| import tensorflow as tf | |
| from keras_tuner import HyperParameters | |
| from src.models import MakeHyperModel | |
| from src.preprocessing import get_data_augmentation | |
| from src.config import IMAGE_SIZE | |
| data_augmentation = get_data_augmentation() | |
| img = tf.keras.preprocessing.image.load_img( | |
| "examples/cat2.jpg", target_size=IMAGE_SIZE | |
| ) | |
| img_array = tf.keras.preprocessing.image.img_to_array(img) | |
| img_array = tf.expand_dims(img_array, 0) | |
| latest = tf.train.latest_checkpoint('./tuner_model/cat-vs-dog/trial_0484d8d758a5ef7b91ca97d334ba7870/checkpoints/epoch_0') | |
| hypermodel = MakeHyperModel(input_shape=IMAGE_SIZE + (3,), num_classes=2, data_augmentation=data_augmentation) | |
| model = hypermodel.build(hp=HyperParameters()) | |
| model.load_weights(latest).expect_partial() | |
| predictions = model.predict(img_array) | |
| score = predictions[0] | |
| print( | |
| "This image is %.2f percent cat and %.2f percent dog." | |
| % (100 * (1 - score), 100 * score) | |
| ) | |