What is the correct way to call Keras flow_from_directory() method? In the following article there is an instruction that dataset needs to be divided into train, validation and test folders where the test folder should not contain the labeled subfolders. Instead it should only contain a single folder (i.e. Test_folder).

When I use the following code, I get the output message refering that no image were found.

Ver.1:
test_generator = test_datagen.flow_from_directory(
    "dataset\\test\\test_folder\\",
    target_size=(IMG_WIDTH, IMG_HEIGHT),
    batch_size=1,
    class_mode=None,
    shuffle=False,
    seed=10)

Output message: "Found 0 images belonging to 0 classes.".


Instead, if I use the same folder structure (dataset\test\class_a\test_1.jpg etc) as in the train and validation folders, everything seems to be OK and I manage to evaluate my model.

Ver.2:
test_generator = test_datagen.flow_from_directory(
    "dataset\\test\\",
    target_size=(IMG_WIDTH, IMG_HEIGHT),
    batch_size=32,
    class_mode='categorical',
    shuffle=False,
    seed=10)

Output message: "Found 1500 images belonging to 3 classes.".


I also tried the recommendation where 'classes' attribute is specified but still 0 images were found.

Ver.3:
test_generator = test_datagen.flow_from_directory(
    "dataset2\\test\\test_folder\\",
    target_size=(IMG_WIDTH, IMG_HEIGHT),
    batch_size=1,
    classes=['test'],
    class_mode=None,
    shuffle=False,
    seed=10)

Output message: Found 0 images belonging to 1 classes.


Thus, what is the correct way to call flow_from_directory() method and why am I getting the message that no files were found? Is my model not correctly evaluated when I use the Ver.2 solution?
Please find a working solution here.

The generators look like:

# Data generators
train_datagen = ImageDataGenerator(
      rescale=1./255,
      rotation_range=40,
      width_shift_range=0.2,
      height_shift_range=0.2,
      shear_range=0.2,
      zoom_range=0.2,
      horizontal_flip=True,
      fill_mode='nearest')

# Note that the validation data should not be augmented!
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        # This is the target directory
        train_dir,
        # All images will be resized to 150x150
        target_size=(150, 150),
        batch_size=batch_size,
        # Since we use categorical_crossentropy loss, we need binary labels
        class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
        validation_dir,
        target_size=(150, 150),
        batch_size=batch_size,
        class_mode='categorical')


Make sure you have your images stored in the correct way. E.g.

...images/train/class1/
...images/train/class2/
...images/val/class1/
...images/val/class2/


The generator function does need this structure. So make sure there are subfolders for each class in the train/test directories.

You can also use the data generator function for prediction as described in this post.