evaluate.py

import tensorflow as tf
import config
from prepare_data import get_datasets
from sklearn.metrics import classification_report
import numpy as np


def eval_model(new_model):
    # Load data
    train_generator, valid_generator, test_generator, \
    train_num, valid_num, test_num= get_datasets()

    # Get the accuracy on the test set
    loss, acc, auc, precision, recall  = new_model.evaluate(test_generator,
                                             batch_size=config.BATCH_SIZE,
                                             steps=test_num // config.BATCH_SIZE)
    print("result of ",config.model_dir)
    print("The accuracy on test set is: {:6.3f}%".format(acc*100))
    print("The auc on test set is: {:6.3f}%".format(auc*100))
    print("The precision on test set is: {:6.3f}%".format(precision*100))
    print("The recall on test set is: {:6.3f}%".format(recall*100))

    # Evaluate per class
    lables_array = test_generator.classes
    predictions = new_model.predict(test_generator)
    predictions = np.argmax(predictions, axis=1)
    print(classification_report(lables_array, predictions))

if __name__ == '__main__':
    # Load the model
    new_model = tf.keras.models.load_model(config.model_dir+config.model_save_name+".h5")
    eval_model(new_model)