def batchGenerator(imgs, steerings, batchSize, isTraining):

    while True:
        batchImg = []
        batchSteering = []

        for i in range(batchSize):
            randIndex = random.randint(0, len(imgs) - 1)
            if isTraining:
                img, steering = randomAugment(imgs[randIndex], steerings[randIndex])
            else:
                img = imgs[randIndex]
                steering = steerings[randIndex]

            img = imgPreprocess(img)

            batchImg.append(img)
            batchSteering.append(steering)

        yield (np.asarray(batchImg), np.asarray(batchSteering))

history = model.fit_generator(batchGenerator(X_train, y_train, 300, 1),
                              steps_per_epoch = 300,
                              epochs = 10,
                              validation_data = batchGenerator(X_valid, y_valid, 200, 0),
                              validation_steps = 200,
                              verbose = 1,
                              shuffle = 1)