Custom Callback After Each Epoch To Log Certain Information

I know how to save the model after every epoch: savemodel = ModelCheckpoint(filepath='models/model_{epoch:02d}-{loss:.2f}.h5') model.fit(X, Y, batch_size=4, epochs=32, verbose=1, c

Solution 1:

Here is the solution, by subclassing Callback:

from keras.callbacks import Callback

classMyLogger(Callback):
    defon_epoch_end(self, epoch, logs=None):
        withopen('log.txt', 'a+') as f:
            f.write('%02d %.3f\n' % (epoch, logs['loss']))

then

mylogger = MyLogger()
model.fit(X, Y, batch_size=32, epochs=32, verbose=1, callbacks=[mylogger])

or even

model.fit(X, Y, batch_size=32, epochs=32, verbose=1, callbacks=[MyLogger()])

Solution 2:

Actually, you don't need to define a function (or even use an explicit callback) for this, as such info is automatically returned by the fit method; from the docs:

History

keras.callbacks.History()

Callback that records events into a History object.

This callback is automatically applied to every Keras model. The History object gets returned by the fit method of models.

You don't even need to explicitly import anything; what you need is just:

hist = model.fit(X, Y, batch_size=4, epochs=32, verbose=1, callbacks=[savemodel]) # no other specific callback

and hist.history will contain the loss and any other metrics of interest you may have defined (e.g. accuracy), for the training and validation (if exists) sets, at the end of each epoch, which you can subsequently save into a file.

See here for detailed examples.