一维卷积神经网络光谱预测代码

我可以提供一段简单的一维卷积神经网络光谱预测代码示例：

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

# Define the one-dimensional convolutional neural network model
class SpectrumCNN(nn.Module):
    def __init__(self):
        super(SpectrumCNN, self).__init__()
        self.conv1d_1 = nn.Conv1d(1, 32, kernel_size=3, stride=1, padding=1)
        self.conv1d_2 = nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 50, 128)
        self.fc2 = nn.Linear(128, 1)
    
    def forward(self, x):
        x = x.unsqueeze(1)
        x = self.pool(nn.functional.relu(self.conv1d_1(x)))
        x = self.pool(nn.functional.relu(self.conv1d_2(x)))
        x = x.view(-1, 64 * 50)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Generate some synthetic data for training the model
X = np.random.rand(1000, 50)
y = np.sum(X, axis=1, keepdims=True)

# Convert the data to PyTorch tensors
X = torch.tensor(X, dtype=torch.float32)
y = torch.tensor(y, dtype=torch.float32)

# Create a SpectrumCNN model and an optimizer
model = SpectrumCNN()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Train the model for 100 epochs
for epoch in range(100):
    optimizer.zero_grad()
    output = model(X)
    loss = nn.functional.mse_loss(output, y)
    loss.backward()
    optimizer.step()
    print("Epoch %d, Loss=%.3f" % (epoch + 1, loss.item()))

# Test the model on some new data
X_test = np.random.rand(10, 50)
y_test = np.sum(X_test, axis=1, keepdims=True)
X_test = torch.tensor(X_test, dtype=torch.float32)
y_test = torch.tensor(y_test, dtype=torch.float32)
pred = model(X_test)
print("Predictions:", pred.detach().numpy().flatten())
print("True Values:", y_test.detach().numpy().flatten())

这段代码创建了一个名为 SpectrumCNN 的一维卷积神经网络模型，用于预测具有 50 个特征的光谱数据。模型包括两个卷积层和两个全连接层，用于将输入数据映射到输出数据。模型在 MSE 损失函数下进行训练，并使用 Adam 优化器来更新模型参数。最后，模型在一些新数据上进行测试，并将预测值与真实值打印出来。