怎么利用pytorch实现对CIFAR-10数据集的分类-创新互联
小编给大家分享一下怎么利用pytorch实现对CIFAR-10数据集的分类，相信大部分人都还不怎么了解，因此分享这篇文章给大家参考一下，希望大家阅读完这篇文章后大有收获，下面让我们一起去了解一下吧！
步骤如下：
1.使用torchvision加载并预处理CIFAR-10数据集、
2.定义网络
3.定义损失函数和优化器
4.训练网络并更新网络参数
5.测试网络
运行环境：
windows+python3.6.3+pycharm+pytorch0.3.0
import torchvision as tv
import torchvision.transforms as transforms
import torch as t
from torchvision.transforms import ToPILImage
show=ToPILImage() #把Tensor转成Image，方便可视化
import matplotlib.pyplot as plt
import torchvision
import numpy as np
###############数据加载与预处理
transform = transforms.Compose([transforms.ToTensor(),#转为tensor
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),#归一化
])
#训练集
trainset=tv.datasets.CIFAR10(root='/python projects/test/data/',
train=True,
download=True,
transform=transform)
trainloader=t.utils.data.DataLoader(trainset,
batch_size=4,
shuffle=True,
num_workers=0)
#测试集
testset=tv.datasets.CIFAR10(root='/python projects/test/data/',
train=False,
download=True,
transform=transform)
testloader=t.utils.data.DataLoader(testset,
batch_size=4,
shuffle=True,
num_workers=0)
classes=('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')
(data,label)=trainset[100]
print(classes[label])
show((data+1)/2).resize((100,100))
# dataiter=iter(trainloader)
# images,labels=dataiter.next()
# print(''.join('11%s'%classes[labels[j]] for j in range(4)))
# show(tv.utils.make_grid(images+1)/2).resize((400,100))
def imshow(img):
img = img / 2 + 0.5
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
dataiter = iter(trainloader)
images, labels = dataiter.next()
print(images.size())
imshow(torchvision.utils.make_grid(images))
plt.show()#关掉图片才能往后继续算
#########################定义网络
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.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)
x = F.max_pool2d(F.relu(self.conv2(x)),2)
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()
print(net)
#############定义损失函数和优化器
from torch import optim
criterion=nn.CrossEntropyLoss()
optimizer=optim.SGD(net.parameters(),lr=0.01,momentum=0.9)
##############训练网络
from torch.autograd import Variable
import time
start_time = time.time()
for epoch in range(2):
running_loss=0.0
for i,data in enumerate(trainloader,0):
#输入数据
inputs,labels=data
inputs,labels=Variable(inputs),Variable(labels)
#梯度清零
optimizer.zero_grad()
outputs=net(inputs)
loss=criterion(outputs,labels)
loss.backward()
#更新参数
optimizer.step()
# 打印log
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')
end_time = time.time()
print("Spend time:", end_time - start_time)pytorch的优点1.PyTorch是相当简洁且高效快速的框架；2.设计追求最少的封装；3.设计符合人类思维，它让用户尽可能地专注于实现自己的想法；4.与google的Tensorflow类似，FAIR的支持足以确保PyTorch获得持续的开发更新；5.PyTorch作者亲自维护的论坛 供用户交流和求教问题6.入门简单
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