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  • Pytorch训练网络测试准确率完整流程及源码

    注:在运行这个源码之前,需要下载cifar-10-python.tar.gz文件

    源码:

    import torch
    import torchvision
    import torchvision.transforms as transforms

    import matplotlib.pyplot as plt
    import numpy as np

    from torch.autograd import Variable
    import torch.nn as nn
    import torch.nn.functional as F

    import torch.optim as optim


    # 加载和归一化Cifar10
    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')


    # 定义一个卷积神经网络
    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()


    # 定义损失函数(loss function)和优化器(optimizer)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)


    # 训练网络
    for epoch in range(2): # loop over the dataset multiple times# 循环遍历数据集的次数

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
    # get the inputs
    inputs, labels = data

    # wrap them in Variable
    inputs, labels = Variable(inputs), Variable(labels)

    # zero the parameter gradients
    optimizer.zero_grad()

    # forward + backward + optimize
    outputs = net(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

    # print statistics
    running_loss += loss.item()
    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')


    # 在测试数据上训练网络
    def imshow(img):
    img = img / 2 + 0.5 # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()

    dataiter = iter(testloader)
    images, labels = dataiter.next()

    # print images
    imshow(torchvision.utils.make_grid(images))
    print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))

    # 神经网络预测图像类别
    outputs = net(images)

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

    print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
    for j in range(4)))


    # 网络在整个测试集上的结果如何
    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().item()

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

    # 分析在什么类上预测较好,什么类预测结果不好
    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
    outputs = net(Variable(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]))

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  • 原文地址:https://www.cnblogs.com/wanggangtao/p/11357981.html
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