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)