def main(_): sess = tf.compat.v1.Session() model = MyModel(sess,model_configs) if args.mode == "train": x_train, y_train, _,_ = load_data(args.data_dir) model.train(x_train, y_train,200) elif args.mode == "test": # Testing on public testing dataset _, _, x_test, y_test = load_data(args.data_dir) model.evaluate(x_test, y_test) elif args.mode == "predict": # Predicting and storing results on private testing dataset x_test = load_testing_images(args.data_dir) predictions = model.predict_prob(x_test) np.save("../predictions.npy", predictions)
if __name__ == '__main__': model = MyModel(model_configs, training_configs) if args.mode == 'train': x_train, y_train, x_test, y_test = load_data(args.data_dir) x_train, y_train, x_valid, y_valid = train_valid_split( x_train, y_train) model.train(x_train, y_train, x_valid, y_valid) model.save_weights( os.path.join(args.save_dir, model_configs["version"], "")) model.evaluate(x_test, y_test) elif args.mode == 'test': # Testing on public testing dataset model.load_weights( os.path.join(args.save_dir, model_configs["version"], "")) _, _, x_test, y_test = load_data(args.data_dir) model.evaluate(x_test, y_test) elif args.mode == 'predict': # Predicting and storing results on private testing dataset model.load_weights( os.path.join(args.save_dir, model_configs["version"], "")) x_test = load_testing_images(args.test_file) predictions = model.predict_prob(x_test) np.save("final_pred_" + model_configs["version"] + ".npy", predictions) ### END CODE HERE