
import torch 
import torch.nn as nn


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
 #         self.conv1 = nn.Conv2d(1, 20, 5, 1)
#         self.conv2 = nn.Conv2d(20, 50, 5, 1)
#         self.fc1 = nn.Linear(4*4*50, 500)
#         self.fc2 = nn.Linear(500, 10) *)

        self.features = nn.Sequential(nn.Conv2d(1, 20, 5, 1), nn.ReLU(True), nn.MaxPool2d(2,2),
                                      nn.Conv2d(20, 50, 5, 1), nn.ReLU(True), nn.MaxPool2d(2,2))
        self.classifier = nn.Sequential(nn.Linear(4*4*50, 500), nn.ReLU(True),
                                        nn.Linear(500, 10))

    def forward(self, x):
#         x = F.relu(self.conv1(x))
#         x = F.max_pool2d(x, 2, 2)
#         x = F.relu(self.conv2(x))
#         x = F.max_pool2d(x, 2, 2)
#         x = x.view(-1, 4*4*50)
#         x = F.relu(self.fc1(x))
#         x = self.fc2(x) *)
        x = self.features(x)
        x = x.view(-1,4*4*50)
        x = self.classifier(x)
        return x#F.log_softmax(x, dim=1)
    
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.cross_entropy(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))
