PyTorch: 训练分类器实战详解(分类CIFAR10)
2018-02-11 17:44
357 查看
神经网络模型训练过程
通过学习前面几个例子,我们可以总结一下神经网络的典型训练过程如下:第一步:定义具有一些可学习参数(或权重)的神经网络
第二步:迭代输入数据集
第三步:通过网络处理输入
第四步:计算损失(loss)
第五步:反向传播网络的参数
第六步:更新网络的参数,通常使用一个简单的更新规则:weight = weight - learning_rate * gradient
使用PyTorch训练分类器
本文通过官网上一个更为具体的例子来更好的学习使用PyTorch训练分类模型的具体过程。数据预处理
一般来说,当我们在处理图像,文本,音频或视频数据时,可以使用标准的Python包将数据加载到一个numpy数组中。然后把这个数组转换成 torch.* Tensor。1.在处理图片数据方面,可以选择使用Pillow,OpenCV等。
2.在处理音频数据方面,可以选择使用scipy,librosa等。
3.在处理文本数据方面,可以选择使用NLTK,SpaCy等。
在PyTorch这个框架中,对于计算机视觉,已经创建了一个名为torchvision的包,包含了常见数据集的数据加载器,如Imagenet,CIFAR10,MNIST等,以及用于图像的数据转换器,即torchvision.datasets和torch.utils.data.DataLoader。
在本文中,我们将使用CIFAR10数据集。 它有 ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’, ‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’类。 CIFAR-10中的图像大小为3x32x32,即32x32像素的3通道彩色图像。
训练图像分类器
根据前面提到的,我们将按顺序执行以下步骤:第一步:使用torchvision加载和规范CIFAR10训练和测试数据集
第二步:定义模型:选用卷积神经网络
第三步:定义损失函数
第四步:在训练数据上训练网络
第五步:在测试数据上测试网络
加载CIFAR10数据集
使用torchvision可以很方面的加载CIFAR10数据集。torchvision数据集的输出是范围[0,1]的PILImage图像。 我们将它们转换为归一化范围的张量[-1,1]。下面代码可视化一个batch的图片。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='./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='./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') import matplotlib.pyplot as plt import numpy as np # functions to show an image def imgshow(img): img = img / 2 + 0.5 # unnormalize npimg = img.numpy() plt.imshow(np.transpose(npimg, (1, 2, 0))) plt.show() if __name__ == '__main__': # get some random training images dataiter = iter(trainloader) images, labels = dataiter.next() # show images imgshow(torchvision.utils.make_grid(images)) # print labels print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
运行结果:
定义卷积神经网络
此时为3通道图像(而不是单通道图像):from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F # 3*32*32 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))) # fully connect 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
定义损失函数和优化器
# Define loss (Cross-Entropy) import torch.optim as optim criterion = nn.CrossEntropyLoss() # SGD with momentum optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
训练网络
循环遍历数据迭代器,并将数据输入到网络并进行优化。# Train the network for epoch in range(5): running_loss = 0.0 for i, data in enumerate(trainloader, 0): # get the inputs inputs, labels = data # warp them in Variable inputs, labels = Variable(inputs), Variable(labels) # zero the parameter gradients optimizer.zero_grad() # forward outputs = net(inputs) # loss loss = criterion(outputs, labels) # backward loss.backward() # update weights optimizer.step() # print statistics running_loss += loss.data[0] if i % 2000 == 1999: # print every 2000 mini-batches print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000)) running_loss = 0.0 print("Finished Training")
在测试数据上测试训练好的模型
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()
完整代码
#author: yuquanle
#date: 2018.2.5
#Classifier use PyTorch (CIFAR10 dataset)
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='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=1)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=1)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# 3*32*32
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)))
# fully connect
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
# our model
net = Net()
# Define loss (Cross-Entropy) import torch.optim as optim criterion = nn.CrossEntropyLoss() # SGD with momentum optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
if __name__ == '__main__':
# Train the network
for epoch in range(5):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
# warp them in Variable
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
optimizer.zero_grad()
# forward
outputs = net(inputs)
# loss
loss = criterion(outputs, labels)
# backward
loss.backward()
# update weights
optimizer.step()
# print statistics
running_loss += loss.data[0]
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print("Finished Training")
print("Beginning Testing")
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 %%' % (
100 * correct / total))
运行结果:
Files already downloaded and verified Files already downloaded and verified Files already downloaded and verified Files already downloaded and verified [1, 2000] loss: 2.190 [1, 4000] loss: 1.860 [1, 6000] loss: 1.687 [1, 8000] loss: 1.591 [1, 10000] loss: 1.524 [1, 12000] loss: 1.471 Files already downloaded and verified Files already downloaded and verified [2, 2000] loss: 1.385 [2, 4000] loss: 1.367 [2, 6000] loss: 1.343 [2, 8000] loss: 1.311 [2, 10000] loss: 1.285 [2, 12000] loss: 1.281 Files already downloaded and verified Files already downloaded and verified [3, 2000] loss: 1.198 [3, 4000] loss: 1.194 [3, 6000] loss: 1.193 [3, 8000] loss: 1.172 [3, 10000] loss: 1.168 [3, 12000] loss: 1.142 Files already downloaded and verified Files already downloaded and verified [4, 2000] loss: 1.063 [4, 4000] loss: 1.096 [4, 6000] loss: 1.070 [4, 8000] loss: 1.074 [4, 10000] loss: 1.086 [4, 12000] loss: 1.066 Files already downloaded and verified Files already downloaded and verified [5, 2000] loss: 0.988 [5, 4000] loss: 0.996 [5, 6000] loss: 1.010 [5, 8000] loss: 0.999 [5, 10000] loss: 1.012 [5, 12000] loss: 1.016 Finished Training Beginning Testing Files already downloaded and verified Files already downloaded and verified Accuracy of the network on the 10000 test images: 62 % Process finished with exit code 0
参考:http://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
相关文章推荐
- PyTorch: RNN实战详解之分类名字
- haartraining训练分类器方法cvCreateTreeCascadeClassifier()详解——人脸识别的尝试系列(四)
- 21天实战caffe - 运行基于已训练模型的图片分类例程 - 报错:Unable to decode image
- 3000门徒内部训练绝密视频(泄密版)第10课:彻底实战详解使用Java开发Spark程序
- 用线性分类器对cifar10数据集进行分类
- PyTorch: RNN实战详解之生成名字
- adaboost训练 之 弱分类器训练的opencv源码详解 1
- 详解tensorflow训练自己的数据集实现CNN图像分类
- pytorch训练imagenet分类
- adaboost训练——弱分类器训练的opencv源码详解 (1)
- Maven实战——仓库的分类详解
- 听霍强老师讲模式分类器设计的最小分类误差训练(MCE)
- caffe19 《深度学习--Caffe之经典模型详解与实战》笔记01 分类测试python调用
- Pytorch tutorials 实战教程(1)——训练自己的数据集(代码详解)
- adaboost训练——弱分类器训练的opencv源码详解 (2)
- 1.3近邻分类器分类Cifar10图像数据集(下)
- Pytorch入门实例:mnist分类训练
- 【深度学习】笔记6:基于CIFAR10网络,训练自己的分类网络的记录
- adaboost训练 之 弱分类器训练的opencv源码详解 2
- (一) cifar10分类训练