Hi all,
I am a noob with pytorch, but I have been making neural network models in JMP for a while now.
I am trying to match the structure of JMP models in pytorch which have 10 input parameters, a single
tanh layer with 10 nodes, feeding a single continuous prediction value.
The final equation ends up being:
predicted_value = b + weight_n1 * [ tanh(0.5*(weight_p1parameter1 + weight_p2parameter2…)]
+ weight_n2 * [ tanh(0.5*(weight_p11* parameter1 + weight_p12*parameter2…)]
+ …
…and so on for 10 total nodes. I have tried to use:
def __init__(self, in_features, out_features):
    super().__init__()
    self.in_features = in_features
    self.out_features = out_features
    self.input_layer = torch.nn.Linear(self.in_features,10)
    self.hidden_layer = torch.nn.Linear(10, self.out_features)

def forward(self, x):
    out = self.input_layer(x)
    out = self.hidden_layer(torch.tanh(0.5*out))    
    
    return out

How do I set up the forward function for this? Any time I add any nonlinear activation function I get a single predicted value.
Thanks in advance!