0645b446e61e3e51e00c20a2f925111dcd29e264,examples/image_retraining/retrain.py,,export_model,#Any#Any#Any#,947

Before Change

        "prediction": tf.saved_model.utils.build_tensor_info(out_classes)
    }

    signature = tf.saved_model.signature_def_utils.build_signature_def(
        inputs=inputs,
        outputs=outputs,
        method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)

    legacy_init_op = tf.group(tf.tables_initializer(), name="legacy_init_op")

    // Save out the SavedModel.
    builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir)
    builder.add_meta_graph_and_variables(
        sess, [tf.saved_model.tag_constants.SERVING],
        signature_def_map={
            tf.saved_model.signature_constants.
            DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                signature
        },
        legacy_init_op=legacy_init_op)
    builder.save()


def main(_):
  // Needed to make sure the logging output is visible.

After Change

  // The SavedModel should hold the eval graph.
  sess, in_image, _, _, _, _ = build_eval_session(module_spec, class_count)
  with sess.graph.as_default() as graph:
    tf.saved_model.simple_save(
        sess,
        saved_model_dir,
        inputs={"image": in_image},
        outputs={"prediction": graph.get_tensor_by_name("final_result:0")},
        legacy_init_op=tf.group(tf.tables_initializer(), name="legacy_init_op")
    )


def main(_):
  // Needed to make sure the logging output is visible.

Instances