图像分类 - 回到 PyTorch 笔记

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np

# numpy 数据转 Tensor
transform = transforms.Compose(
    [
        transforms.ToTensor(),
        transforms.Normalize(
            (0.5, 0.5, 0.5,),
            (0.5, 0.5, 0.5,),
        ),
    ]
)

# 训练数据
train_set = torchvision.datasets.CIFAR10(
    root='./data',
    train=True,
    download=True,
    transform=transform,
)
train_loader = DataLoader(
    train_set,
    batch_size=4,
    shuffle=True,
    num_workers=2,
)
# 测试数据
test_set = torchvision.datasets.CIFAR10(
    root='./data',
    train=False,
    download=True,
    transform=transform,
)
test_loader = DataLoader(
    test_set,
    batch_size=4,
    shuffle=False,
    num_workers=2,
)
classes = ('飞机', '汽车', '鸟', '猫', '鹿', '狗', '青蛙', '马', '船', '卡车')


def im_show(img):
    img = img / 2 + 0.5
    np_img = img.numpy()
    plt.imshow(np.transpose(np_img, (1, 2, 0)))
    plt.show()


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)  # 卷积操作；提取特征
        self.pool = nn.MaxPool2d(2, 2)  # 卷积操作；它的作用是提取输入数据中的最大值
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # 线性变换
        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


if __name__ == '__main__':  # 这里不这样来一下就会报错
    # 下面这段主要是查看一下图片被标记的种类
    dataiter = iter(train_loader)
    images, labels = dataiter.__next__()
    im_show(torchvision.utils.make_grid(images))
    print(' '.join('%5s' % classes[labels[j]] for j in range(4)))

    net = Net()  # 创建神经网络
    criterion = nn.CrossEntropyLoss()  # 指定损失函数
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)  # 创建优化器
    for epoch in range(3):
        running_loss = .0
        for i, data in enumerate(train_loader, 0):
            inputs, labels = data
            optimizer.zero_grad()  # 重置所有模型参数的梯度
            outputs = net(inputs)
            loss = criterion(outputs, labels)  # 计算损失
            loss.backward()  # 反向传播
            optimizer.step()  # 更新规则
            running_loss += loss.item()
            if i % 2000 == 1999:
                print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0

    dataiter = iter(test_loader)
    images, labels = dataiter.__next__()
    im_show(torchvision.utils.make_grid(images))
    print('预期: ', ' '.join('%6s' % classes[labels[j]] for j in range(4)))

    outputs = net(images)  # 进行预测
    _, predicted = torch.max(outputs, 1)
    print('预测: ', ' '.join('%6s' % classes[predicted[j]] for j in range(4)))

    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()  # 这里的 sum() 是 torch 提供的，是 tensor 的函数

    print('网络对10000张测试图像的准确性: %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 test_loader:
            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('准确率 %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))

船    汽车     马     船
[1,  2000] loss: 2.156
[1,  4000] loss: 1.835
[1,  6000] loss: 1.689
[1,  8000] loss: 1.577
[1, 10000] loss: 1.512
[1, 12000] loss: 1.467
[2,  2000] loss: 1.385
[2,  4000] loss: 1.354
[2,  6000] loss: 1.341
[2,  8000] loss: 1.306
[2, 10000] loss: 1.309
[2, 12000] loss: 1.272
[3,  2000] loss: 1.219
[3,  4000] loss: 1.193
[3,  6000] loss: 1.205
[3,  8000] loss: 1.183
[3, 10000] loss: 1.176
[3, 12000] loss: 1.154
预期:       猫      船      船     飞机
预测:       猫      船     飞机     飞机
网络对10000张测试图像的准确性: 58 %
准确率    飞机 : 66 %
准确率    汽车 : 68 %
准确率     鸟 : 59 %
准确率     猫 : 43 %
准确率     鹿 : 50 %
准确率     狗 : 51 %
准确率    青蛙 : 69 %
准确率     马 : 58 %
准确率     船 : 63 %
准确率    卡车 : 48 %

进程已结束，退出代码为 0