python - 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.

---

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)