Calling "fit_generator()" multiple times in Keras

Question

I have a generator function which generates tuples of (inputs, targets) on which my model is trained using the fit_generator() method in Keras.

My dataset is divided into 9 equal parts. I wish to perform a leave-one-out cross validation on the dataset using the fit_generator() method and keep the learned parameters of the previous training intact.

My question is that will calling fit_generator() multiple times on the model make it re-learn its learned parameters on the previous train and validation sets from scratch or will it keep those learned parameters intact leading to improvement of accuracy?

After a little digging I found that the fit() method in Keras retains the learned parameters as over here Calling "fit" multiple times in Keras but I'm not sure if the same happens for fit_generator() and if it does can it be used for cross-validation of data.

The pseudo-code I'm thinking of implementing to achieve the cross-validation is as follows:

class DatasetGenerator(Sequence):
    def __init__(validation_id, mode):
        #Some code

    def __getitem__():
        #The generator function

        #Some code

        return (inputs, targets)

for id in range(9):

    train_set = DatasetGenerator(id, 'train') 
    #train_set contains all 8 parts leaving the id part out for validation.

    validation_set = DatasetGenerator(id, 'val')
    #val_set contains the id part.

    history = model.fit_generator(train_set, epochs = 10, steps_per_epoch = 24000, validation_data = val_set, validation_steps = 3000)

print('History Dict:', history.history)
results = model.evaluate_generator(test_set, steps=steps)
print('Test loss, acc:', results)

Will the model keep the learned parameters intact and improve upon them for each iteration of the for loop?

Answer 1

fit and fit_generator behave the same in that regard, calling them again will resume training from the previously trained weights.

Also note that what you are trying to do is not cross-validation, as to do real cross-validation, you train one model for each fold, and the models are completely independent, not continued from training of the previous fold.

Answer 2

As far as I know it will keep the previous trained params. Also, I think what you are trying to do can be done by modifying the on_epoch_end() method of Sequence. Could be something like this:

class DatasetGenerator(Sequence):
    def __init__(self, id, mode):
        self.id = id
        self.mode = mode
        self.current_epoch=0
        #some code

    def __getitem__(self, idx):
        id = self.id
        #Some code
        return (inputs, targets)

    def on_epoch_end():
        self.current_epoch += 1
        if self.current_epoch % 10 == 0:
            self.id += 1