My code is below
loss_epoch_arr = []
max_epochs = 5
min_loss = 1000
n_iters = np.ceil(50000/training_batchsize)
for epoch in range(max_epochs):
for i, data in enumerate(train_loader, 0):

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

    opt.zero_grad()

    outputs, aux_outputs = inception(inputs)
    loss = loss_fn(outputs, labels) + 0.3 * loss_fn(aux_outputs, labels)
    loss.backward()
    opt.step()
    
    if min_loss > loss.item():
        min_loss = loss.item()
        best_model = copy.deepcopy(inception.state_dict())
        print('Min loss %0.2f' % min_loss)
    
    if i % 100 == 0:
        print('Iteration: %d/%d, Loss: %0.2f' % (i, n_iters, loss.item()))
        
    del inputs, labels, outputs
    torch.cuda.empty_cache()
    
loss_epoch_arr.append(loss.item())
    
print('Epoch: %d/%d, Test acc: %0.2f, Train acc: %0.2f' % (
    epoch, max_epochs, 
    evaluation_inception(test_loader, inception), 
    evaluation_inception(train_loader, inception)))

plt.plot(loss_epoch_arr)
plt.show()
Error occurred:-
RuntimeError:-Calculated padded input size per channel: (3 x 3). Kernel size: (5 x 5). Kernel size can’t be greater than actual input size
How to resolve it?