fuatbeser / notes

Useful techniques and methods that you can use while you are developing machine learning models.

notes

Useful techniques and methods that you can use while you are developing machine learning models.

Stop training process if the trained model reaches the desired accuracy (or loss)

DESIRED_ACCURACY = 0.98

class myCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs = {}):
    if(logs.get('acc') > DESIRED_ACCURACY):
      print(f"\nReached {DESIRED_ACCURACY * 100}% accuracy so cancelling training!")
      self.model.stop_training = True

callbacks = myCallback()

How to use?

We can make use of tf.keras.callbacks.Callback to stop training if it reaches a desired accuracy (or loss). Simply create a myCallback class and call it. After that, don't forget to add [callbacks].

model.fit(training_data, training_labels, epochs=100, callbacks=[callbacks])

If you would like to use loss instead of accuracy, use logs.get('loss').