Oscar_Rangel:

[64 x 512],


Hi  @ptrblck, I have this values
inpt1 -> torch.Size([64, 512])
inpt2 -> torch.Size([64, 73728])
inpt3 -> torch.Size([64, 65536])
if I do this
class EnsembleModel(nn.Module):
    
    def __init__(self,out_size,training=True):
        super().__init__()
#         self.fc1 = nn.Linear(8192,512)
        self.fc2 = nn.Linear(131072,512)
        self.fc3 = nn.Linear(82944,512)
        self.fc4 = nn.Linear(512,out_size)

    def forward(self,inp1,inp2,inp3):
        print(inp1.shape)
        print(inp2.shape)
        print(inp3.shape)
        
#         out1 = self.fc1(F.dropout(inp1,training=self.training))
        out2 = self.fc2(F.dropout(inp2,training=self.training))
        out3 = self.fc3(F.dropout(inp3,training=self.training))
        out = out1 + out2 + out3
        out = self.fc4(F.dropout(out,training=self.training))
        return out

I get this:
torch.Size([64, 73728])
torch.Size([64, 65536])

RuntimeError: size mismatch, m1: [64 x 73728], m2: [131072 x 512] at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:283

no clue what is going on....