def forward()

in python/lltm.py [0:0]

• 11 lines of code
• 3 McCabe index (conditional complexity)

    def forward(self, input, state):
        old_h, old_cell = state
        X = torch.cat([old_h, input], dim=1)

        # Compute the input, output and candidate cell gates with one MM.
        gate_weights = F.linear(X, self.weights, self.bias)
        # Split the combined gate weight matrix into its components.
        gates = gate_weights.chunk(3, dim=1)

        input_gate = torch.sigmoid(gates[0])
        output_gate = torch.sigmoid(gates[1])
        # Here we use an ELU instead of the usual tanh.
        candidate_cell = F.elu(gates[2])

        # Compute the new cell state.
        new_cell = old_cell + candidate_cell * input_gate
        # Compute the new hidden state and output.
        new_h = torch.tanh(new_cell) * output_gate

        return new_h, new_cell