Adding data augmentation right into the model

# Adding data augmentation right into the model
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing

# Create a data augmentation stage with horizontal flipping, rotations, zooms
data_augmentation = keras.Sequential([
  preprocessing.RandomFlip("horizontal_and_vertical"),
  preprocessing.RandomRotation(0.3),
  preprocessing.RandomZoom(0.3),
  preprocessing.RandomHeight(0.3),
  preprocessing.RandomWidth(0.3),
  # preprocessing.Rescaling(1./255) # keep for ResNet50V2, remove for EfficientNetB0
], name ="data_augmentation")


# View a random image
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import os
import random
target_class = random.choice(train_g.class_names) # choose a random class
target_dir = "C:/Users/User/Desktop/computer vision/final_dataset/training/" + target_class # create the target directory
random_image = random.choice(os.listdir(target_dir)) # choose a random image from target directory
random_image_path = target_dir + "/" + random_image # create the choosen random image path
img = mpimg.imread(random_image_path) # read in the chosen target image
plt.imshow(img) # plot the target image
plt.title(f"Original random image from class: {target_class}")
plt.axis(False); # turn off the axes

# Augment the image
augmented_img = data_augmentation(tf.expand_dims(img, axis=0)) # data augmentation model requires shape (None, height, width, 3)
plt.figure()
plt.imshow(tf.squeeze(augmented_img)/255.) # requires normalization after augmentation
plt.title(f"Augmented random image from class: {target_class}")
plt.axis(False);

# "C:/Users/User/Desktop/computer vision/final_dataset/training/"