python from tensorflow import keras from tensorflow.keras import layers model = keras.Sequential([ layers.Conv2D(32, (3, 3), activation='relu', input_shape=(image_height, image_width, channels)), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, (3, 3), activation='relu')), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, (3, 3), activation='relu')), layers.Flatten(), layers.Dense(64, activation='relu'), layers.Dense(num_classes, activation='softmax') ]) python model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.fit(train_images, train_labels, epochs=num_epochs, validation_data=(test_images, test_labels)) python test_loss, test_accuracy = model.evaluate(test_images, test_labels)