pytorch 入门（二） cnn 手写数字识别

import torch
import torch.nn as nn
import torchvision.datasets as normal_datasets
import torchvision.transforms as transforms
from torch.autograd import Variable

num_epochs = 1
batch_size = 100
learning_rate = 0.001

# 将数据处理成Variable, 如果有GPU, 可以转成cuda形式
def get_variable(x):
    x = Variable(x)
    return x.cuda() if torch.cuda.is_available() else x

# 从torchvision.datasets中加载一些常用数据集
train_dataset = normal_datasets.MNIST(
                            root='./mnist/',                 # 数据集保存路径
                            train=True,                      # 是否作为训练集
                            transform=transforms.ToTensor(), # 数据如何处理, 可以自己自定义
                            download=True)                   # 路径下没有的话, 可以下载

# 见数据加载器和batch
test_dataset = normal_datasets.MNIST(root='./mnist/',
                           train=False,
                           transform=transforms.ToTensor())

train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=False)


# 两层卷积
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        # 使用序列工具快速构建
        self.conv1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.conv2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.fc = nn.Linear(7 * 7 * 32, 10)

    def forward(self, x):
        out = self.conv1(x)
        out = self.conv2(out)
        out = out.view(out.size(0), -1)  # reshape
        out = self.fc(out)
        return out


cnn = CNN()
if torch.cuda.is_available():
    cnn = cnn.cuda()
    
# 选择损失函数和优化方法
loss_func = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)

for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = get_variable(images)
        labels = get_variable(labels)

        outputs = cnn(images)
        loss = loss_func(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i + 1) % 100 == 0:
            print('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f'
                  % (epoch + 1, num_epochs, i + 1, len(train_dataset) // batch_size, loss.data[0]))

# 测试模型
cnn.eval()  # 改成测试形态, 应用场景如: dropout
correct = 0
total = 0
for images, labels in test_loader:
    images = get_variable(images)
    labels = get_variable(labels)

    outputs = cnn(images)
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)
    correct += (predicted == labels.data).sum()

print(' 测试 准确率: %d %%' % (100 * correct / total))

# Save the Trained Model
torch.save(cnn.state_dict(), 'cnn.pkl')