Dropout() takes 2 positional arguments but 3 were given
时间: 2023-10-31 22:44:54 浏览: 34
这个错误通常是因为在使用 Dropout 函数时传递了多余的参数。Dropout() 函数只需要一个参数,即要丢弃的神经元的比例。请检查你的代码,看看是否在 Dropout() 函数中传递了多余的参数。
另外,如果你在定义模型时使用了 Dropout 层,那么 Dropout 层应该是作为其他层的一部分进行定义的,而不是作为单独的函数调用。在这种情况下,你应该检查是否正确地定义了 Dropout 层,并且在调用该层时是否传递了正确的参数。
相关问题
Inception.__init__() missing 2 required positional arguments: 'ch5x5' and 'pool_proj'
非常抱歉,我刚才的代码有误,已经修正。以下是正确的代码:
```python
import torch.nn as nn
import torch.nn.functional as F
class Inception(nn.Module):
def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
super(Inception, self).__init__()
# 1x1 conv branch
self.branch1 = nn.Sequential(
nn.Conv2d(in_channels, ch1x1, kernel_size=1),
nn.BatchNorm2d(ch1x1),
nn.ReLU(inplace=True)
)
# 1x1 conv -> 3x3 conv branch
self.branch2 = nn.Sequential(
nn.Conv2d(in_channels, ch3x3red, kernel_size=1),
nn.BatchNorm2d(ch3x3red),
nn.ReLU(inplace=True),
nn.Conv2d(ch3x3red, ch3x3, kernel_size=3, padding=1),
nn.BatchNorm2d(ch3x3),
nn.ReLU(inplace=True)
)
# 1x1 conv -> 5x5 conv branch
self.branch3 = nn.Sequential(
nn.Conv2d(in_channels, ch5x5red, kernel_size=1),
nn.BatchNorm2d(ch5x5red),
nn.ReLU(inplace=True),
nn.Conv2d(ch5x5red, ch5x5, kernel_size=5, padding=2),
nn.BatchNorm2d(ch5x5),
nn.ReLU(inplace=True)
)
# 3x3 pool -> 1x1 conv branch
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
nn.Conv2d(in_channels, pool_proj, kernel_size=1),
nn.BatchNorm2d(pool_proj),
nn.ReLU(inplace=True)
)
def forward(self, x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
outputs = [branch1, branch2, branch3, branch4]
return torch.cat(outputs, 1)
class GoogLeNet(nn.Module):
def __init__(self):
super(GoogLeNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.inception1 = Inception(64, 64, 96, 128, 16, 32, 32)
self.inception2 = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.inception3 = Inception(480, 192, 96, 208, 16, 48, 64)
self.inception4 = Inception(512, 160, 112, 224, 24, 64, 64)
self.inception5 = Inception(512, 128, 128, 256, 24, 64, 64)
self.inception6 = Inception(512, 112, 144, 288, 32, 64, 64)
self.inception7 = Inception(528, 256, 160, 320, 32, 128, 128)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(p=0.4)
self.fc = nn.Linear(832, 10)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = self.maxpool(x)
x = self.inception1(x)
x = self.inception2(x)
x = self.maxpool(x)
x = self.inception3(x)
x = self.inception4(x)
x = self.inception5(x)
x = self.inception6(x)
x = self.inception7(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.dropout(x)
x = self.fc(x)
return x
```
这个GoogLeNet模型可以通过在CIFAR-10数据集上进行训练来实现图像分类任务。
nn.dropout2d
nn.Dropout2d是PyTorch中的一个函数,用于在卷积神经网络(CNN)中进行2D的随机失活操作。在CNN中,通过在每个训练批次中随机将部分神经元的输出置为零,Dropout2d可以有效地减少过拟合问题。具体而言,Dropout2d会在每个训练步骤中随机将输入张量的某些通道置为零,以此来增强模型的泛化能力。
与nn.Dropout不同,nn.Dropout2d是专门针对于2D输入的。它通常应用在卷积层之后,用于减少特征图中的冗余信息,提高模型的鲁棒性。
下面是nn.Dropout2d的一个例子:
```
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 50, 5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
```
在这个例子中,Dropout2d被应用在第二个卷积层之后,通过将部分通道的值置为零来减少特征图的冗余信息。这有助于模型更好地学习和泛化。
希望这个解释对您有帮助!如果您有任何其他问题,请随时提问。
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