loader.load(sess, args.tag_set.split(","), args.export_dir)
elif args.model_dir:
// load graph from a checkpoint
ckpt = tf.train.latest_checkpoint(args.model_dir)
assert ckpt, "Invalid model checkpoint path: {}".format(args.model_dir)
logging.info("===== restoring from checkpoint: {}".format(ckpt + ".meta"))
saver = tf.train.import_meta_graph(ckpt + ".meta", clear_devices=True)
saver.restore(sess, ckpt)
After Change
// get list of input/output tensors (by name)
if args.signature_def_key:
input_tensors = [inputs_tensor_info[t].name for t in input_tensor_names]
output_tensors = [outputs_tensor_info[output_tensor_names[0]].name]
else:
input_tensors = [t + ":0" for t in input_tensor_names]
output_tensors = [t + ":0" for t in output_tensor_names]
