# How to add ROC AUC as a metric in Tensorflow / Keras

The way to **add the ROC AUC as a metric on your Tensorflow / Keras** project is to copy this function that computes the ROC AUC and use the function name in the model. The function only requires a little customized tf code.

To use the function in the model. We first need to compile with the function passed directly and not a string (as it is shown in the example below).

Then we can use it in the callbacks but we need to refer to it as a string (so this time between the “” as shown in the snippet below).

Other customized functions follow the same pattern.

The function **tf.keras.metrics.AUC already implements the Area Under the Curve natively in TensorFlow in the Keras module**. You can **check the documentation** for further information in this regard.