import tensorflow as tf
from keras import layers
from keras.models import Sequential
from keras.optimizers import Adam
from keras.utils import to_categorical
from keras.preprocessing.image import ImageDataGenerator
import os
import PIL
import PIL.Image
import numpy

# Set the paths to the folders containing the training data
train_data_dir = "Training/"
validation_data_dir = "Validation/"


# Set the number of classes and batch size
num_classes = 3
batch_size = 32

# Set the image size and input shape
img_width, img_height = 100, 100
input_shape = (img_width, img_height, 3)

train_ds = tf.keras.utils.image_dataset_from_directory(
  train_data_dir,
  validation_split=0.2,
  subset="training",
  shuffle=True,
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

val_ds = tf.keras.utils.image_dataset_from_directory(
  train_data_dir,
  validation_split=0.2,
  subset="validation",
  shuffle=True,
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

class_names = train_ds.class_names
print(class_names)