Pytorch error mat1 and mat2 shapes cannot be multiplied in Autoencoder to compress images-CodePudding

I receive this error. Whereas the size of my input image is 3x120x120, so I flatten the image by the following code, however, I received this error:

mat1 and mat2 shapes cannot be multiplied (720x120 and 43200x512)

I have tu use an autoencoder to compress my images of a factor of 360 ( So i started from 3x120x120 input to 120 in the encoder).

my code:

class AE(torch.nn.Module):
    def __init__(self):
        super().__init__()
         
        
        self.encoder = torch.nn.Sequential(
            torch.nn.Linear(3*120*120, 512),
            torch.nn.ReLU(),
            torch.nn.Linear(512, 256),
            torch.nn.ReLU(),
            torch.nn.Linear(256, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 120)
        )
         
        
        self.decoder = torch.nn.Sequential(
            torch.nn.Linear(120, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 256),
            torch.nn.ReLU(),
            torch.nn.Linear(256, 512),
            torch.nn.ReLU(),
            torch.nn.Linear(512, 3*120*120),
            torch.nn.Sigmoid()
        )
 
    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

CodePudding user response：

I think you missed some basic characteristics of nn.Linear function. Its inputs are input and output channel dimension, respectively. And therefore only 1D input is allowable (considering batch, which is 3 in your case, it will be 2D in total ). Therefore you should first flatten your x for later compute compatibility.

class AE(torch.nn.Module):
    def __init__(self):
        super().__init__()
    
        self.encoder = torch.nn.Sequential(
            torch.nn.Linear(120*120, 512),
            torch.nn.ReLU(),
            torch.nn.Linear(512, 256),
            torch.nn.ReLU(),
            torch.nn.Linear(256, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 120)
        )
     
    
        self.decoder = torch.nn.Sequential(
            torch.nn.Linear(120, 128),
            torch.nn.ReLU(),
            torch.nn.Linear(128, 256),
            torch.nn.ReLU(),
            torch.nn.Linear(256, 512),
            torch.nn.ReLU(),
            torch.nn.Linear(512, 3*120*120),
            torch.nn.Sigmoid()
        )

    def forward(self, x):
        x = x.reshape(3, -1)
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

I recommend you to carefully search for the structure and dimensional change of common neural networks. It will help you alot for sure.