Saving best model in keras

Question

I use the following code when training a model in keras

from keras.callbacks import EarlyStopping  model = Sequential() model.add(Dense(100, activation='relu', input_shape = input_shape)) model.add(Dense(1))  model_2.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])   model.fit(X, y, epochs=15, validation_split=0.4, callbacks=[early_stopping_monitor], verbose=False)  model.predict(X_test) 

but recently I wanted to get the best trained model saved as the data I am training on gives a lot of peaks in "high val_loss vs epochs" graph and I want to use the best one possible yet from the model.

Is there any method or function to help with that?

Answer 1

EarlyStopping and ModelCheckpoint is what you need from Keras documentation.

You should set save_best_only=True in ModelCheckpoint. If any other adjustments needed, are trivial.

Just to help you more you can see a usage here on Kaggle.

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Adding the code here in case the above Kaggle example link is not available:

model = getModel() model.summary()  batch_size = 32  earlyStopping = EarlyStopping(monitor='val_loss', patience=10, verbose=0, mode='min') mcp_save = ModelCheckpoint('.mdl_wts.hdf5', save_best_only=True, monitor='val_loss', mode='min') reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=7, verbose=1, epsilon=1e-4, mode='min')  model.fit(Xtr_more, Ytr_more, batch_size=batch_size, epochs=50, verbose=0, callbacks=[earlyStopping, mcp_save, reduce_lr_loss], validation_split=0.25) 

Answer 2

EarlyStopping's restore_best_weights argument will do the trick:

restore_best_weights: whether to restore model weights from the epoch with the best value of the monitored quantity. If False, the model weights obtained at the last step of training are used.

So not sure how your early_stopping_monitor is defined, but going with all the default settings and seeing you already imported EarlyStopping you could do this:

early_stopping_monitor = EarlyStopping(     monitor='val_loss',     min_delta=0,     patience=0,     verbose=0,     mode='auto',     baseline=None,     restore_best_weights=True ) 

And then just call model.fit() with callbacks=[early_stopping_monitor] like you already do.