import torch
from torch import nn, optim

from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision import datasets

batch_size      = 32
learning_rate   = 1e-2
num_epoches     = 50

# 下载训练集 MNIST 手写数字训练集
dataset_path = "../data/mnist"

train_dataset = datasets.MNIST(
    root=dataset_path, train=True, transform=transforms.ToTensor(), download=True)

test_dataset = datasets.MNIST(
    root=dataset_path, train=False, transform=transforms.ToTensor())

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader  = DataLoader(test_dataset,  batch_size=batch_size, shuffle=False)


# 定义简单的前馈神经网络
class Neuralnetwork(nn.Module):
    def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):
        super(Neuralnetwork, self).__init__()
        self.layer1 = nn.Linear(in_dim, n_hidden_1)
        self.layer2 = nn.Linear(n_hidden_1, n_hidden_2)
        self.layer3 = nn.Linear(n_hidden_2, out_dim)

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        return x


model = Neuralnetwork(28 * 28, 300, 100, 10)
if torch.cuda.is_available():
    model = model.cuda()

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

for epoch in range(num_epoches):
    print('epoch {}'.format(epoch + 1))
    print('*' * 10)
    running_loss = 0.0
    running_acc = 0.0

    for i, data in enumerate(train_loader, 1):
        # FIXME: label need to change one-hot coding
        img, label = data
        img = img.view(img.size(0), -1)
        target = torch.zeros(label.size(0), 10)
        target = target.scatter_(1, label.data, 1)

        if torch.cuda.is_available():
            img = Variable(img).cuda()
            label = Variable(label).cuda()
        else:
            img = Variable(img)
            label = Variable(label)

        # 向前传播
        out = model(img)
        loss = criterion(out, label)
        running_loss += loss.data[0] * label.size(0)
        _, pred = torch.max(out, 1)
        num_correct = (pred == label).sum()
        running_acc += num_correct.data[0]

        # 向后传播
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if i % 300 == 0:
            print('[{}/{}] Loss: {:.6f}, Acc: {:.6f}'.format(
                epoch + 1, num_epoches, running_loss / (batch_size * i),
                running_acc / (batch_size * i)))

    print('Finish {} epoch, Loss: {:.6f}, Acc: {:.6f}'.format(
        epoch + 1, running_loss / (len(train_dataset)), running_acc / (len(
            train_dataset))))

    model.eval()
    eval_loss = 0.
    eval_acc = 0.

    for data in test_loader:
        img, label = data
        img = img.view(img.size(0), -1)
        if torch.cuda.is_available():
            img = Variable(img, volatile=True).cuda()
            label = Variable(label, volatile=True).cuda()
        else:
            img = Variable(img, volatile=True)
            label = Variable(label, volatile=True)
        out = model(img)
        loss = criterion(out, label)
        eval_loss += loss.data[0] * label.size(0)
        _, pred = torch.max(out, 1)
        num_correct = (pred == label).sum()
        eval_acc += num_correct.data[0]

    print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(
        test_dataset)), eval_acc / (len(test_dataset))))
    print()

# 保存模型
torch.save(model.state_dict(), './model_Neural_Network.pth')