pytorch实现cifar10分类
1.pytorch60分钟上手教程中的代码，网上的代码存在部分错误，在此进行了改正，并添加了必要注释。总的来说，pytorch无论在网络模型的定义还是在数据的预处理等方面都比使用tensorflow要方便，tensorflow代码的风格更偏向于细节，参数较多，底层函数也较容易更改。在调试代码方面，一般容易出错的地方在tensor的维度上，用debug调试，查看pytorch中tensor变量的维度值，并使用正确索引或使用 .sequeeze()函数，对tensor变量去除维度为1的多余shape信息。
2.代码
'''
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 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):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:]
num_features = 1
for s in size:
num_features *= s
return num_features
net = Net()
print(net)
optimizer = optim.SGD(net.parameters(), lr=0.01)
input = Variable(torch.randn(1, 1, 32, 32))
target = Variable(torch.arange(1, 11))
criterion = nn.MSELoss()
# print(input)
optimizer.zero_grad()
output = net.forward(input)
loss = criterion(output, target)
print('conv1.bias.grad begore backward')
print(net.conv1.bias.grad)
loss.backward()
optimizer.step()
print('conv1.bias.grad after backward')
print(net.conv1.bias.grad)
#*******************************************************************************
'''
import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
)
trainset = torchvision.datasets.CIFAR10(root='./datasets', train=True, download=False, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2) # 2线程读取数据
testset = torchvision.datasets.CIFAR10(root='./datasets', train=False, download=False, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
import matplotlib.pyplot as plt
import numpy as np
def imshow(img):
img = img / 2 + 0.5 # [-1, 1] -> [0, 1]
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
#get some random training images
# dataiter = iter(trainloader)
# images, labels = dataiter.next()
#
# imshow(torchvision.utils.make_grid(images))
# print(''.join('%5s' % classes[labels[j]] for j in range(4)))
# plt.show()
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
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):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16*5*5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(trainloader, 0): # enumerate可以接收第二个参数，用于指定索引起始值
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] # 将torch.tensor变量loss.data变为realnumber(真实数据)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')
dataiter = iter(testloader)
images, labels = dataiter.next()
# imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
outputs = net(Variable(images))
_, predicted = torch.max(outputs.data, 1)
print('Predicted: ', ' '.join('%5s' % classes[predicted[j][0]] for j in range(4))) # predicted是一个4*1的张量,predicted[j][0]以保证turple索引的是一个数
# plt.show()
correct = 0
total = 0
for data in testloader:
images, labels = data
outputs = net(Variable(images))
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Accuracy of the network on the 10000 test images: %d %% \n' % (100 * correct / total))
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
for data in testloader:
images, labels = data # labels shape -> (4,)
outputs = net(Variable(images)) # predicted shape -> (4, 1)
_, predicted = torch.max(outputs.data, 1)
c = (predicted == labels) # squeeze(), return a tensor with all the dimensions of input of size 1 removed
c = c.squeeze() # translate shape from (4*1) to (4)
for i in range(4):
label = labels[i]
class_correct[label] += c[i]
class_total[label] += 1
for i in range(10):
print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))