龙良曲pytorch学习笔记_minst

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

# 小工具

def plot_curve(data):
    fig = plt.figure()
    plt.plot(range(len(data)),data,color='blue')
    plt.legend(['value'],loc='upper right')
    plt.xlabel('step')
    plt.tlabel('value')
    plt.show()

def plot_image(img,label,name):
    fig = plt.figure()
    for i in range(6):
        plt.subplot(2,3,i+1)
        plt,tight_layout()
        plt.imshow(img[i][0]*0.3081+0.1307,cmap='gray',interpolation='none')
        plt.title("{}:{}".format(name,label[i].item()))
        plt.xticks([])
        plt.xticks([])
        
    plt.show()
        
def one_hot(label,depth = 10):
    out = torch.zeros(label.size(0),depth)
    idx = torch.LongTensor(label).view(-1,1)
    out.scatter_(dim=1,index=idx,value=1)
    return out
    
# 一次加载多少图片
batch_size = 512
# step1. load dataset 数据加载
train_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MINST('mnist_data',train=True,download=True,
                              transform=torchvision.transforms.Compose([
                                  torchvision.transfroms.ToTensor(),
                                  
                                  torchvision.transfroms.Normalize(
                                      (0.1307,),(0.3081,))
                              ])),
    batch_size=batch_size,shuffle=True)
test_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MINST('mnist_data/',train=False,download=True,
                              transform=torchvision.transforms.Compose([
                                  torchvision.transfroms.ToTensor(),
                                  torchvision.transfroms.Normalize(
                                      (0.1307,),(0.3081,))
                              ])),
    batch_size=batch_size,shuffle=False)
    
# 网络创建
class Net(nn.Module):
    
    def __init__(self):
        super(Net,self).__init__()
        
    #xw+b
    self.fc1 = nn.Linear(28*28,256)
    self.fc2 = nn.Linear(256,64)
    self.fc3 = nn.Linear(64,10)
    
    def forward(self,x):
        # x:[batch_size,1,28,28]
        # h1 = relu(xw1+b1)
        x = F.relu(self.fc1(x))
        # h1 = relu(h1w2+b2)
        x = F.relu(self.fc2(x))
        # h3 = h2w3+b3
        x = self.fc3(x)
        
        return x
        
net = Net()
# [w1,b1,w2,b1,w3,b3]
optimizer = optim.SGD(net.parameters(),lr=0.01,momentum=0.9)

train_loss = []

# 训练
for epoch in range(3):

    for batch_idx,(x,y) in enumerate(train_loader):
    
        # x: [b,1,28,28], y:[512]
        # [b,1,28,28]-->[b,feature]
        x = x.view(x.size(0),28*28)
        # --> [b,10]
        out = net(x)
        # --> [b,10]
        y_onehot = one_hot(y)
        # loss = mse(out,y_onehot)
        loss = F.mse_loss(out,y_onehot)
        # 清零梯度
        optimizer.zero_grad()
        # 计算梯度
        loss.backward()
        #w' = w - lr*grad 更新梯度
        optimizer.step()
        
        train_loss.append(loss.item())
        
        if batch_idx % 10 == 0:
            print(epoch,batch_idx,loss.item())
            
plot_curve(train_loss)
            
# 得到一个比较好的    [w1,b1,w2,b1,w3,b3]    


# 验证准确率
total_correct = 0
for x,y in test_loader"
    x = x.view(x.size(0),28*28)
    out = net(x)
    # out: [b,10] --> pred: [b]
    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)

# 直观显示验证
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')