net = nn.Sequential(nn.Conv2d(3,96,11,stride=4), nn.ReLU(), nn.MaxPool2d(stride=2), nn.Conv2d(48,256,5,padding=2,stride=1), nn.ReLU(), nn.MaxPool2d(stride=2), nn.Conv2d(256,384,3,padding=1,stride=1), nn.ReLU(), nn.Conv2d(192,384,3,padding=1,stride=1), nn.ReLU(), nn.Conv2d(192,256,3,padding=1,stride=1), nn.ReLU(), nn.MaxPool2d(stride=2) )检查一下对不对

这段代码有一个问题，第二个卷积层中输入通道数应该为256而不是48。可以将代码修改为：

net = nn.Sequential(
    nn.Conv2d(3, 96, 11, stride=4),
    nn.ReLU(),
    nn.MaxPool2d(stride=2),
    nn.Conv2d(96, 256, 5, padding=2, stride=1),
    nn.ReLU(),
    nn.MaxPool2d(stride=2),
    nn.Conv2d(256, 384, 3, padding=1, stride=1),
    nn.ReLU(),
    nn.Conv2d(384, 384, 3, padding=1, stride=1),
    nn.ReLU(),
    nn.Conv2d(384, 256, 3, padding=1, stride=1),
    nn.ReLU(),
    nn.MaxPool2d(stride=2)
)

这样修改后，第二个卷积层中的输入通道数就是256了。

相关问题

class TPCNN(nn.Module): def __init__(self, num_class=10, head_payload=False): super(TPCNN, self).__init__() # 上 self.uconv1 = nn.Sequential( # nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(16, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.uconv2 = nn.Sequential( # nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(32, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) # 中 self.mconv1 = nn.Sequential( # nn.Conv2d(1, 32, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(32, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) # 下 self.dconv1 = nn.Sequential( # nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(32, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), nn.MaxPool2d(kernel_size=2) ) self.uconv3 = nn.Sequential( # nn.Conv2d(96, 128, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(128, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.mconv2 = nn.Sequential( # nn.Conv2d(96, 128, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(128, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.dconv2 = nn.Sequential( # nn.Conv2d(96, 128, kernel_size=3, stride=1, padding=1, dilation=1, bias=True), nn.BatchNorm2d(128, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.uconv4 = nn.Sequential( # nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, dilation=1, bias=True), nn.BatchNorm2d(512, eps=1e-05, momentum=0.9, affine=True), nn.ReLU(), ) self.globalconv1 = nn.Sequential( nn.Conv2d(896, 1024, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(1024, eps=1e-05, momentum=0.9, affine=True), nn.ReLU() ) self.dmaxpool = nn.MaxPool2d(kernel_size=2,padding=1) # self.lstm1 = nn.LSTM(256,512, 2) # self.lstm2 = nn.LSTM(self.i_size*2,self.i_size*2, 2) self.avpool = nn.AdaptiveAvgPool2d(2) # self.globallstm = nn.LSTM(512, 256, 1) self.fc1 = nn.Linear(1024*2*2, 512) self.fc2 = nn.Linear(512, num_class)

这段代码定义了一个名为TPCNN的类，继承自nn.Module。它具有一个num_class参数，默认值为10，以及一个head_payload参数，其默认值为False。在__init__函数中，它首先调用父类的构造函数，然后定义了该类的其余属性和方法。

如果卷积情况设计如下： def __init__(self): super(CNN, self).__init__() self.layer1 = nn.Sequential( nn.Conv2d(1, 25, kernel_size=3),#卷积核的维度大小 nn.BatchNorm2d(25), nn.ReLU(inplace=True), # nn.Sigmoid() ) self.layer2 = nn.Sequential( nn.MaxPool2d(kernel_size=2, stride=2)#池化操作，核为2，步长也为 ) self.layer3 = nn.Sequential( nn.Conv2d(25, 50, kernel_size=3), nn.BatchNorm2d(50), nn.ReLU(inplace=True) # nn.Sigmoid() ) self.layer4 = nn.Sequential( nn.MaxPool2d(kernel_size=2, stride=2) ) self.layer5=nn.Sequential( nn.Conv2d(50, 75, kernel_size=3), nn.BatchNorm2d(75), nn.ReLU(inplace=True) ) self.layer6 = nn.Sequential( nn.MaxPool2d(kernel_size=2, stride=2) )，请问全连接层应该如何设计？

这是一个使用 PyTorch 定义的卷积神经网络 (CNN) 的类。在类的初始化函数中，使用了 nn.Sequential 来定义第一层，这一层包含一个 2D 卷积层，其中输入通道数为 1，输出通道数为 25，卷积核的尺寸为 3x3。