def forward(self, x): # Max pooling over a (2, 2) window x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) # If the size is a square you can only specify a single number x = F.max_pool2d(F.relu(self.conv2(x)), 2) x = x.view(-1, self.num_flat_features(x)) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x
时间: 2024-01-03 22:04:31 浏览: 31
这段代码定义了神经网络的前向传播方法,用于数据在网络中的流动。
- `x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))`:这一行代码先将输入x通过卷积层self.conv1,然后应用ReLU激活函数,最后通过2x2的最大池化层进行下采样。
- `x = F.max_pool2d(F.relu(self.conv2(x)), 2)`:这一行代码先将上一步的输出x通过卷积层self.conv2,然后应用ReLU激活函数,最后再次通过2x2的最大池化层进行下采样。
- `x = x.view(-1, self.num_flat_features(x))`:这一行代码将上一步的输出x通过view函数进行形状变换,将其转换为一个二维张量。
- `x = F.relu(self.fc1(x))`:这一行代码先将上一步的输出x通过全连接层self.fc1,然后应用ReLU激活函数。
- `x = F.relu(self.fc2(x))`:这一行代码先将上一步的输出x通过全连接层self.fc2,然后应用ReLU激活函数。
- `x = self.fc3(x)`:这一行代码将上一步的输出x通过全连接层self.fc3,得到最终的输出。
整个前向传播过程就是将输入数据从卷积层、激活函数、池化层、全连接层等不同层之间传递,并经过相应的操作和激活函数处理,最终得到输出结果x。
相关问题
请将如下的代码用图片的形式表现出来 class Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = torch.nn.Conv2d(3, 64, kernel_size=3, padding=1) self.conv2 = torch.nn.Conv2d(64, 64, kernel_size=3, padding=1) self.conv3 = torch.nn.Conv2d(64, 128, kernel_size=3, padding=1) self.conv4 = torch.nn.Conv2d(128, 128, kernel_size=3, padding=1) self.conv5 = torch.nn.Conv2d(128, 256, kernel_size=3, padding=1) self.conv6 = torch.nn.Conv2d(256, 256, kernel_size=3, padding=1) self.maxpooling = torch.nn.MaxPool2d(2) self.avgpool = torch.nn.AvgPool2d(2) self.globalavgpool = torch.nn.AvgPool2d((8, 10)) self.bn1 = torch.nn.BatchNorm2d(64) self.bn2 = torch.nn.BatchNorm2d(128) self.bn3 = torch.nn.BatchNorm2d(256) self.dropout50 = torch.nn.Dropout(0.5) self.dropout10 = torch.nn.Dropout(0.1) self.fc1 = torch.nn.Linear(256, 40) def forward(self, x): batch_size = x.size(0) x = self.bn1(F.relu(self.conv1(x))) x = self.bn1(F.relu(self.conv2(x))) x = self.maxpooling(x) x = self.dropout10(x) x = self.bn2(F.relu(self.conv3(x))) x = self.bn2(F.relu(self.conv4(x))) x = self.maxpooling(x) x = self.dropout10(x) x = self.bn3(F.relu(self.conv5(x))) x = self.bn3(F.relu(self.conv6(x))) x = self.globalavgpool(x) x = self.dropout50(x) x = x.view(batch_size, -1) x = self.fc1(x) return x
很抱歉,我不能直接插入图片,但是我可以为您提供代码的格式化版本:
```
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.conv2 = torch.nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.conv3 = torch.nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv4 = torch.nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.conv5 = torch.nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.conv6 = torch.nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.maxpooling = torch.nn.MaxPool2d(2)
self.avgpool = torch.nn.AvgPool2d(2)
self.globalavgpool = torch.nn.AvgPool2d((8, 10))
self.bn1 = torch.nn.BatchNorm2d(64)
self.bn2 = torch.nn.BatchNorm2d(128)
self.bn3 = torch.nn.BatchNorm2d(256)
self.dropout50 = torch.nn.Dropout(0.5)
self.dropout10 = torch.nn.Dropout(0.1)
self.fc1 = torch.nn.Linear(256, 40)
def forward(self, x):
batch_size = x.size(0)
x = self.bn1(F.relu(self.conv1(x)))
x = self.bn1(F.relu(self.conv2(x)))
x = self.maxpooling(x)
x = self.dropout10(x)
x = self.bn2(F.relu(self.conv3(x)))
x = self.bn2(F.relu(self.conv4(x)))
x = self.maxpooling(x)
x = self.dropout10(x)
x = self.bn3(F.relu(self.conv5(x)))
x = self.bn3(F.relu(self.conv6(x)))
x = self.globalavgpool(x)
x = self.dropout50(x)
x = x.view(batch_size, -1)
x = self.fc1(x)
return x
```
def __init__(self): super(Net, self).__init__() self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5) self.conv1 = torch.nn.Conv2d(10, 20, kernel_size=5) self.pooling = torch.nn.MaxPool2d(2) self.Fc = torch.nn.Linear(320, 10)有什么错误码
这段代码存在一个错误:第二个卷积层的定义应该是`self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)`而不是`self.conv1 = torch.nn.Conv2d(10, 20, kernel_size=5)`,因为第一个卷积层已经使用了`self.conv1`的名称。这会导致第二个卷积层覆盖掉第一个卷积层的定义,导致模型结构发生错误。正确的代码如下:
```
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
self.pooling = torch.nn.MaxPool2d(2)
self.Fc = torch.nn.Linear(320, 10)
```
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