networks.py 文件源码

def build_BiLSTM(incoming, num_units, mask=None, grad_clipping=0, precompute_input=True, peepholes=False, dropout=True,
                 in_to_out=False):
    # construct the forward and backward rnns. Now, Ws are initialized by Glorot initializer with default arguments.
    # Need to try other initializers for specific tasks.

    # dropout for incoming
    if dropout:
        incoming = lasagne.layers.DropoutLayer(incoming, p=0.5)

    ingate_forward = Gate(W_in=lasagne.init.GlorotUniform(), W_hid=lasagne.init.GlorotUniform(),
                          W_cell=lasagne.init.Uniform(range=0.1))
    outgate_forward = Gate(W_in=lasagne.init.GlorotUniform(), W_hid=lasagne.init.GlorotUniform(),
                           W_cell=lasagne.init.Uniform(range=0.1))
    # according to Jozefowicz et al.(2015), init bias of forget gate to 1.
    forgetgate_forward = Gate(W_in=lasagne.init.GlorotUniform(), W_hid=lasagne.init.GlorotUniform(),
                              W_cell=lasagne.init.Uniform(range=0.1), b=lasagne.init.Constant(1.))
    # now use tanh for nonlinear function of cell, need to try pure linear cell
    cell_forward = Gate(W_in=lasagne.init.GlorotUniform(), W_hid=lasagne.init.GlorotUniform(), W_cell=None,
                        nonlinearity=nonlinearities.tanh)
    lstm_forward = lasagne.layers.LSTMLayer(incoming, num_units, mask_input=mask, grad_clipping=grad_clipping,
                                            nonlinearity=nonlinearities.tanh, peepholes=peepholes,
                                            precompute_input=precompute_input,
                                            ingate=ingate_forward, outgate=outgate_forward,
                                            forgetgate=forgetgate_forward, cell=cell_forward, name='forward')

    ingate_backward = Gate(W_in=lasagne.init.GlorotUniform(), W_hid=lasagne.init.GlorotUniform(),
                           W_cell=lasagne.init.Uniform(range=0.1))
    outgate_backward = Gate(W_in=lasagne.init.GlorotUniform(), W_hid=lasagne.init.GlorotUniform(),
                            W_cell=lasagne.init.Uniform(range=0.1))
    # according to Jozefowicz et al.(2015), init bias of forget gate to 1.
    forgetgate_backward = Gate(W_in=lasagne.init.GlorotUniform(), W_hid=lasagne.init.GlorotUniform(),
                               W_cell=lasagne.init.Uniform(range=0.1), b=lasagne.init.Constant(1.))
    # now use tanh for nonlinear function of cell, need to try pure linear cell
    cell_backward = Gate(W_in=lasagne.init.GlorotUniform(), W_hid=lasagne.init.GlorotUniform(), W_cell=None,
                         nonlinearity=nonlinearities.tanh)
    lstm_backward = lasagne.layers.LSTMLayer(incoming, num_units, mask_input=mask, grad_clipping=grad_clipping,
                                             nonlinearity=nonlinearities.tanh, peepholes=peepholes,
                                             precompute_input=precompute_input, backwards=True,
                                             ingate=ingate_backward, outgate=outgate_backward,
                                             forgetgate=forgetgate_backward, cell=cell_backward, name='backward')

    # concatenate the outputs of forward and backward RNNs to combine them.
    concat = lasagne.layers.concat([lstm_forward, lstm_backward], axis=2, name="bi-lstm")

    # dropout for output
    if dropout:
        concat = lasagne.layers.DropoutLayer(concat, p=0.5)

    if in_to_out:
        concat = lasagne.layers.concat([concat, incoming], axis=2)

    # the shape of BiRNN output (concat) is (batch_size, input_length, 2 * num_hidden_units)
    return concat