def load_model(sess, saved_path):
class Config:
def __init__(self, **entries):
self.__dict__.update(entries)
# Load configuration from trained model
with open("/".join([saved_path, "config.json"]), "r") as file:
saved = json.load(file)
print ("Configuration recoverd:")
pprint (saved)
config = Config(**saved)
if config.model == "vallina":
model = LinearModel(config, "inference")
model.build()
else:
pass
# other model
print ("Restore from previous results...")
ckpt = tf.train.get_checkpoint_state(saved_path)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print ("saved path: {}".format(saved_path))
model.restore(sess, saved_path)
print ("Model reloaded from {}".format(ckpt.model_checkpoint_path))
else:
raise ValueError("CAN NOT FIND CHECKPOINTS EXISTS!")
return model
def create_model(sess, FLAGS, mode):
"""Create model only used for train mode.
"""
if FLAGS.model == "vallina":
model = LinearModel(FLAGS, mode)
model.build()
else:
pass
# other model
# create task file
model_path = os.path.join(FLAGS.logdir, FLAGS.task_name)
if not os.path.exists(model_path):
os.makedirs(model_path)
print("Save model to {}".format(model_path))
elif (FLAGS.reset):
shutil.rmtree(model_path)
os.makedirs(model_path)
print("Remove existing model at {} and restart.".format(model_path))
else:
raise ValueError("Fail to create the new model.")
# Save the current configurations
config = dict(FLAGS.__flags.items())
with open("/".join([model_path, "config.json"]), "w") as file:
json.dump(config, file)
# initialize variables
sess.run(tf.global_variables_initializer())
return model
def create_model(sess, FLAGS, mode):
model = LinearModel(FLAGS, mode)
model.build()
# initialize variables
sess.run(tf.global_variables_initializer())
return model