可以在Keras模型中使用tf.contrib.quantize.create_training_graph吗？

Question

是否可以将tf.contrib.quantize.create_training_graph已训练的Keras模型用于模型量化？

据我了解，我可以tf.Graph从Keras模型导入，但是我可以在用修改后对其进行微调tf.contrib.quantize.create_training_graph吗？

我能够tf.contrib.quantize.create_training_graph(input_graph=K.get_session().graph, quant_delay=int(0))在模型定义和模型加载之后调用，但是得到：

2019-02-22 14:56:24.216742: W tensorflow/c/c_api.cc:686] Operation '{name:'global_average_pooling2d_1_1/Mean' id:3777 op device:{} def:{global_average_pooling2d_1_1/Mean = Mean[T=DT_FLOAT, Tidx=DT_INT32, keep_dims=false](conv2d_15_1/act_quant/FakeQuantWithMinMaxVars:0, global_average_pooling2d_1_1/Mean/reduction_indices)}}' was changed by updating input tensor after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.

转换为keras-> tensorflow-> tflite时，至少我能够使用uint8权重保存模型，因为我了解到模型和推理的输入仍然是fp32。

converter = tf.contrib.lite.TFLiteConverter.from_frozen_graph(
        graph_def_file='tf_model.pb',
        input_arrays=input_node_names,
        output_arrays=output_node_names)

converter.post_training_quantize = True

tflite_model = converter.convert()

https://github.com/keras-team/keras/issues/11105