def wide_and_deep_classifier()

in 10_mlops/model.py [0:0]

• 13 lines of code
• 3 McCabe index (conditional complexity)

def wide_and_deep_classifier(inputs, linear_feature_columns, dnn_feature_columns, dnn_hidden_units):
    deep = tf.keras.layers.DenseFeatures(dnn_feature_columns, name='deep_inputs')(inputs)
    layers = [int(x) for x in dnn_hidden_units.split(',')]
    for layerno, numnodes in enumerate(layers):
        deep = tf.keras.layers.Dense(numnodes, activation='relu', name='dnn_{}'.format(layerno + 1))(deep)
    wide = tf.keras.layers.DenseFeatures(linear_feature_columns, name='wide_inputs')(inputs)
    both = tf.keras.layers.concatenate([deep, wide], name='both')
    output = tf.keras.layers.Dense(1, activation='sigmoid', name='pred')(both)
    model = tf.keras.Model(inputs, output)
    model.compile(optimizer='adam',
                  loss='binary_crossentropy',
                  metrics=['accuracy', rmse, tf.keras.metrics.AUC()])
    return model