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

 # Normalizes the pixel values of an image to the range [0, 1].
 
def normalize(image, label):
    return image / 255, label
 # Set the paths to the folder containing the training data
train_data_dir = "Network/Training/"
 # 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, 1)
 # Load the training and validation data
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)
 # Get the class names
class_names = train_ds.class_names
print(class_names)
 # Normalize the training and validation data
train_ds = train_ds.map(normalize)
val_ds = val_ds.map(normalize)
 # Define the model architecture
model = tf.keras.Sequential([
    layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(img_height, img_width, 1)),
    layers.MaxPooling2D(),
    layers.Conv2D(32, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(64, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(num_classes, activation='softmax')
])
 # Compile the model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
 # Print the model summary
model.summary()
 # Train the model
epochs=10
model.fit(train_ds,
          validation_data=val_ds,
          epochs=epochs)
 # Save the trained model
model.save('Network/trained_model.h5')