查看tensorflow pb模型文件的节点信息：

import tensorflow as tf

with tf.Session() as sess:

    with open('./quantized_model.pb', 'rb') as f:

        graph_def = tf.GraphDef()

        graph_def.ParseFromString(f.read())

        print graph_def
        

效果：

# ...

node {

  name: "FullyConnected/BiasAdd"

  op: "BiasAdd"

  input: "FullyConnected/MatMul"

  input: "FullyConnected/b/read"

  attr {

    key: "T"

    value {

      type: DT_FLOAT

    }

  }

  attr {

    key: "data_format"

    value {

      s: "NHWC"

    }

  }

}

node {

  name: "FullyConnected/Softmax"

  op: "Softmax"

  input: "FullyConnected/BiasAdd"

  attr {

    key: "T"

    value {

      type: DT_FLOAT

    }

  }

}

library {

}

参考：https://tang.su/2017/01/export-TensorFlow-network/

https://github.com/tensorflow/tensorflow/issues/15689

一些核心代码：

import tensorflow as tf

with tf.Session() as sess:

    with open('./graph.pb', 'rb') as f:

        graph_def = tf.GraphDef()

        graph_def.ParseFromString(f.read())

        print graph_def

        output = tf.import_graph_def(graph_def, return_elements=['out:0'])

        print(sess.run(output))

This is part of my Tensorflow frozen graph, I have named the input and output nodes.

>>> g.ParseFromString(open('frozen_graph.pb','rb').read())

>>> g

node {

  name: "input"

  op: "Placeholder"

  attr {

    key: "dtype"

    value {

      type: DT_FLOAT

    }

  }

  attr {

    key: "shape"

    value {

      shape {

        dim {

          size: -1

        }

        dim {

          size: 68

        }

      }

    }

  }

}

...

node {

  name: "output"

  op: "Softmax"

  input: "add"

  attr {

    key: "T"

    value {

      type: DT_FLOAT

    }

  }

}

I ran this model by the following code
(CELL is name of directory where my file is located)

final String MODEL_FILE = "file:///android_asset/" + CELL + "/optimized_graph.pb" ;

final String INPUT_NODE = "input" ;

final String OUTPUT_NODE = "output" ;

final int[] INPUT_SIZE = {1,68} ;

float[] RESULT = new float[8];

inferenceInterface = new TensorFlowInferenceInterface();

inferenceInterface.initializeTensorFlow(getAssets(),MODEL_FILE) ;

inferenceInterface.fillNodeFloat(INPUT_NODE,INPUT_SIZE,input);

and finally

inferenceInterface.readNodeFloat(OUTPUT_NODE,RESULT);