How to use ModelCheckpoint with custom metrics in Keras?

Question

Is it possible to use custom metrics in the ModelCheckpoint callback?

Answer 1

Yes, it is possible.

Define the custom metrics as described in the documentation:

import keras.backend as K  def mean_pred(y_true, y_pred):     return K.mean(y_pred)  model.compile(optimizer='rmsprop',               loss='binary_crossentropy',               metrics=['accuracy', mean_pred]) 

To check all available metrics:

print(model.metrics_names) > ['loss', 'acc', 'mean_pred'] 

Pass the metric name to ModelCheckpoint through monitor. If you want the metric calculated in the validation, use the val_ prefix.

ModelCheckpoint(weights.{epoch:02d}-{val_mean_pred:.2f}.hdf5,                 monitor='val_mean_pred',                 save_best_only=True,                 save_weights_only=True,                 mode='max',                 period=1) 

Don't use mode='auto' for custom metrics. Understand why here.

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