龙良曲pytorch学习笔记_03

 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')