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
