CODEWITHSUNDEEP
pythontensorflowkerasconv-neural-networkkeras-2
Lilo
Lilo

Reputation: 640

Keras ModelCheckpoint monitor multiple values

I want to use Keras ModelCheckpoint callback to monitor several parameters ( I have a multi-task network). Is it possible with just one callback ? Or do I need to do that in many callbacks ??

The ckechpoint creation : 

checkpointer = ModelCheckpoint(filepath='checkpoints/weights-{epoch:02d}.hdf5', monitor='val_O1_categorical_accuracy' , verbose=1, save_best_only=True, mode='max')

The second parameter I want to monitor : val_O2_categorical_accuracy

Doing that in a list will not work. i.e. 

checkpointer = ModelCheckpoint(filepath='checkpoints/weights-{epoch:02d}.hdf5', monitor=['val_O1_categorical_accuracy','val_O2_categorical_accuracy'] , verbose=1, save_best_only=True, mode='max')

TypeError: unhashable type: 'list'

Upvotes: 6

Views: 7657

Answers (1)

Vivek Kalyanarangan
Vivek Kalyanarangan

Reputation: 9081

I am afraid you will have to do it in separate instances. Think about what is happening here - 

checkpointer = ModelCheckpoint(filepath='checkpoints/weights-{epoch:02d}.hdf5', monitor='val_O1_categorical_accuracy' , verbose=1, save_best_only=True, mode='max')

When you are saving a model by monitoring val_O1_categorical_accuracy, here is what it will do in pseudocode - 

for each epoch:
    check the val_O1_categorical_accuracy after updating weights
    if this metric is better in this epoch than the previous ones:
        save the model
    else
        pass

So really specifying multiple monitor is out of scope. In this case it has to be an either/or choice as based on a monitor metric only one model among other conflicting models can be the best one.

Upvotes: 2

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