class Net(nn.Module):
    def __init__(self):
        super().__init__()
        #(input channel, output channel, kenel size)
        #channel is a dimension of a tensor which is a container that can house data in N dimensions (matrices)
        self.conv1 = nn.Conv2d(3, 6, 5) 
        #shrink the image stack by pooling(kernel size, stride(shift)) and take max value per window
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        #TODO: add conv3
        self.conv3 = nn.Conv2d(16, 32, 5)
        #drop layer deletes 20% of the feautures to help prevent overfitting
        self.drop = nn.Dropout2d(p=0.2)
        #linear predicts the output as a linear function of inputs 
        #(output channels, height, width, batch size
        #TODO:
        self.fc1 = nn.Linear(16 * 16 * 5, 120)
        #TODO:
        self.fc1_5 = nn.Linear()
        #layer(size of input, size of output)
        #Linear layer=Fully connected layer
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        #F.ReLUs change negative values to 0. Apply to all stack of images.
        #they are activation functions. We apply it after each liner layer. 
        #only used in hidden layers. 
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        #Select some feautures to drop after 3rd conv to prevent overfitting
        x = self.drop(F.relu(self.conv3(x)))
        x = torch.flatten(x, 1) # flatten all dimensions except batch into 1-D
        x = F.relu(self.fc1(x))
        #TODO: add fc1_5
        x = F.relu(self.fc1_5(x)) 
        x = F.relu(self.fc2(x))
        #Feed to Fully connected layer to predict class
        x = self.fc3(x) # no relu b/c it's a last layer. 
        return x

我使用的是CIFAR 10中的图像，大小为3x 32 x32。当我之前运行代码时，它停止了，因为self.fc1线性层大小不适用于我添加的self.conv3。
我也不知道该为self.fc1_5写些什么。有人能给我解释一下这是如何工作的以及解决方案吗？谢谢！
我添加了一个额外的卷积层，您可以看到它是self.conv3 = nn.Conv2d（16，32，5）。TODO下面的行是我卡住的地方。我将该行更新为：fc 1 = nn.线性（16 * 16 * 5，120）之前，它是：fc 1 = nn.线性（16 * 5 * 5，120）。