如何从python中的.pb文件恢复Tensorflow模型? [英] How to restore Tensorflow model from .pb file in python?

问题描述

我有一个tensorflow .pb文件，我想将其加载到python DNN中，恢复图形并获得预测.我这样做是为了测试创建的.pb文件是否可以做出与普通Saver.save()模型相似的预测.

I have an tensorflow .pb file which I would like to load into python DNN, restore the graph and get the predictions. I am doing this to test out whether the .pb file created can make the predictions similar to the normal Saver.save() model.

我的基本问题是，当我使用上述.pb文件在Android上进行预测时，得到的预测值有很大不同

My basic problem is am getting a very different value of predictions when I make them on Android using the above mentioned .pb file

我的.pb文件创建代码:

My .pb file creation code:

frozen_graph = tf.graph_util.convert_variables_to_constants(
        session,
        session.graph_def,
        ['outputLayer/Softmax']
    )
with open('frozen_model.pb', 'wb') as f:
  f.write(frozen_graph.SerializeToString())

所以我有两个主要问题:

So I have two major concerns:

1. 如何将上述.pb文件加载到python Tensorflow模型中?
2. 为什么我在python和android中获得的预测值完全不同?

推荐答案

以下代码将读取模型并打印出图中节点的名称.

The following code will read the model and print out the names of the nodes in the graph.

import tensorflow as tf
from tensorflow.python.platform import gfile
GRAPH_PB_PATH = './frozen_model.pb'
with tf.Session() as sess:
   print("load graph")
   with gfile.FastGFile(GRAPH_PB_PATH,'rb') as f:
       graph_def = tf.GraphDef()
   graph_def.ParseFromString(f.read())
   sess.graph.as_default()
   tf.import_graph_def(graph_def, name='')
   graph_nodes=[n for n in graph_def.node]
   names = []
   for t in graph_nodes:
      names.append(t.name)
   print(names)

您正确冻结了图形，这就是为什么您得到不同结果的原因，基本上权重没有存储在模型中.您可以使用 freeze_graph.py (

You are freezing the graph properly that is why you are getting different results basically weights are not getting stored in your model. You can use the freeze_graph.py (link) for getting a correctly stored graph.

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