一维卷积cnn-lstm pytorch预测刀具磨损量模型代码

以下是一个简单的一维卷积CNN-LSTM PyTorch模型代码，用于预测刀具磨损量。这个模型由一维卷积层和LSTM层组成，用于处理时间序列数据。

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

class ConvLSTM(nn.Module):

    def __init__(self, input_size, hidden_size, kernel_size, num_layers, dropout):
        super(ConvLSTM, self).__init__()

        self.hidden_size = hidden_size
        self.num_layers = num_layers

        self.conv = nn.Conv1d(input_size, input_size, kernel_size, padding=(kernel_size - 1) // 2)
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, dropout=dropout, batch_first=True)
        self.linear = nn.Linear(hidden_size, 1)

    def forward(self, x):
        x = self.conv(x)
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.linear(out[:, -1, :])
        return out

# Hyperparameters
input_size = 1
hidden_size = 64
kernel_size = 3
num_layers = 2
dropout = 0.2
lr = 0.001
num_epochs = 100

# Model, Loss and Optimizer
model = ConvLSTM(input_size, hidden_size, kernel_size, num_layers, dropout).to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# Train the model
for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_loader):
        inputs = inputs.to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(inputs)
        loss = criterion(outputs, labels)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    # Print the loss
    if (epoch+1) % 10 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# Test the model
with torch.no_grad():
    correct = 0
    total = 0
    for inputs, labels in test_loader:
        inputs = inputs.to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(inputs)

        # Calculate the loss
        loss = criterion(outputs, labels)

        total += labels.size(0)
        correct += (abs(outputs - labels) <= 0.1).sum().item()

    print('Test Accuracy of the model on the test data: {} %'.format(100 * correct / total))

在训练之前，你需要准备你的数据，并将其转换为PyTorch张量格式。你可以使用PyTorch的DataLoader类来批量加载数据。在上面的代码中，我们使用均方误差损失函数和Adam优化器来训练模型。最后，我们在测试集上评估了模型的准确性。