networks.py 文件源码

def build_BiRNN(incoming, num_units, mask=None, grad_clipping=0, nonlinearity=nonlinearities.tanh,
                precompute_input=True, dropout=True, in_to_out=False):
    # construct the forward and backward rnns. Now, Ws are initialized by He 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)

    rnn_forward = lasagne.layers.RecurrentLayer(incoming, num_units,
                                                mask_input=mask, grad_clipping=grad_clipping,
                                                nonlinearity=nonlinearity, precompute_input=precompute_input,
                                                W_in_to_hid=lasagne.init.GlorotUniform(),
                                                W_hid_to_hid=lasagne.init.GlorotUniform(), name='forward')
    rnn_backward = lasagne.layers.RecurrentLayer(incoming, num_units,
                                                 mask_input=mask, grad_clipping=grad_clipping,
                                                 nonlinearity=nonlinearity, precompute_input=precompute_input,
                                                 W_in_to_hid=lasagne.init.GlorotUniform(),
                                                 W_hid_to_hid=lasagne.init.GlorotUniform(), backwards=True,
                                                 name='backward')

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

    # 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