pytorch回归的卷积神经网络

我正试图为回归的目的创建CNN。输入是图像数据。 为了便于学习，我有10个形状的图像

（10,3448448）

，其中10个是图像，3个是通道，448个是高度和宽度。
输出标签为

（10245）

。 这是我的架构

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=5)
        self.conv2 = nn.Conv2d(32, 32, kernel_size=5)
        self.conv3 = nn.Conv2d(32,64, kernel_size=5)
        self.fc1 = nn.Linear(3*3*64, 256)
        self.fc2 = nn.Linear(256, 245)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        #x = F.dropout(x, p=0.5, training=self.training)
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = F.dropout(x, p=0.5, training=self.training)
        x = F.relu(F.max_pool2d(self.conv3(x),2))
        x = F.dropout(x, p=0.5, training=self.training)
        x = x.view(-1,3*3*64 )
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return x

cnn = CNN()
print(cnn)

it = iter(train_loader)
X_batch, y_batch = next(it)
print(cnn.forward(X_batch).shape)

---

使用批量大小2，我希望模型生成的数据形状是

（2245）

。但它是在self之后生成形状数据的。这就是2592的来源。 更改行： self.fc1=nn.Linear（3*3*64256） 和 “x=x.view（-1,3*3*64）” 因此，他们在图层之后使用适当的图像大小

以下是固定代码：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=5)
        self.conv2 = nn.Conv2d(32, 32, kernel_size=5)
        self.conv3 = nn.Conv2d(32,64, kernel_size=5)
        self.fc1 = nn.Linear(108*108*64, 256)
        self.fc2 = nn.Linear(256, 245)

    def forward(self, x):
        print (x.shape)
        x = F.relu(self.conv1(x))
        print (x.shape)
        #x = F.dropout(x, p=0.5, training=self.training)
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        print (x.shape)
        x = F.dropout(x, p=0.5, training=self.training)
        print (x.shape)
        x = F.relu(F.max_pool2d(self.conv3(x),2))
        print (x.shape)
        x = F.dropout(x, p=0.5, training=self.training)
        print (x.shape)
        x = x.view(-1,108*108*64 )
        print (x.shape)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return x

cnn = CNN()
print(cnn)

# X_batch, y_batch = next(it)
print(cnn.forward(X_batch).shape)