(labels)
        accuracy_local = (correct / total)*100
        accuracy_local = accuracy_local.data.cpu().numpy()

        accuracy.append(accuracy_local)
        val_acc = valid(model,criterion,optimizer,val_loader)
        val_accuracy.append(val_acc)
        scheduler.step()

        print('EPOCH: {:} Accuracy: {:.2f}% Val_Accuracy: {:.2f}% Time: {:.2f} seconds'.format(ep, accuracy_local, val_acc, time.time() - start_time))


criterion is bcewithlogitsloss