I have a dataset with 4 classes A, B, C and D. After training the alexnet to descriminative between the three classes, I want  to extract the features from the last layer for each class individeually. in other words,  I want a vector with (number of samples in class A, 4096) and the same for B,C and D.
the code divides into some stages: load the dataset-   modify the last layer in alexnet - trained_model function- trained the alexnet for all classes in the dataset- create dataloader for each class - /// should I pass each class separetly to train_model function
 dataset_root = dset.ImageFolder('path root', transform=Compose([Resize((224,224)),ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]))
print(len(dataset))
datasets = train_val_dataset(dataset_root)
print('number of training samples= ', len(datasets['train']))
print('number of validation samples= ', len(datasets['val']))
# The original dataset is available in the Subset class
print(datasets['train'].dataset)

dataloaders    = {x:DataLoader(datasets[x], batch_size= 64, shuffle=True, num_workers=4) for x in ['train','val']}
dataset_sizes = {x: len(datasets[x]) for x in ['train', 'val']}

class_names = dataset.class_to_idx
print(dict(Counter(dataset.targets)))


x,y = next(iter(dataloaders['train']))
print(x.shape, y.shape)   # The shape of a batch is (batch_size, color_channels, height, width).
print('class_names is :', class_names)


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

############
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    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':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)
            if phase == 'train':
                scheduler.step()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

         
            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
                
        print()

    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))

    # load best model weights
    model.load_state_dict(best_model_wts)
    

    return model
############

Alexnet_model = torchvision.models.alexnet(pretrained=True)
 # this loop will freeze all layers
for param in Alexnet_model.parameters():
    param.requires_grad = False

Alexnet_model.classifier[6] = nn.Linear(Alexnet_model.classifier[6].in_features, 4)

#Compute SGD cross-entropy loss
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_conv = Alexnet_model.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that only parameters of final layer are being optimized as  opposed to before.
optimizer_conv = optim.SGD(model_conv.classifier.parameters(), lr=0.0001, momentum=0.9, weight_decay=0.0001)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=3, gamma=0.9)

model, best_model_wts = train_model(model_conv, criterion, optimizer_conv,
                         exp_lr_scheduler, num_epochs=256)

###############
### pick out the feature extractor:
#import  trained alexnet
m.classifier[6] = nn.Linear(Alexnet_model.classifier[6].in_features, 1)

#Compute SGD cross-entropy loss
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_class= m.to(device)
criterion = nn.CrossEntropyLoss()
optimizer_conv = optim.SGD(model_class.classifier.parameters(), lr=0.0001, momentum=0.9, weight_decay=0.0001)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=3, gamma=0.9)


# define each class in the dataset
class_names = train_img.class_to_idx
print(dict(Counter(dataset_root.targets)))

A_dset = Subset(dataset_root,   range(0, 826))
B_dset = Subset(dataset_root,   range(826, 1648))
C_dset = Subset(dataset_root,   range(1648, 2043))
D_dset = Subset(dataset_root,   range(2043, 2870))


A_loader = DataLoader(A_dset, batch_size=1, shuffle = False)
B_loader = DataLoader(B_dset, batch_size=1, shuffle = False)
C_loader = DataLoader(C_dset, batch_size=1, shuffle = False)
D_loader = DataLoader(D_dset, batch_size=1, shuffle = False)



I confused about the next step!!. should I creat a custom train_model for each class or what