def test_trainer(): search_space = SearchSpace(model_output_shape=2) tokens = search_space.generate_token() # controller = Controller(tokens=tokens) trainer = Trainer() # samples = controller.generate_sequence() samples = [[65, 146, 143, 201, 281, 382]] architectures = search_space.create_models(samples=samples, model_input_shape=(128, 128, 3)) epoch_performance = trainer.train_models(samples=samples, architectures=architectures) assert len(epoch_performance) != 0
def test_controller_rnn_trainer(): search_space = SearchSpace(model_output_shape=2) tokens = search_space.generate_token() controller = Controller(tokens=tokens) # samples = controller.generate_sequence() manual_epoch_performance = { (320, 96, 338, 84, 176, 382): (0.968, 0), # (acc, lat) (22, 47, 225, 315, 223, 382): (0.87, 0), (74, 204, 73, 236, 309, 382): (0.74, 0), (110, 60, 191, 270, 199, 382): (0.51, 0) } loss_avg = controller.train_controller_rnn( epoch_performance=manual_epoch_performance) print(loss_avg)
def test_search_space(): search_space = SearchSpace(model_output_shape=2) token = search_space.generate_token() dense_tokens = [x for x, y in token.items() if "Dense" in y] # dense layers start from 865 sample_sequence = [52, 146, 31, 119, 138, 244] translated_sequence = search_space.translate_sequence(sample_sequence) assert len(translated_sequence) == 4 model = search_space.create_model(sequence=sample_sequence, model_input_shape=(128, 128, 3)) keras.utils.plot_model(model, to_file="model.png", show_shapes=True) print(model.summary()) assert len(token) == 890
def test_controller_generate_sequence_naive(): search_space = SearchSpace(model_output_shape=2) tokens = search_space.generate_token() controller = Controller(tokens=tokens) # samples = controller.generate_sequence_naive(mode="b") # for sequence in samples: # sequence_ = sequence # print(sequence_) # sequences_random = controller.generate_sequence_naive(mode="r") for i in range(20): sequences_random = controller.generate_sequence_naive(mode="r_var_len") print(sequences_random) print("Done.")
def test_controller_sample_generator(): search_space = SearchSpace(model_output_shape=2) tokens = search_space.generate_token() controller = Controller(tokens=tokens) samples = controller.generate_sequence() print(samples)