for index, eval_set in enumerate(eval_iterators):
log_entry.Clear()
acc = evaluate(FLAGS,
model,
eval_set,
log_entry,
logger,
trainer,
vocabulary,
show_sample=True,
eval_index=index)
print(log_entry)
logger.LogEntry(log_entry)
else:
train_loop(FLAGS, model, trainer, training_data_iter, eval_iterators,
logger)
if __name__ == '__main__':
get_flags()
# Parse command line flags.
FLAGS(sys.argv)
flag_defaults(FLAGS)
if FLAGS.model_type != "RLSPINN":
raise Exception("Reinforce is only implemented for RLSPINN.")
run(only_forward=FLAGS.expanded_eval_only_mode)
self.debug = FLAGS.debug
model.apply(set_debug)
# Do an evaluation-only run.
eval_str = eval_format(model)
logger.Log("Eval-Format: {}".format(eval_str))
eval_extra_str = eval_extra_format(model)
logger.Log("Eval-Extra-Format: {}".format(eval_extra_str))
index = 0
eval_set = eval_iterators[index]
acc = evaluate(FLAGS, model, data_manager, eval_set, index, logger, step,
vocabulary)
if __name__ == '__main__':
get_flags()
# Parse command line flags.
FLAGS(sys.argv)
flag_defaults(FLAGS, load_log_flags=True)
if len(FLAGS.eval_data_path.split(":")) > 1:
raise Exception(
"The evaluate.py script only runs against one eval set. "
"Please refrain from the ':' token in --eval_data_path")
run()
