你可以帮忙写一个pytorch框架下的3DCNN网络,用来依次读取.mat格式的高光谱数据块进行模型训练。代码添加详细的中文注释。
时间: 2023-05-23 19:00:38 浏览: 185
pytorch框架YOLOv3在Visdrone开源数据集的训练权重和代码
当然可以,下面是一个示例代码:
```python
import torch
import torch.nn as nn
class Spectral3DCNN(nn.Module):
def __init__(self, in_channels=1, num_classes=2):
super(Spectral3DCNN, self).__init__()
self.conv_layer1 = nn.Sequential(
nn.Conv3d(in_channels, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm3d(32),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=2, stride=2)
)
self.conv_layer2 = nn.Sequential(
nn.Conv3d(32, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm3d(64),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=2, stride=2)
)
self.conv_layer3 = nn.Sequential(
nn.Conv3d(64, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm3d(128),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=2, stride=2)
)
self.conv_layer4 = nn.Sequential(
nn.Conv3d(128, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm3d(256),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=2, stride=2)
)
self.linear_layer = nn.Sequential(
nn.Linear(20*20*256, 2048),
nn.ReLU(inplace=True),
nn.Linear(2048, num_classes)
)
def forward(self, x):
out = self.conv_layer1(x)
out = self.conv_layer2(out)
out = self.conv_layer3(out)
out = self.conv_layer4(out)
out = out.reshape(out.size(0), -1)
out = self.linear_layer(out)
return out
```
上面的代码中,我们定义了一个名为Spectral3DCNN的3D CNN模型,该模型有四个卷积层和一个全连接层。此外,我们还定义了一个名为forward的函数,该函数接受一个输入张量并将其传递给模型的每一层。最后,我们返回模型输出的张量。
要读取.mat格式的高光谱数据块进行模型训练,您需要转换数据集格式为Tensor,例如下面的代码:
```python
import scipy.io as sio
import torch.utils.data as data_utils
mat_file = 'data_file.mat'
mat_data = sio.loadmat(mat_file) # 加载.mat格式数据
data = mat_data['data']
# 转换数据格式为Tensor
tensor_data = torch.tensor(data).unsqueeze(1)
tensor_labels = torch.tensor(labels)
# 划分数据集
train_dataset = data_utils.TensorDataset(tensor_data, tensor_labels)
train_loader = data_utils.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
```
上面的代码中,我们使用scipy库的loadmat函数从.mat文件中加载数据。然后,我们将数据转换为PyTorch张量,并使用TensorDataset和DataLoader将其划分为批次进行训练。
希望以上代码可以帮助您训练3DCNN网络。
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