python/lltm.py [32:42]:
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        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
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python/lltm_baseline.py [33:40]:
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        gates = gate_weights.chunk(3, dim=1)

        input_gate = torch.sigmoid(gates[0])
        output_gate = torch.sigmoid(gates[1])
        candidate_cell = F.elu(gates[2])

        new_cell = old_cell + candidate_cell * input_gate
        new_h = torch.tanh(new_cell) * output_gate
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