pytorch完成1DCNN对特征进行提取

首先，我们需要导入PyTorch和其他所需的模块。

import torch
import torch.nn as nn
import torch.nn.functional as F

接下来，我们可以定义我们的1D卷积神经网络模型。

class CNN1D(nn.Module):
    def __init__(self, input_size, output_size, kernel_size=3, stride=1, padding=1):
        super(CNN1D, self).__init__()
        self.conv1 = nn.Conv1d(input_size, 32, kernel_size=kernel_size, stride=stride, padding=padding)
        self.conv2 = nn.Conv1d(32, 64, kernel_size=kernel_size, stride=stride, padding=padding)
        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 125, 128)
        self.fc2 = nn.Linear(128, output_size)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 64 * 125)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

这个模型有两个卷积层，每个卷积层都有32和64个过滤器，分别用于提取32和64个特征。在每个卷积层之后，我们使用Max Pooling来减少输入的大小。然后，我们将数据展平并将其传递到两个全连接层中，以得到最终的输出。

接下来，我们可以定义我们的训练和测试函数。

def train(model, device, train_loader, optimizer, criterion):
    model.train()
    train_loss = 0
    correct = 0
    for data, target in train_loader:
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        pred = output.argmax(dim=1, keepdim=True)
        correct += pred.eq(target.view_as(pred)).sum().item()

    train_loss /= len(train_loader.dataset)
    accuracy = 100. * correct / len(train_loader.dataset)

    return train_loss, accuracy

def test(model, device, test_loader, criterion):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)

    return test_loss, accuracy

在这些函数中，我们使用PyTorch的优化器来更新模型参数，以最小化损失。我们还使用PyTorch的Cross Entropy损失作为我们的损失函数。

最后，我们可以训练我们的模型并测试其性能。

input_size = 1
output_size = 10
lr = 0.001
epochs = 10
batch_size = 64

train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False)

model = CNN1D(input_size, output_size).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
criterion = nn.CrossEntropyLoss()

for epoch in range(1, epochs + 1):
    train_loss, train_accuracy = train(model, device, train_loader, optimizer, criterion)
    test_loss, test_accuracy = test(model, device, test_loader, criterion)
    print('Epoch: {} \tTraining Loss: {:.6f} \tTraining Accuracy: {:.2f}% \tTest Loss: {:.6f} \tTest Accuracy: {:.2f}%'.format(
        epoch, train_loss, train_accuracy, test_loss, test_accuracy))

在训练和测试期间，我们使用PyTorch的DataLoader来加载数据，并将其传递给我们的模型。每个时期，我们计算训练和测试损失和准确性，并将其打印出来。