import torchvision as tv
import torchvision.transforms as transforms
from torchvision.transforms import ToPILImage
import matplotlib.pyplot as plt
import numpy as np
import torch
import matplotlib
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
matplotlib.use('Qt5Agg')
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
#nn.Module.__init__(self)
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
#print("forward被执行")
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2 )
x = x.view(x.size(0),-1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
if __name__ == '__main__':
show = ToPILImage() # tensor转image
transforms = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) # 对数据的预处理
trainset = tv.datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=2)
testset = tv.datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# (data,label) = trainset[100]
# print(classes[label]) #查看训练集中第一百个样本的类
# plt.imshow(show((data+1)/2).resize((100,100)))
# plt.axis('off') # 关闭坐标轴
# plt.show()
dataiter = iter(trainloader)
images, labels = dataiter.__next__()
print(' '.join('%11s' % classes[labels[j]] for j in range(4)))
#show(tv.utils.make_grid((images + 1) / 2)).resize((400, 100))
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(20):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs,labels =Variable(inputs),Variable(labels)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.data[0]
if i % 2000 == 1999:
print('[%d,%5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
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