How to compute Receiving Operating Characteristic (ROC) and AUC in keras?

Question

I have a multi output(200) binary classification model which I wrote in keras.

In this model I want to add additional metrics such as ROC and AUC but to my knowledg

Answer 1

You can monitor auc during training by providing metrics the following way:

METRICS = [
      keras.metrics.TruePositives(name='tp'),
      keras.metrics.FalsePositives(name='fp'),
      keras.metrics.TrueNegatives(name='tn'),
      keras.metrics.FalseNegatives(name='fn'), 
      keras.metrics.BinaryAccuracy(name='accuracy'),
      keras.metrics.Precision(name='precision'),
      keras.metrics.Recall(name='recall'),
      keras.metrics.AUC(name='auc'),
]


model = keras.Sequential([
    keras.layers.Dense(16, activation='relu', input_shape=(train_features.shape[-1],)),
    keras.layers.Dense(1, activation='sigmoid'),
  ])

model.compile(
    optimizer=keras.optimizers.Adam(lr=1e-3)
    loss=keras.losses.BinaryCrossentropy(),
    metrics=METRICS)

for a more detailed tutorial see:
https://www.tensorflow.org/tutorials/structured_data/imbalanced_data