As the title says, if I change the number of hidden layers in my pytorch neural network to be anything different from the amount of input nodes it returns the error below.
RuntimeError: mat1 and mat2 shapes cannot be multiplied (380x10 and 2x10)
I think that the architecture is incorrectly coded but I am relatively new to pytorch and neural networks so I can't spot the mistake. Any help is greatly appreciated, I've included the code below
class FCN(nn.Module):def __init__(self, N_INPUT, N_OUTPUT, N_HIDDEN, N_LAYERS): super().__init__() activation = nn.Tanh self.fcs = nn.Sequential(*[ nn.Linear(N_INPUT, N_HIDDEN), activation()]) self.fch = nn.Sequential(*[ nn.Sequential(*[ nn.Linear(N_INPUT, N_HIDDEN), activation()]) for _ in range(N_LAYERS-1)]) self.fce = nn.Linear(N_INPUT, N_HIDDEN)def forward(self, x): x = self.fcs(x) x = self.fch(x) x = self.fce(x) return xtorch.manual_seed(123)pinn = FCN(2, 2, 10, 8)If the pinn architecture is defined as pinn = FCN(2, 2, 2, 8) no errors are returned but neural network does not perform well.
Other information:
- the input is a matrix tensor with a batch size of 380
Please let me know if you need anymore information and thank you!