使用 PyTorch 构建神经网络模型 - 示例代码

import torch from torch import nn import numpy as np

def myfunc(x): try: data = np.loadtxt('data.txt', delimiter=',') except IOError as error: print('Error: can't read the file. Details: ', error) x_data = data[:, 1] y_data = data[:, 0]

# 创建神经网络模型
model = nn.Sequential(
            nn.Linear(1, 32),
            nn.ReLU(),
            nn.Linear(32, 32),
            nn.ReLU(),
            nn.Linear(32, 1)
        )

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())

# 将数据转化为tensor
x_data = torch.from_numpy(x_data).float()
y_data = torch.from_numpy(y_data).float()

# 训练模型
for epoch in range(100):
    optimizer.zero_grad()
    outputs = model(x_data)
    loss = criterion(outputs, y_data)
    loss.backward()
    optimizer.step()

# 使用模型进行预测
x = torch.from_numpy(np.array(x)).float()
y = model(x)
return y.detach().numpy()