tf.data.Dataset from tf.keras.preprocessing.image.ImageDataGenerator.flow_from_directory?

Question

How do I create a tf.data.Dataset from tf.keras.preprocessing.image.ImageDataGenerator.flow_from_directory?

I'm considering tf.data.Dataset.from_generator, but it's unclear how to acquire the output_types keyword argument for it, given the return type:

A DirectoryIterator yielding tuples of (x, y) where x is a numpy array containing a batch of images with shape (batch_size, *target_size, channels) and y is a numpy array of corresponding labels.

Answer 1

Both batch_x and batch_y in ImageDataGenerator are of type K.floatx(), so must be tf.float32 by default.

Similar question was discussed already at How to use Keras generator with tf.data API. Let me copy-paste the answer from there:

def make_generator():
    train_datagen = ImageDataGenerator(rescale=1. / 255)
    train_generator = 
    train_datagen.flow_from_directory(train_dataset_folder,target_size=(224, 224), class_mode='categorical', batch_size=32)
    return train_generator

train_dataset = tf.data.Dataset.from_generator(make_generator,(tf.float32, tf.float32))

The author faced another issue with the graph scope, but I guess it is unrelated to your question.

Or as a one liner:

tf.data.Dataset.from_generator(lambda:
    ImageDataGenerator().flow_from_directory('folder_path'),(tf.float32, tf.float32))