def eval_model(embedding = None, metamodel = None):
model = metamodel
if model is None:
model = MetaModel(hyperparameters)
if embedding is None:
model.populate_with_nasnet_metacells()
else:
model.populate_from_embedding(embedding)
model.build_model(dataset.images_shape)
model.evaluate(dataset, 1, dir_path)
model.save_metadata(dir_path)
model.save_model(dir_path)
model.generate_graph(dir_path)
model.clear_model()
tf.keras.backend.clear_session()
def test_model_accuracy_from_embedding(dir_name, embedding):
dir_path = os.path.join(evo_dir, dir_name)
# dataset = ImageDataset.get_cifar10_reduced()
dataset = ImageDataset.get_cifar10()
if not os.path.exists(dir_path):
os.makedirs(dir_path)
hyperparameters = Hyperparameters()
model = MetaModel(hyperparameters)
model.populate_from_embedding(embedding)
model.build_model(dataset.images_shape)
model.evaluate(dataset)
model.save_model(dir_path)
model.generate_graph(dir_path)
model.save_metadata(dir_path)
model.clear_model()