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pythontensorflowkeras
AaronDT
AaronDT

Reputation: 4060

Keras: How to expand validation_split to generate a third set i.e. test set?

I am using Keras with a TensorFlow backend. I am using the ImageDataGenerator with the validation_split argument to split my data into train set and validation set. As such, I use flow_from_directory with the subset set to "training" and "testing" like so:

total_gen = ImageDataGenerator(validation_split=0.3)


train_gen = data_generator.flow_from_directory(my_dir, target_size=(input_size, input_size), shuffle=False, seed=13,
                                                     class_mode='categorical', batch_size=BATCH_SIZE, subset="training")

valid_gen = data_generator.flow_from_directory(my_dir, target_size=(input_size, input_size), shuffle=False, seed=13,
                                                     class_mode='categorical', batch_size=32, subset="validation")

This is amazingly convenient, as it allows me to use only one directory instead of two (one for training and one for validation). Now I wonder if it is possible to expand this process in order to generating a third set i.e. test set?

Upvotes: 3

Views: 2480

Answers (1)

sdcbr
sdcbr

Reputation: 7129

This is not possible out of the box. You should be able to do it with some minor modifications to the source code of ImageDataGenerator:

if subset is not None:
    if subset not in {'training', 'validation'}: # add a third subset here
        raise ValueError('Invalid subset name:', subset,
                         '; expected "training" or "validation".') # adjust message
    split_idx = int(len(x) * image_data_generator._validation_split) 
    # you'll need two split indices here
    if subset == 'validation':
        x = x[:split_idx]
        x_misc = [np.asarray(xx[:split_idx]) for xx in x_misc]
        if y is not None:
            y = y[:split_idx]
    elif subset == '...' # add extra case here

    else:
        x = x[split_idx:]
        x_misc = [np.asarray(xx[split_idx:]) for xx in x_misc] # change slicing
        if y is not None:
            y = y[split_idx:] # change slicing

Edit: this is how you could modify the code:

if subset is not None:
    if subset not in {'training', 'validation', 'test'}:
        raise ValueError('Invalid subset name:', subset,
                         '; expected "training" or "validation" or "test".')
    split_idxs = (int(len(x) * v) for v in image_data_generator._validation_split)
    if subset == 'validation':
        x = x[:split_idxs[0]]
        x_misc = [np.asarray(xx[:split_idxs[0]]) for xx in x_misc]
        if y is not None:
            y = y[:split_idxs[0]]
    elif subset == 'test':
        x = x[split_idxs[0]:split_idxs[1]]
        x_misc = [np.asarray(xx[split_idxs[0]:split_idxs[1]]) for xx in x_misc]
        if y is not None:
            y = y[split_idxs[0]:split_idxs[1]]
    else:
        x = x[split_idxs[1]:]
        x_misc = [np.asarray(xx[split_idxs[1]:]) for xx in x_misc]
        if y is not None:
            y = y[split_idxs[1]:]

Basically, validation_split is now expected to be a tuple of two floats instead of a single float. The validation data will be the fraction of data between 0 and validation_split[0], test data between validation_split[0] and validation_split[1] and training data between validation_split[1] and 1. This is how you can use it:

import keras
# keras_custom_preprocessing is how i named my directory
from keras_custom_preprocessing.image import ImageDataGenerator

generator = ImageDataGenerator(validation_split=(0.1, 0.5))
# First 10%: validation data - next 40% test data - rest: training data        
gen = generator.flow_from_directory(directory='./data/', subset='test')
# Finds 40% of the images in the dir

You will need to modify the file in two or three additional lines (there is a typecheck you will have to change), but that's it and that should work. I have the modified file, let me know if you are interested, I can host it on my github.

Upvotes: 2

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