写一个神经网络训练获取结构光检测时投影的最优灰度的代码
时间: 2024-02-23 17:58:08 浏览: 18
好的,以下是一个使用PyTorch编写的神经网络训练获取结构光检测时投影的最优灰度的简单代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
# 定义一个卷积神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 1)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = self.conv2(x)
x = nn.functional.relu(x)
x = nn.functional.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
return x
# 定义训练函数
def train(model, optimizer, criterion, train_loader, device):
model.train()
running_loss = 0.0
for i, (inputs, targets) in enumerate(train_loader):
inputs = inputs.to(device)
targets = targets.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
running_loss += loss.item()
return running_loss / len(train_loader)
# 定义测试函数
def test(model, criterion, test_loader, device):
model.eval()
running_loss = 0.0
with torch.no_grad():
for i, (inputs, targets) in enumerate(test_loader):
inputs = inputs.to(device)
targets = targets.to(device)
outputs = model(inputs)
loss = criterion(outputs, targets)
running_loss += loss.item()
return running_loss / len(test_loader)
# 定义数据集和加载器
class GrayDataset(torch.utils.data.Dataset):
def __init__(self, x, y):
self.x = x
self.y = y
def __getitem__(self, index):
return self.x[index], self.y[index]
def __len__(self):
return len(self.x)
x = np.random.rand(100, 1, 28, 28).astype(np.float32)
y = np.random.rand(100, 1).astype(np.float32)
dataset = GrayDataset(x, y)
train_loader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=True)
# 定义训练参数
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Net().to(device)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()
# 训练模型
epochs = 10
for epoch in range(epochs):
train_loss = train(model, optimizer, criterion, train_loader, device)
test_loss = test(model, criterion, train_loader, device)
print("Epoch: {}/{} | Train Loss: {:.4f} | Test Loss: {:.4f}".format(epoch+1, epochs, train_loss, test_loss))
```
这是一个简单的示例,您可以根据自己的实际需求进行修改和扩展。希望对您有所帮助!