def CCPM(linear_feature_columns, dnn_feature_columns, conv_kernel_width=(6, 5), conv_filters=(4, 4),
         dnn_hidden_units=(128, 64), l2_reg_linear=1e-5, l2_reg_embedding=1e-5, l2_reg_dnn=0, dnn_dropout=0,
         seed=1024, task='binary'):
    """Instantiates the Convolutional Click Prediction Model architecture.

    :param linear_feature_columns: An iterable containing all the features used by linear part of the model.
    :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
    :param conv_kernel_width: list,list of positive integer or empty list,the width of filter in each conv layer.
    :param conv_filters: list,list of positive integer or empty list,the number of filters in each conv layer.
    :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN.
    :param l2_reg_linear: float. L2 regularizer strength applied to linear part
    :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
    :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
    :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param init_std: float,to use as the initialize std of embedding vector
    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.
    """

    if len(conv_kernel_width) != len(conv_filters):
        raise ValueError(
            "conv_kernel_width must have same element with conv_filters")

    features = build_input_features(
        linear_feature_columns + dnn_feature_columns)
    inputs_list = list(features.values())

    linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed,
                                    l2_reg=l2_reg_linear)

    sparse_embedding_list, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding,
                                                          seed, support_dense=False)

    n = len(sparse_embedding_list)
    l = len(conv_filters)

    conv_input = concat_func(sparse_embedding_list, axis=1)
    pooling_result = Lambda(
        lambda x: tf.expand_dims(x, axis=3))(conv_input)

    for i in range(1, l + 1):
        filters = conv_filters[i - 1]
        width = conv_kernel_width[i - 1]
        k = max(1, int((1 - pow(i / l, l - i)) * n)) if i < l else 3

        conv_result = Conv2D(filters=filters, kernel_size=(width, 1), strides=(1, 1), padding='same',
                             activation='tanh', use_bias=True, )(pooling_result)
        pooling_result = KMaxPooling(
            k=min(k, int(conv_result.shape[1])), axis=1)(conv_result)

    flatten_result = Flatten()(pooling_result)
    dnn_out = DNN(dnn_hidden_units, l2_reg=l2_reg_dnn, dropout_rate=dnn_dropout)(flatten_result)
    dnn_logit = Dense(1, use_bias=False)(
        dnn_out)

    final_logit = add_func([dnn_logit, linear_logit])

    output = PredictionLayer(task)(final_logit)
    model = Model(inputs=inputs_list, outputs=output)
    return model
Instantiates the Convolutional Click Prediction Model architecture.

    :param linear_feature_columns: An iterable containing all the features used by linear part of the model.
    :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
    :param conv_kernel_width: list,list of positive integer or empty list,the width of filter in each conv layer.
    :param conv_filters: list,list of positive integer or empty list,the number of filters in each conv layer.
    :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN.
    :param l2_reg_linear: float. L2 regularizer strength applied to linear part
    :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
    :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
    :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param init_std: float,to use as the initialize std of embedding vector
    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.
    
deepctr/models/ccpm.py