从数据集中包含的值创建具有特定池的顺序网络。这个过程在“图像识别模块”中也得到了很好的应用。

以下步骤用于使用PyTorch创建卷积神经网络(Convents)的序列处理模型：

1、导入模块

导入必要的模块，以执行序列处理使用卷积神经网络(Convents)。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

2、数据转换与加载

使用下面的代码执行必要的操作，以相应的顺序创建一个模式：

# 参数配置
batch_size = 128
num_classes = 10
epochs = 12
img_rows, img_cols = 28, 28

# 数据转换与加载
transform = transforms.Compose([
     # 转换为 (C, H, W) 范围 [0,1]
    transforms.ToTensor(),  
     # 标准化 MNIST 的均值与方差
    transforms.Normalize((0.1307,), (0.3081,)) 
])

train_dataset = datasets.MNIST(root='./data', 
train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./data',
train=False, download=True, transform=transform)

train_loader = DataLoader(train_dataset,
batch_size=batch_size, shuffle=True, num_workers=2)
test_loader = DataLoader(test_dataset, 
batch_size=batch_size, shuffle=False, num_workers=2)

# 数据集信息打印
print(f'x_train shape: {train_dataset.data.shape} (raw tensor shape)')
print(f'{len(train_dataset)} train samples')
print(f'{len(test_dataset)} test samples')

3、训练模型

1）model

定义了一个用于图像分类的二维卷积神经网络（CNN），模型包含两个卷积层（用于提取特征）、两个全连接层（用于分类），并在中间使用 ReLU 激活、最大池化和 Dropout 进行特征压缩和正则化，适合处理如 MNIST 这类的灰度图像并输出 10 类分类结果。

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)  # 输入通道数为1
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        return x

model = CNN()

2）加载训练

通过 DataLoader 加载训练和测试数据集，然后将模型迁移到 GPU 或 CPU，使用 CrossEntropyLoss 和 Adadelta 优化器训练模型若干轮（epoch），在每轮中计算训练损失与准确率，最后在测试集上评估模型性能并输出测试损失与准确率。

rain_loader = DataLoader(train_dataset, batch_size=batch_size, 
shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size)

# 有一个已定义的 model，类别为分类任务
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# 损失函数和优化器
# 用于 one-hot 标签，记得 y_train 是整数编码
criterion = nn.CrossEntropyLoss()  
optimizer = optim.Adadelta(model.parameters())

# 训练模型
for epoch in range(epochs):
    model.train()
    running_loss = 0.0
    correct = 0
    total = 0

    for inputs, targets in train_loader:
        inputs, targets = inputs.to(device), targets.to(device)

        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        running_loss += loss.item() * inputs.size(0)
        _, predicted = torch.max(outputs, 1)
        correct += (predicted == targets).sum().item()
        total += targets.size(0)

    train_loss = running_loss / total
    train_acc = correct / total
    print(f"Epoch {epoch + 1}/{epochs} - ")
    print(f"Loss: {train_loss:.4f} - Accuracy: {train_acc:.4f}")

3）测试模型

# 测试模型
model.eval()
test_loss = 0.0
correct = 0
total = 0

with torch.no_grad():
    for inputs, targets in test_loader:
        inputs, targets = inputs.to(device), targets.to(device)
        outputs = model(inputs)
        loss = criterion(outputs, targets)

        test_loss += loss.item() * inputs.size(0)
        _, predicted = torch.max(outputs, 1)
        correct += (predicted == targets).sum().item()
        total += targets.size(0)

test_loss /= total
test_acc = correct / total

print("Test loss:", test_loss)
print("Test accuracy:", test_acc)