给个基于pytorch的使用卷积神经网络的超声波无损检测的代码示例
时间: 2024-12-15 22:16:08 浏览: 22
在PyTorch中,我们可以使用卷积神经网络(Convolutional Neural Networks,简称CNN)对超声波图像数据进行无损检测。下面是一个简单的例子,展示如何构建一个基础的CNN模型用于二分类任务,比如缺陷检测:
```python
import torch
import torch.nn as nn
import torch.optim as optim
class SimpleUltrasonicCNN(nn.Module):
def __init__(self):
super(SimpleUltrasonicCNN, self).__init__()
# 卷积层
self.conv1 = nn.Conv2d(in_channels=1, out_channels=8, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3, padding=1)
# 池化层
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
# 全连接层
self.fc1 = nn.Linear(16 * 4 * 4, 64) # 假设输入图片大小为256x256,经过两次池化后变成16x16
self.fc2 = nn.Linear(64, 2) # 输出层有两个节点,对应两个类别(无缺陷、有缺陷)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 4 * 4) # 将特征图展平成一维向量
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# 创建模型实例
model = SimpleUltrasonicCNN()
# 假设我们有随机生成的数据 (batch_size, channels, height, width)
data = torch.randn((32, 1, 256, 256))
target = torch.randint(0, 2, (32,))
criterion = nn.CrossEntropyLoss() # 二分类交叉熵损失函数
optimizer = optim.Adam(model.parameters(), lr=0.001) # 使用Adam优化器
# 训练步骤
for epoch in range(num_epochs): # 假设num_epochs是训练轮数
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
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