コード例 #1
0
ファイル: FM_keras.py プロジェクト: JerryCatLeung/CTR-2
def model_fn():
    # build Keras Model

    # use feature_column as keras input
    input = {}
    for f in FEATURE_NAME:
        if f != TARGET:
            input[f] = Input(shape=(1, ), name=f, dtype=DTYPE[f])

    feature_columns = build_features()
    feature_layer = tf.keras.layers.DenseFeatures(feature_columns)

    dense_feature = feature_layer(input)

    fm = FM_Layer(name='fm_layer', factor_dim=8)(dense_feature)

    tf.summary.histogram('fm_output', fm)

    output = Dense(1, activation='sigmoid', name='output')(fm)

    model = Model(inputs=[i for i in input.values()], outputs=output)

    optimizer = tf.keras.optimizers.Adam(learning_rate=0.01,
                                         beta_1=0.9,
                                         beta_2=0.999,
                                         amsgrad=False)

    model.compile(optimizer=optimizer,
                  loss='binary_crossentropy',
                  metrics=['binary_accuracy', 'AUC'])
    print(model.summary())

    return model
コード例 #2
0
def model_fn(features, labels, mode, params):
    """
    FM model
    """
    feature_columns= build_features()

    input = tf.feature_column.input_layer(features, feature_columns)

    input_dim = input.get_shape().as_list()[-1]

    with tf.variable_scope('linear'):
        init = tf.random_normal( shape = (input_dim,1) )
        w = tf.get_variable('w', dtype = tf.float32, initializer = init, validate_shape = False)
        b = tf.get_variable('b', shape = [1], dtype= tf.float32)

        linear_term = tf.add(tf.matmul(input,w), b)
        add_layer_summary( linear_term.name, linear_term)

    with tf.variable_scope('fm_interaction'):
        init = tf.truncated_normal(shape = (input_dim, params['factor_dim']))
        v = tf.get_variable('v', dtype = tf.float32, initializer = init, validate_shape = False)

        sum_square = tf.pow(tf.matmul(input, v),2)
        square_sum = tf.matmul(tf.pow(input,2), tf.pow(v,2))

        interaction_term = 0.5 * tf.reduce_mean(sum_square - square_sum, axis=1, keep_dims= True)

        add_layer_summary(interaction_term.name, interaction_term)

    with tf.variable_scope('output'):
        y = tf.math.add(interaction_term, linear_term)
        add_layer_summary(y.name, y)

    return y