将AlexNet的输入层改为适合list数据的代码并定义损失函数以及真实标签
时间: 2023-12-03 09:44:13 浏览: 78
alexnet-代码+数据集
假设我们有一个列表数据,每个元素都是一个长度为10的向量。我们可以将AlexNet的输入层改为:
```python
import torch.nn as nn
class AlexNetList(nn.Module):
def __init__(self):
super(AlexNetList, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, 10) # 10 classes
)
def forward(self, x):
x = torch.tensor(x).unsqueeze(0).unsqueeze(0).repeat(1, 3, 1, 1) # convert to 3-channel grayscale image
x = self.features(x)
x = self.avgpool(x)
x = x.view(x.size(0), 256 * 6 * 6)
x = self.classifier(x)
return x
```
对于损失函数,我们可以选择交叉熵损失函数:
```python
loss_fn = nn.CrossEntropyLoss()
```
真实标签可以是一个整数张量,其大小等于批次大小,每个元素都是类别标签。例如,如果我们有一个大小为4的批次,每个元素都是长度为10的向量,则可以将真实标签定义为:
```python
true_labels = torch.tensor([2, 6, 1, 9])
```
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