def forward(self, x): x = self.conv1(x) x = self.relu(x) x = self.maxpool1(x) x = self.conv2(x) x = self.maxpool2(x) x = x.view(-1, 16*5*5) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) output = F.log_softmax(x, dim=1) return output
时间: 2024-01-15 21:04:12 浏览: 23
这段代码定义了Net类的前向传播函数forward(),用于定义神经网络的数据流向。
在forward()函数中,我们首先将输入x传入卷积层self.conv1中进行卷积运算,然后通过ReLU激活函数self.relu进行非线性变换,再通过最大池化层self.maxpool1进行下采样。接着,我们将经过第一层卷积和下采样后的特征图作为输入传入第二层卷积层self.conv2中进行卷积运算,然后同样通过最大池化层self.maxpool2进行下采样。
在进行完卷积和下采样操作后,我们将特征图展开成一维向量,并通过全连接层self.fc1进行线性变换,然后再通过ReLU激活函数进行非线性变换。接着,我们再通过全连接层self.fc2进行线性变换,再通过ReLU激活函数进行非线性变换,最后通过全连接层self.fc3进行线性变换得到最终的输出结果。
在得到输出结果后,我们通过log_softmax()函数对输出结果进行处理,以便于后续的损失计算。最终,我们将处理后的输出结果作为函数返回值。
相关问题
class CNNmodel(nn.Module): def __init__(self): super(CNNmodel, self).__init__() self.conv1d = nn.Conv1d(1, 64, kernel_size=2) self.relu = nn.ReLU(inplace=True) self.Linear1 = nn.Linear(64*75, 50) self.Linear2 = nn.Linear(50, 1) def forward(self, x): x = self.conv1d(x) x = self.relu(x) x = x.view(-1) x = self.Linear1(x) x = self.relu(x) x = self.Linear2(x) return x报错
这段代码存在一些问题,导致运行时报错。以下是可能出现的问题及解决方法:
1. `x = x.view(-1)`这一行应该改为`x = x.view(-1, 64*75)`,因为在前面`self.conv1d`的输出通道数为64,而输出的feature map长度为75,需要将其展平后输入全连接层。
2. `self.Linear2 = nn.Linear(50, 1)`中的1代表输出的类别数,如果是二分类问题,应该改为`self.Linear2 = nn.Linear(50, 2)`,并在模型的输出处加上softmax激活函数。
修改后的代码如下:
```
class CNNmodel(nn.Module):
def __init__(self):
super(CNNmodel, self).__init__()
self.conv1d = nn.Conv1d(1, 64, kernel_size=2)
self.relu = nn.ReLU(inplace=True)
self.Linear1 = nn.Linear(64*75, 50)
self.Linear2 = nn.Linear(50, 2)
def forward(self, x):
x = self.conv1d(x)
x = self.relu(x)
x = x.view(-1, 64*75)
x = self.Linear1(x)
x = self.relu(x)
x = self.Linear2(x)
x = F.softmax(x, dim=1)
return x
```
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
```