TensorFlow实现打印每一层的输出

在test.py中可以通过如下代码直接生成带weight的pb文件，也可以通过tf官方的freeze_graph.py将ckpt转为pb文件。

constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def,['net_loss/inference/encode/conv_output/conv_output'])
with tf.gfile.FastGFile('net_model.pb', mode='wb') as f:
  f.write(constant_graph.SerializeToString())

tf1.0中通过带weight的pb文件与get_tensor_by_name函数可以获取每一层的输出

import os
import os.path as ops
import argparse
import time
import math
 
import tensorflow as tf
import glob
import numpy as np
import matplotlib.pyplot as plt
import cv2
 
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
gragh_path = './model.pb'
image_path = './lvds1901.JPG'
inputtensorname = 'input_tensor:0'
tensorname = 'loss/inference/encode/resize_images/ResizeBilinear'
filepath='./net_output.txt'
HEIGHT=256
WIDTH=256
VGG_MEAN = [103.939, 116.779, 123.68]
 
with tf.Graph().as_default():
  graph_def = tf.GraphDef()
  with tf.gfile.GFile(gragh_path, 'rb') as fid:
    serialized_graph = fid.read()
    graph_def.ParseFromString(serialized_graph)
 
    tf.import_graph_def(graph_def, name='')
 
    image = cv2.imread(image_path)
    image = cv2.resize(image, (WIDTH, HEIGHT), interpolation=cv2.INTER_CUBIC)
    image_np = np.array(image)
    image_np = image_np - VGG_MEAN
    image_np_expanded = np.expand_dims(image_np, axis=0)
 
    with tf.Session() as sess:
      ops = tf.get_default_graph().get_operations()
      tensor_name = tensorname + ':0'
      tensor_dict = tf.get_default_graph().get_tensor_by_name(tensor_name)
      image_tensor = tf.get_default_graph().get_tensor_by_name(inputtensorname)
      output = sess.run(tensor_dict, feed_dict={image_tensor: image_np_expanded})
      
      ftxt = open(filepath,'w')
      transform = output.transpose(0, 3, 1, 2)
      transform = transform.flatten()
      weight_count = 0
      for i in transform:
        if weight_count % 10 == 0 and weight_count != 0:
          ftxt.write('\n')
        ftxt.write(str(i) + ',')
        weight_count += 1
      ftxt.close()

以上这篇TensorFlow实现打印每一层的输出就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。