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