cd 下载cd pycharm-2024.2.1cd bin

sh ./pycharm.sh

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

1)测试集图片，包含10000张图。t10k-images.idx3-ubyte2)标签集，包含10000张测试集图片所对应的标签。t10k-labels.idx1-ubyte3)训练集和验证集，包含55000张训练集和5000张验证集图片。train-images.idx3-ubyte4)标签集，包含训练集图片所对应的标签。train-labels.idx1-ubyte

import torchimport numpy as npfrom matplotlib import pyplot as pltfrom torch.utils.data import DataLoaderfrom torchvision import transformsfrom torchvision import datasetsimport torch.nn.functional as F
"""卷积运算 使用mnist数据集，和10-4，11类似的，只是这里：1.输出训练轮的acc 2.模型上使用torch.nn.Sequential"""
batch_size = 64learning_rate = 0.01momentum = 0.5EPOCH = 10

transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])# softmax归一化指数函数,其中0.1307是mean均值和0.3081是std标准差
train_dataset = datasets.MNIST(root='./data/mnist', train=True, transform=transform,download=True)  # 本地没有就加上download=Truetest_dataset = datasets.MNIST(root='./data/mnist', train=False, transform=transform,download=True)  # train=True训练集，=False测试集train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
fig = plt.figure()for i in range(12):    plt.subplot(3, 4, i+1)    plt.tight_layout()    plt.imshow(train_dataset.train_data[i], cmap='gray', interpolation='none')    plt.title("Labels: {}".format(train_dataset.train_labels[i]))    plt.xticks([])    plt.yticks([])plt.show()
# 训练集乱序，测试集有序
class Net(torch.nn.Module):    def __init__(self):        super(Net, self).__init__()        self.conv1 = torch.nn.Sequential(            torch.nn.Conv2d(1, 10, kernel_size=5),            torch.nn.ReLU(),            torch.nn.MaxPool2d(kernel_size=2),        )        self.conv2 = torch.nn.Sequential(            torch.nn.Conv2d(10, 20, kernel_size=5),            torch.nn.ReLU(),            torch.nn.MaxPool2d(kernel_size=2),        )        self.fc = torch.nn.Sequential(            torch.nn.Linear(320, 50),            torch.nn.Linear(50, 10),        )
    def forward(self, x):        batch_size = x.size(0)        x = self.conv1(x)  # 一层卷积层,一层池化层,一层激活层(图是先卷积后激活再池化，差别不大)        x = self.conv2(x)  # 再来一次        x = x.view(batch_size, -1)  # flatten 变成全连接网络需要的输入 (batch, 20,4,4) ==> (batch,320), -1 此处自动算出的是320        x = self.fc(x)        return x  # 最后输出的是维度为10的，也就是（对应数学符号的0~9）
model = Net()# Construct loss and optimizer ------------------------------------------------------------------------------criterion = torch.nn.CrossEntropyLoss()  # 交叉熵损失optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)  # lr学习率，momentum冲量

# Train and Test CLASS --------------------------------------------------------------------------------------# 把单独的一轮一环封装在函数类里
def train(epoch):    running_loss = 0.0  # 这整个epoch的loss清零    running_total = 0    running_correct = 0    for batch_idx, data in enumerate(train_loader, 0):        inputs, target = data        optimizer.zero_grad()
        # forward + backward + update        outputs = model(inputs)        loss = criterion(outputs, target)
        loss.backward()        optimizer.step()
        # 把运行中的loss累加起来，为了下面300次一除        running_loss += loss.item()        # 把运行中的准确率acc算出来        _, predicted = torch.max(outputs.data, dim=1)        running_total += inputs.shape[0]        running_correct += (predicted == target).sum().item()
        if batch_idx % 300 == 299:  # 不想要每一次都出loss，浪费时间，选择每300次出一个平均损失,和准确率            print('[%d, %5d]: loss: %.3f , acc: %.2f %%'                  % (epoch + 1, batch_idx + 1, running_loss / 300, 100 * running_correct / running_total))            running_loss = 0.0  # 这小批300的loss清零            running_total = 0            running_correct = 0  # 这小批300的acc清零
        # torch.save(model.state_dict(), './model_Mnist.pth')        # torch.save(optimizer.state_dict(), './optimizer_Mnist.pth')
def test():    correct = 0    total = 0    with torch.no_grad():  # 测试集不用算梯度        for data in test_loader:            images, labels = data            outputs = model(images)            _, predicted = torch.max(outputs.data, dim=1)  # dim = 1 列是第0个维度，行是第1个维度，沿着行(第1个维度)去找1.最大值和2.最大值的下标            total += labels.size(0)  # 张量之间的比较运算            correct += (predicted == labels).sum().item()    acc = correct / total    print('[%d / %d]: Accuracy on test set: %.1f %% ' % (epoch+1, EPOCH, 100 * acc))  # 求测试的准确率，正确数/总数    return acc# Start train and Test --------------------------------------------------------------------------------------if __name__ == '__main__':    acc_list_test = []    for epoch in range(EPOCH):        train(epoch)        # if epoch % 10 == 9:  #每训练10轮 测试1次        acc_test = test()        acc_list_test.append(acc_test)
    plt.plot(acc_list_test)    plt.xlabel('Epoch')    plt.ylabel('Accuracy On TestSet')    plt.show()