K-Fold Crossvalidation in Tensorflow when using flow_from_directory for image recognition

Question

Disclaimer: I have very little experience with Tensorflow.

I have a custom dataset with 20 categories with 100+ images in each. I am doing 5-fold cross validation using InceptionV3 for transfer learning. The easiest way to load this dataset into Tensorflow that I was able to find was flow_from_directory. The method works for one fold, but not for 5 folds since you can't set the folds. How would I go about dividing up the generators into 5 folds? Should I use an alternative method of importing data instead of flow_from_directory? There was a similar question where the answer was seemingly just importing it in a different way.

from tensorflow.keras.preprocessing.image import ImageDataGenerator
datagen=ImageDataGenerator(preprocessing_function=preprocess_input,
                                 validation_split=0.2)
train_generator=datagen.flow_from_directory('/content/dataset',
                                                 target_size=(299,299),
                                                 color_mode='rgb',
                                                 batch_size=32,
                                                 class_mode='categorical',
                                                 shuffle=True,
                                                  subset='training')
val_generator = datagen.flow_from_directory('/content/dataset',
                                                 target_size=(299,299),
                                                 color_mode='rgb',
                                                 batch_size=32,
                                                 class_mode='categorical',
                                                 shuffle=True,
                                                subset='validation')

Answer 1

The easiest way I found was replacing flow_from_directory command to flow_from_dataframe (for more information on this command see).

That way you can split the dataframe. You just have to make a dataframe with images paths and labels.

 i = 1
 df_metrics = pd.DataFrame()
    kf = KFold(n_splits = 10, shuffle = True, random_state = None)
    for train_index, test_index in kf.split(dataframe):
        trainData = dataframe.iloc[train_index]
        testData = dataframe.iloc[test_index]
        print('Initializing Kfold %s'%str(i))
        print('Train shape:',trainData.shape)
        print('Test shape:',testData.shape)
        epochs = 30

    train_datagen = ImageDataGenerator(rescale=1./255,validation_split=0.2)
    test_datagen = ImageDataGenerator(rescale=1. / 255) 

    train_generator=train_datagen.flow_from_dataframe(
    dataframe=trainData,
    directory="./train/",
    x_col="id",
    y_col="label",
    subset="training",
    batch_size=batch_size,
    shuffle=True,
    class_mode="categorical",
    target_size=(img_width, img_height))

    validation_generator=train_datagen.flow_from_dataframe(
        dataframe=trainData,
        directory="./train/",
        x_col="id",
        y_col="label",
        subset="validation",
        batch_size=batch_size,
        shuffle=True,
        class_mode="categorical",
        target_size=(img_width, img_height))

    test_generator=test_datagen.flow_from_dataframe(
        dataframe=testData,
        directory="./test/",
        x_col="id",
        y_col="label",
        batch_size=1,
        shuffle=False,
        class_mode="categorical",
        target_size=(img_width, img_height)

       .
       .
       .

       i +=1