I'm dealing with bunch of image dataset
However it takes a lot of time to learn, so I used earlystopping in tensorflow
This is my callback option & fit option
(I know monitoring acc is not a good option, but just wanted to see how earlystopping works)
tf.keras.callbacks.EarlyStopping(
monitor='accuracy',
patience=3,
#mode='max',
verbose=2,
baseline=0.98
)
model.fit(x, y, batch_size=16, epochs=10, verbose=2, validation_split=0.2, callbacks=callbacks)
However, this is the result:
101/101 - 42s - loss: 6.9557 - accuracy: 6.2461e-04 - val_loss: 6.9565 - val_accuracy: 0.0000e+00
Epoch 2/10
101/101 - 39s - loss: 6.9549 - accuracy: 0.0019 - val_loss: 6.9558 - val_accuracy: 0.0000e+00
Epoch 3/10
101/101 - 37s - loss: 6.9537 - accuracy: 0.0037 - val_loss: 6.9569 - val_accuracy: 0.0000e+00
Epoch 00003: early stopping
since monitoring value 'accuracy' kept increasing, expected it not to stop.
plus, I want earlystopping to monitor acc like this
acc=0, acc=0.1....acc=0.5, acc=0.4, acc=0.5, acc=0.6 #dont stop if increases again in patience epoch
acc=0, acc=0.1....acc=0.5, acc=0.3, acc=0.4, acc=0.35 #stop if acc does not increases again in patience epoch
How should I do that?
The issue is with the use of Baseline
As per the documentation it is defined as :
Baseline value for the monitored quantity. Training will stop if the model doesn't show improvement over the baseline.
By setting Baseline to 98% you are stating that the model's accuracy starts at 98% and it it does not improve over the baseline over 3 epochs stop training.
Instead do the following as per your use case:
tf.keras.callbacks.EarlyStopping(
monitor='accuracy',
min_delta=0.001,
patience=3,
mode='auto',
verbose=2,
baseline=None
)