evaluate.py

import tensorflow as tf
import config
from prepare_data import generate_datasets
from train import get_model

if __name__ == '__main__':

    # GPU settings
    gpus = tf.config.experimental.list_physical_devices('GPU')
    if gpus:
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)

    # get the original_dataset
    train_dataset, valid_dataset, test_dataset, train_count, valid_count, test_count = generate_datasets()
    # print(train_dataset)
    # load the model
    model = get_model()
    model.load_weights(filepath=config.save_model_dir)

    # Get the accuracy on the test set
    loss_object = tf.keras.metrics.SparseCategoricalCrossentropy()
    test_loss = tf.keras.metrics.Mean()
    test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()

    @tf.function
    def test_step(images, labels):
        predictions = model(images, training=False)
        t_loss = loss_object(labels, predictions)

        test_loss(t_loss)
        test_accuracy(labels, predictions)

    for test_images, test_labels in test_dataset:
        test_step(test_images, test_labels)
        print("loss: {:.5f}, test accuracy: {:.5f}".format(test_loss.result(),
                                                           test_accuracy.result()))

    print("The accuracy on test set is: {:.3f}%".format(test_accuracy.result()*100))