def get_config(num_features, num_epochs, debug=False): """ Retrieve model configurations from config.py """ if debug: return c.TestConfig(num_features, num_epochs) else: return c.ProductionConfig(num_features, num_epochs)
def setUp(self): print("Setting up test environment") configuration = config.TestConfig() app.config.from_object(configuration) self.app = app.test_client() db.create_all() user_datastore.create_user(email='*****@*****.**', password='******') db.session.commit()
def get_config(num_features, num_epochs, debug=False): return c.ProductionConfig(num_features, num_epochs) if not debug else c.TestConfig( num_features, num_epochs)
import config import data import model import utils import os import tensorflow as tf if __name__ == "__main__": # LOAD EMBEDDING word_to_index, index_to_word, word_to_vec, emb_matrix = utils.read_glove_vecs( os.path.join(config.EMBEDDING_DIR, config.EMBEDDING_PATH)) print("Pretrained Embedding Loaded") #LOAD CONFIG train_config = config.TrainConfig() test_config = config.TestConfig() # LOAD DATA train_data = data.DATA(train_config) train_data.read_file(config.TRAIN_PATH, word_to_index) print("Train data Loaded") test_data = data.DATA(test_config) test_data.read_file(config.TEST_PATH, word_to_index) print("Test data Loaded") # BUILD MODEL #initializer = tf.random_uniform_initializer(train_config.init_scale, train_config.init_scale) with tf.name_scope("Train"): with tf.variable_scope("Model", reuse=None): train_model = model.MODEL(train_config, len(word_to_index), training=True)
def pytest_cmdline_main(config): """ After command line is parsed """ test_config._config = test_config.TestConfig(config=config)
logger.setLevel(level=logging.INFO) return logger if __name__ == '__main__': ENV = os.getenv('ENV', None) TF_LOGGER = setup_logger('tensorflow', False) LOGGER = setup_logger() APP = App() if ENV is None: LOGGER.info( "Environment variable 'ENV' not set, returning development configs." ) ENV = 'DEV' if ENV == 'DEV': APP.config = config.DevelopmentConfig(LOGGER, ENV) elif ENV == 'TEST': APP.config = config.TestConfig(LOGGER, ENV) elif ENV == 'PROD': APP.config = config.ProductionConfig(LOGGER, ENV) else: raise ValueError('Invalid environment name') APP.ci_config = config.CIConfig OUTPUT_DIR = '/home/frans/Documents/tsprediction/model' LOGGER.info('Cleanning ouput directory') shutil.rmtree(OUTPUT_DIR, ignore_errors=True) # start fresh each time LOGGER.info('Outpout directory clean') APP.experiment_fn(OUTPUT_DIR)
64: 2, 128: 2, 256: 2 } # Resolution-specific overrides if __name__ == "__main__": begin_time = dt.datetime.now() env = sys.argv[1] if len(sys.argv) > 2 else 'test' if env == 'dev': print('With development config,') cfg = config.DevelopmentConfig() elif env == 'test': print('With test config,') cfg = config.TestConfig() elif env == 'prod': print('With production config,') cfg = config.ProductionConfig() else: print('With my config,') cfg = MyConfig() print('Running FaceGen()...') np.random.seed(cfg.common.random_seed) facegen = FaceGen(cfg) facegen.train() end_time = dt.datetime.now() print()
def create_app(self): app.config.from_object(config.TestConfig()) return app