訓練一個分類器（筆記）

import torch

import torchvision

import torchvision.transforms as trasnforms

import torch.nn as nn

import torch.nn.functional as F

import torch.optim as optim

import os

import matplotlib.pyplot as plt

import numpy as np





os.environ["CUDA_VISIBLE_DEVICES"] = "5"







transform=trasnforms.Compose([trasnforms.ToTensor(),trasnforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])



trainset=torchvision.datasets.CIFAR10(root='/home/tianxiaoxiao/data',train=True,download=True,transform=transform)



trainloader=torch.utils.data.DataLoader(trainset,batch_size=4,shuffle=True,num_workers=2)



testset=torchvision.datasets.CIFAR10(root='/home/tianxiaoxiao/data',train=False,download=True,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')









def imshow(img):

	img=img/2+0.5

	npimg=img.numpy() # error

	plt.imshow(np.transpose(npimg,(1,2,0)))



dataiter=iter(trainloader)

images,labels=dataiter.next()



imshow(torchvision.utils.make_grid(images))



#print(' '.join('%5s' % classes[labels[j]] for j in range(4)))









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()



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):

		inputs,labels=data

		optimizer.zero_grad()



		outputs=net(inputs)

		loss=criterion(outputs,labels)

		loss.backward()

		optimizer.step()



		running_loss+=loss.item()

		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(images)

_,predicted=torch.max(outputs,1)



print('predicted: ',' '.join('%5s' % classes[predicted[j]]

	for j in range(4)))



correct=0

total=0

with torch.no_grad():

	for data in testloader:

		images,labels=data

		outputs=net(images)

		_,predicted=torch.max(outputs.data,1)

		total+=labels.size(0)

		correct+=(predicted==labels).sum().item()



print('Accuracy of the network on the 10000 test images: %d %%' %(100*correct/total))





class_correct=list(0. for i in range(10))

class_total=list(0. for i in range(10))

with torch.no_grad():

	for data in testloader:

		images,labels=data

		outputs=net(images)

		_,predicted=torch.max(outputs,1)

		c=(predicted==labels).squeeze()

		for i in range(4):

			label=labels[i]

			class_correct[label]+=c[i].item()

			class_total[label]+=1



for i in range(10):

	print('Accuracy of %5s : %2d %%' % (classes[i],100*class_correct[i]/class_total[i]))