model.py 文件源码

def sbrt2017(num_hiddens, var_dropout, dropout, weight_decay, num_features=39,
             num_classes=28):
    """ SBRT model
    Reference:
        [1] Gal, Y, "A Theoretically Grounded Application of Dropout in
        Recurrent Neural Networks", 2015.
        [2] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. "Speech
        recognition with deep recurrent neural networks", 2013.
        [6] Wu, Yonghui, et al. "Google's Neural Machine Translation System:
        Bridging the Gap between Human and Machine Translation.", 2016.
    """

    x = Input(name='inputs', shape=(None, num_features))
    o = x

    if dropout > 0.0:
        o = Dropout(dropout)(o)

    o = Bidirectional(LSTM(num_hiddens,
                           return_sequences=True,
                           W_regularizer=l2(weight_decay),
                           U_regularizer=l2(weight_decay),
                           dropout_W=var_dropout,
                           dropout_U=var_dropout,
                           consume_less='gpu'))(o)

    if dropout > 0.0:
        o = Dropout(dropout)(o)

    o = TimeDistributed(Dense(num_classes,
                              W_regularizer=l2(weight_decay)))(o)

    # Define placeholders
    labels = Input(name='labels', shape=(None,), dtype='int32', sparse=True)
    inputs_length = Input(name='inputs_length', shape=(None,), dtype='int32')

    # Define a decoder
    dec = Lambda(decode, output_shape=decode_output_shape,
                 arguments={'is_greedy': True}, name='decoder')
    y_pred = dec([o, inputs_length])

    ctc = Lambda(ctc_lambda_func, output_shape=(1,), name="ctc")
    # Define loss as a layer
    loss = ctc([o, labels, inputs_length])

    return Model(input=[x, labels, inputs_length], output=[loss, y_pred])