8_softmax_MNIST_pred.eq(labels.long().view_as(pred))

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets, transforms


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.l1 = nn.Linear(784, 520)
        self.l2 = nn.Linear(520, 320)
        self.l3 = nn.Linear(320, 240)
        self.l4 = nn.Linear(240, 120)
        self.l5 = nn.Linear(120, 10)

    def forward(self, x):
    	# view类似于np中的resize
        x = x.view(-1, 784)
        x = F.relu(self.l1(x))
        x = F.relu(self.l2(x))
        x = F.relu(self.l3(x))
        x = F.relu(self.l4(x))
        return self.l5(x)


class Run():
    def train(self, epoch):
        model.train()
        for batch_idx, (data, target) in enumerate(train_loader):
            data, target = Variable(data), Variable(target)
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
            if batch_idx % 10 == 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(self):
        model.eval()
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            data, target = Variable(data, volatile=True), Variable(target)
            output = model(data)

            test_loss += criterion(output, target).data[0]
            pred = output.data.max(1, keeepdim=True)[1]
            correct += pred.eq(target.data.view_as(pred)).cpu().sum()

        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)))

    def run(self):
        for epoch in range(1, 10):
            self.train(epoch)
            self.test()


if __name__ == "__main__":
    print("Life is short, You need Python!")
    batch_size = 64
    # MNIST Dataset
    train_dataset = datasets.MNIST(root='.//data//', download=True, train=True, transform=transforms.ToTensor())
    test_dataset = datasets.MNIST(root='.//data//', download=True, train=True, transform=transforms.ToTensor())

    # Data loader
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=False)
    test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)

    model = Net()
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
    r = Run()
    r.run()
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