TensorFlow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)

        for node in graph_def.node:
            if node.op == 'RefSwitch':
                node.op = 'Switch'
                for index in range(len(node.input)):
                    if 'moving_' in node.input[index]:
                        node.input[index] = node.input[index] + '/read'
            elif node.op == 'AssignSub':
                node.op = 'Sub'
                if 'use_locking' in node.attr: 
                    del node.attr['use_locking']

        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()