能否帮我写一个神经网络降噪的例子

当然可以！这是一个基于PyTorch的神经网络降噪例子，用于处理噪声图像：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from PIL import Image

class Autoencoder(nn.Module):
    def __init__(self):
        super(Autoencoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 16, 3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(16, 32, 3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(32, 64, 3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 128, 3, stride=2, padding=1),
            nn.ReLU()
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 32, 4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(32, 16, 4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(16, 3, 4, stride=2, padding=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

# 载入噪声图像并转换为tensor
noisy_img = Image.open("noisy_image.jpg")
noisy_img = np.asarray(noisy_img)
noisy_img = noisy_img.transpose(2, 0, 1)
noisy_img = noisy_img.astype(np.float32) / 255.
noisy_img = torch.from_numpy(noisy_img)
noisy_img = noisy_img.unsqueeze(0)

# 创建模型及优化器
model = Autoencoder()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 50
for epoch in range(num_epochs):
    output = model(noisy_img)
    loss = nn.MSELoss()(output, noisy_img)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    if epoch % 10 == 0:
        print("Epoch [{}/{}], Loss: {:.4f}".format(epoch+1, num_epochs, loss.item()))

# 保存降噪后的图像
denoised_img = model(noisy_img).detach().numpy()[0]
denoised_img = (denoised_img * 255.).astype(np.uint8)
denoised_img = denoised_img.transpose(1, 2, 0)
denoised_img = Image.fromarray(denoised_img)
denoised_img.save("denoised_image.jpg")

这个例子使用了一个简单的自编码器模型，通过反向传播算法优化模型参数，从而降低图像噪声的影响。你可以将这个例子稍作修改，适用于你自己的数据集。