baseline_end2end.py 文件源码

def create_blstm(l_incoming, l_mask, hidden_units, cell_parameters, gate_parameters, name):

    if cell_parameters is None:
        cell_parameters = Gate()
    if gate_parameters is None:
        gate_parameters = Gate()

    l_lstm = LSTMLayer(
        l_incoming, hidden_units,
        # We need to specify a separate input for masks
        mask_input=l_mask,
        # Here, we supply the gate parameters for each gate
        ingate=gate_parameters, forgetgate=gate_parameters,
        cell=cell_parameters, outgate=gate_parameters,
        # We'll learn the initialization and use gradient clipping
        learn_init=True, grad_clipping=5., name='f_{}'.format(name))

    # The "backwards" layer is the same as the first,
    # except that the backwards argument is set to True.
    l_lstm_back = LSTMLayer(
        l_incoming, hidden_units, ingate=gate_parameters,
        mask_input=l_mask, forgetgate=gate_parameters,
        cell=cell_parameters, outgate=gate_parameters,
        learn_init=True, grad_clipping=5., backwards=True, name='b_{}'.format(name))

    return l_lstm, l_lstm_back