def __init__(self, config):
super().__init__(config)
self.model_compiled = False
self.model_path = config['solver']['saver']['model_path']
self.checkpoint_dir = get_checkpoint_dir(self.config)
self.session_conf = get_session_conf(self.config)
self.session = tf.Session(config=self.session_conf)
tf.keras.backend.set_session(self.session)
self.metrics = self.get_metrics()
def set_experimental_environment(self):
"""Set the experimental environment."""
# Set configuration
session_conf = get_session_conf(self.config)
task_config = self.config["data"]["task"]
batch_size = task_config['batch_size']
num_epochs = task_config['epochs']
saver_conf = self.config['solver']['saver']
smax_to_keep = saver_conf['max_to_keep']
save_checkpoint_steps = saver_conf['save_checkpoint_steps']
resume_model_path = saver_conf.get('resume_model_path', None)
print_every = saver_conf['print_every']
return session_conf, smax_to_keep, batch_size, num_epochs, \
save_checkpoint_steps, \
resume_model_path, print_every
def __init__(self, config):
super().__init__(config)
self.all_modes = (utils.INFER, utils.EVAL, utils.TRAIN)
self.infer_no_label = self.config["data"][utils.INFER].get(
'infer_no_label', False)
self.model_config = self.config["model"]
self.task_config = self.config["data"]["task"]
self.batch_size = self.task_config['batch_size']
self.num_parallel_calls = self.task_config['num_parallel_calls']
self.vocab_min_frequency = self.task_config['vocab_min_frequency']
self.use_custom_vocab = self.task_config.get('use_custom_vocab', False)
self.text_vocab_file_path = self.task_config['text_vocab']
self.label_vocab_file_paths = self.task_config['label_vocab']
if not isinstance(self.label_vocab_file_paths, list):
self.label_vocab_file_paths = [self.label_vocab_file_paths]
self.output_num = len(self.label_vocab_file_paths)
self.multi_output = bool(self.output_num > 1)
self.multi_text = False
self.session_conf = get_session_conf(self.config)
self.init_feed_dict = {}
