Data processing with image augmentation

data_processing.py

# Inputs are suitably resized for the selected module.
validation_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
validation_generator = validation_datagen.flow_from_directory(
    validation_dir, 
    shuffle=False, 
    seed=42,
    color_mode="rgb", 
    class_mode="categorical",
    target_size=IMAGE_SIZE,
    batch_size=BATCH_SIZE)
do_data_augmentation = True #@param {type:"boolean"}
if do_data_augmentation:
  train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
      rescale = 1./255,
      rotation_range=40,
      horizontal_flip=True,
      width_shift_range=0.2, 
      height_shift_range=0.2,
      shear_range=0.2, 
      zoom_range=0.2,
      fill_mode='nearest' )
else:
  train_datagen = validation_datagen
  
train_generator = train_datagen.flow_from_directory(
    train_dir, 
    subset="training", 
    shuffle=True, 
    seed=42,
    color_mode="rgb", 
    class_mode="categorical",
    target_size=IMAGE_SIZE,
    batch_size=BATCH_SIZE)