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?
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)