思路：
网络是三层：

1. h1=relu(w1*x+b1)-输入28*28，输出256
2. h2=relu(w2*h1+b2)-输入256，输出64
3. h3=w3*h2+b3-输入64，输出10
   其中256，64是自己构想的，10是固定的结果10分类
   每次是512个28*28的1通道图片进入一起处理，处理三次得出结果
   注意
   每次计算梯度都要先清零：optimizer.zero_grad()，不然梯度累加就不正确

import  torch
from    torch import nn
from    torch.nn import functional as F
from    torch import optim

import  torchvision
from    matplotlib import pyplot as plt

from    utils import plot_image, plot_curve, one_hot

batch_size = 512

# step1. load dataset
train_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST('mnist_data', train=True, download=False,
                               transform=torchvision.transforms.Compose([
                                   torchvision.transforms.ToTensor(),
                                   torchvision.transforms.Normalize(
                                       (0.1307,), (0.3081,))
                               ])),
    batch_size=batch_size, shuffle=True)

test_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST('mnist_data/', train=False, download=False,
                               transform=torchvision.transforms.Compose([
                                   torchvision.transforms.ToTensor(),
                                   torchvision.transforms.Normalize(
                                       (0.1307,), (0.3081,))
                               ])),
    batch_size=batch_size, shuffle=False)

x, y = next(iter(test_loader))
print(x.shape, y.shape, x.min(), x.max())
plot_image(x, y, 'image sample')

class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()

        self.fc1 = nn.Linear(28*28, 256)
        self.fc2 = nn.Linear(256, 64)
        self.fc3 = nn.Linear(64, 10)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

train_loss = []
for epoch in range(3):

    for batch_idx, (x, y) in enumerate(train_loader):

        x = x.view(x.size(0), 28*28)
        out = net (x)
        y_onehot = one_hot(y)
        loss = F.mse_loss(out, y_onehot)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        train_loss.append(loss.item())

        if batch_idx % 10 == 0:
            print(epoch, batch_idx, loss.item())

plot_curve(train_loss)

total_correct = 0
for x,y in test_loader:
    x = x.view(x.size(0), 28*28)
    out = net(x)
    pred = out.argmax(dim=1)
    correct = pred.eq(y).sum().float().item()
    total_correct += correct

total_num = len(test_loader.dataset)
acc = total_correct / total_num
print('test acc:', acc)

x, y = next(iter(test_loader))
out = net(x.view(x.size(0), 28*28))
pred = out.argmax(dim=1)
plot_image(x, pred, 'test')
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