def test_save_sup_load_rl(self):
pass
model_to_save = MockModel(spinn.spinn_core_model.BaseModel,
default_args())
# Parse command line flags.
get_flags()
FLAGS(sys.argv)
log_temp = tempfile.NamedTemporaryFile()
ckpt_temp = tempfile.NamedTemporaryFile()
logger = afs_safe_logger.ProtoLogger(log_temp.name)
FLAGS.ckpt_path = '.'
trainer_to_save = ModelTrainer(model_to_save, logger, FLAGS)
model_to_load = MockModel(spinn.rl_spinn.BaseModel, default_args())
trainer_to_load = ModelTrainer(model_to_load, logger, FLAGS)
# Save to and load from temporary file.
trainer_to_save.save(ckpt_temp.name)
trainer_to_load.load(ckpt_temp.name, cpu=True)
compare_models(model_to_save, model_to_load)
# Cleanup temporary file.
ckpt_temp.close()
log_temp.close()
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)
