Cross validation for MNIST dataset with pytorch and sklearn

I think you're confused!

Ignore the second dimension for a while, When you've 45000 points, and you use 10 fold cross-validation, what's the size of each fold? 45000/10 i.e. 4500.

It means that each of your fold will contain 4500 data points, and one of those fold will be used for testing, and the remaining for training i.e.

For testing: one fold => 4500 data points => size: 4500
For training: remaining folds => 45000-4500 data points => size: 45000-4500=40500

Thus, for first iteration, the first 4500 data points (corresponding to indices) will be used for testing and the rest for training. (Check below image)

Given your data is x_train: torch.Size([45000, 784]) and y_train: torch.Size([45000]), this is how your code should look like:

for train_index, test_index in kfold.split(x_train, y_train):  
    print(train_index, test_index)

    x_train_fold = x_train[train_index] 
    y_train_fold = y_train[train_index] 
    x_test_fold = x_train[test_index] 
    y_test_fold = y_train[test_index] 

    print(x_train_fold.shape, y_train_fold.shape) 
    print(x_test_fold.shape, y_test_fold.shape) 
    break 

[ 4500  4501  4502 ... 44997 44998 44999] [   0    1    2 ... 4497 4498 4499]
torch.Size([40500, 784]) torch.Size([40500])
torch.Size([4500, 784]) torch.Size([4500])

So, when you say

I want the variable x_train_fold to be the first 4500 picture... shape torch.Size([4500, 784]).

you're wrong. this size corresonds to x_test_fold. In the first iteration, based on 10 folds, x_train_fold will have 40500 points, thus its size is supposed to be torch.Size([40500, 784]).

Think I have it right now, but I feel the code is a bit messy, with 3 nested loops. Is there any simpler way to it or is this approach okay?

Here's my code for the training with cross validation:

def train(network, epochs, save_Model = False):
    total_acc = 0
    for fold, (train_index, test_index) in enumerate(kfold.split(x_train, y_train)):
        ### Dividing data into folds
        x_train_fold = x_train[train_index]
        x_test_fold = x_train[test_index]
        y_train_fold = y_train[train_index]
        y_test_fold = y_train[test_index]

        train = torch.utils.data.TensorDataset(x_train_fold, y_train_fold)
        test = torch.utils.data.TensorDataset(x_test_fold, y_test_fold)
        train_loader = torch.utils.data.DataLoader(train, batch_size = batch_size, shuffle = False)
        test_loader = torch.utils.data.DataLoader(test, batch_size = batch_size, shuffle = False)

        for epoch in range(epochs):
            print('\nEpoch {} / {} \nFold number {} / {}'.format(epoch + 1, epochs, fold + 1 , kfold.get_n_splits()))
            correct = 0
            network.train()
            for batch_index, (x_batch, y_batch) in enumerate(train_loader):
                optimizer.zero_grad()
                out = network(x_batch)
                loss = loss_f(out, y_batch)
                loss.backward()
                optimizer.step()
                pred = torch.max(out.data, dim=1)[1]
                correct += (pred == y_batch).sum()
                if (batch_index + 1) % 32 == 0:
                    print('[{}/{} ({:.0f}%)]\tLoss: {:.6f}\t Accuracy:{:.3f}%'.format(
                        (batch_index + 1)*len(x_batch), len(train_loader.dataset),
                        100.*batch_index / len(train_loader), loss.data, float(correct*100) / float(batch_size*(batch_index+1))))
        total_acc += float(correct*100) / float(batch_size*(batch_index+1))
    total_acc = (total_acc / kfold.get_n_splits())
    print('\n\nTotal accuracy cross validation: {:.3f}%'.format(total_acc))

You messed with indices.

x_train = x[train_index]
x_test = x[test_index]
y_train = y[train_index]
y_test = y[test_index]

    x_fold = x_train[train_index]
    y_fold = y_train[test_index]

It should be:

x_fold = x_train[train_index]
y_fold = y_train[train_index]