TypeError: __init__() missing 1 required positional argument: 'kernel_size'
时间: 2023-08-18 10:20:03 浏览: 67
这个错误通常是因为你没有在初始化卷积层对象时提供必需的参数之一,即卷积核的大小 `kernel_size`。在初始化时,你需要指定卷积核的大小,例如 `nn.Conv2d(in_channels, out_channels, kernel_size)`。请确保你在初始化时提供了所有必需的参数。
例如,如果你的代码是这样的:
```
import torch.nn as nn
class MyNet(nn.Module):
def __init__(self):
super(MyNet, self).__init__()
self.conv1 = nn.Conv2d(3, 16)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3)
self.fc1 = nn.Linear(32 * 6 * 6, 64)
self.fc2 = nn.Linear(64, 10)
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 = x.view(-1, 32 * 6 * 6)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
return x
```
你需要将 `self.conv1 = nn.Conv2d(3, 16)` 改为 `self.conv1 = nn.Conv2d(3, 16, kernel_size=3)`,以指定卷积核的大小,例如:
```
import torch.nn as nn
class MyNet(nn.Module):
def __init__(self):
super(MyNet, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3)
self.fc1 = nn.Linear(32 * 6 * 6, 64)
self.fc2 = nn.Linear(64, 10)
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 = x.view(-1, 32 * 6 * 6)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
return x
```
这样就能够解决这个错误了。