rnn.py 文件源码

def prep_model(inputs, N, s0pad, s1pad, c):
    outputs = B.rnn_input(inputs, N, s0pad,
                dropout=c['dropout'], dropoutfix_inp=c['dropoutfix_inp'], dropoutfix_rec=c['dropoutfix_rec'],
                sdim=c['sdim'],
                rnnbidi=c['rnnbidi'], rnn=c['rnn'], rnnact=c['rnnact'], rnninit=c['rnninit'],
                rnnbidi_mode=c['rnnbidi_mode'], rnnlevels=c['rnnlevels'])

    # Projection
    if c['project']:
        proj = Dense(int(N*c['pdim']), activation=c['pact'], kernel_regularizer=l2(c['l2reg']), name='proj')
        e0p = proj(outputs[0])
        e1p = proj(outputs[1])
        N = N*c['pdim']
        return [e0p, e1p], N 
    else:
        return [outputs[0], outputs[1]], N

    #input_dim=int(N*c['sdim'])