# layers self.hidden_layer_1 = torch.nn.Linear(80,100) self.hidden_layer_2 = torch.nn.Linear(100,100) self.output = torch.nn.Linear(100,15)
def forward(self, input): H1 = self.hidden_layer_1(input) H1 = self.ReLU(H1) H2 = self.hidden_layer_2(H1) H2 = self.ReLU(H2) final_inputs = self.output(H2) # not applying activation on final_inputs because CrossEntropyLoss does that return final_inputs
I have a feed forward neural network with 80 input features, two hidden layers with 100 nodes each and 15 outputs(one for each class).
I save weights from a restricted boltzmann machine on tensor flow in a .pkl file.
How do I initialise the above code with these weights?