data_loader_constructor.py

def __init__(self, image_size, batch_size):

    self.image_size = image_size
    self.batch_size = batch_size

    # 80% train data, 10% validation data, 10% test data
    split_weights = (8, 1, 1)
    splits = tfds.Split.TRAIN.subsplit(weighted=split_weights)

    (self.train_data_raw, self.validation_data_raw, self.test_data_raw), self.metadata = tfds.load(
        'cats_vs_dogs', split=list(splits),
        with_info=True, as_supervised=True)

    # Get the number of train examples
    self.num_train_examples = self.metadata.splits['train'].num_examples*80/100
    self.get_label_name = self.metadata.features['label'].int2str

    # Pre-process data
    self._prepare_data()
    self._prepare_batches()