models.py 文件源码

def get_model_4(params):
    embedding_weights = pickle.load(open(common.TRAINDATA_DIR+"/embedding_weights_w2v_%s.pk" % params['embeddings_suffix'],"rb"))
    graph_in = Input(shape=(params['sequence_length'], params['embedding_dim']))
    convs = []
    for fsz in params['filter_sizes']:
        conv = Convolution1D(nb_filter=params['num_filters'],
                             filter_length=fsz,
                             border_mode='valid',
                             activation='relu',
                             subsample_length=1)
        x = conv(graph_in)
        logging.debug("Filter size: %s" % fsz)
        logging.debug("Output CNN: %s" % str(conv.output_shape))

        pool = GlobalMaxPooling1D()
        x = pool(x)
        logging.debug("Output Pooling: %s" % str(pool.output_shape))
        convs.append(x)

    if len(params['filter_sizes'])>1:
        merge = Merge(mode='concat')
        out = merge(convs)
        logging.debug("Merge: %s" % str(merge.output_shape))
    else:
        out = convs[0]

    graph = Model(input=graph_in, output=out)

    # main sequential model
    model = Sequential()
    if not params['model_variation']=='CNN-static':
        model.add(Embedding(len(embedding_weights[0]), params['embedding_dim'], input_length=params['sequence_length'],
                            weights=embedding_weights))
    model.add(Dropout(params['dropout_prob'][0], input_shape=(params['sequence_length'], params['embedding_dim'])))
    model.add(graph)
    model.add(Dense(params['n_dense']))
    model.add(Dropout(params['dropout_prob'][1]))
    model.add(Activation('relu'))

    model.add(Dense(output_dim=params["n_out"], init="uniform"))
    model.add(Activation(params['final_activation']))
    logging.debug("Output CNN: %s" % str(model.output_shape))

    if params['final_activation'] == 'linear':
        model.add(Lambda(lambda x :K.l2_normalize(x, axis=1)))

    return model

# word2vec ARCH with LSTM