先罗列出来代码，有时间再解释
from tensorflow.python.framework import graph_util
import tensorflow as tfdef export_model(input_checkpoint, output_graph):
#这个可以加载saver的模型
saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True)
graph = tf.get_default_graph() # 获得默认的图
input_graph_def = graph.as_graph_def() # 返回一个序列化的图代表当前的图 with tf.Session() as sess:
sess.run(tf.global_variables_initializer()) saver.restore(sess, input_checkpoint)
output_graph_def = graph_util.convert_variables_to_constants( # 模型持久化，将变量值固定
sess=sess,
input_graph_def=input_graph_def,# 等于:sess.graph_def
output_node_names=['softmax_linear/softmax_linear','Cast_1'])# 如果有多个输出节点，以逗号隔开这个是重点，输入和输出的参数都需要在这里记录 with tf.gfile.GFile(output_graph, "wb") as f: #保存模型
f.write(output_graph_def.SerializeToString()) #序列化输出export_model('E:\\python\\image\\code2\\model_10\\model.ckpt',"E:\\python\\image\\code2\\model_10\\model.pb")
使用的代码
import os
import numpy as np
import tensorflow as tf
import model_new
from PIL import Image
import matplotlib.pyplot as plt
import csv
import shutil
from tensorflow.python.platform import gfiledef get_one_image(img_dir): image = Image.open(img_dir) image = image.resize((128,128))
image = np.array(image) return image, img_dirdef test_model(model_path, img_path):
image_array,img_dir = get_one_image( img_path)
image = tf.cast(image_array,tf.float32)
#image = tf.image.per_image_standardization(image)
image = tf.reshape(image,[1,128,128,3]) with tf.Session() as sess: with gfile.FastGFile(model_path,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def,name='')
sess.run(tf.global_variables_initializer()) input_x = sess.graph.get_tensor_by_name('Cast_1:0')
out = sess.graph.get_tensor_by_name('softmax_linear/softmax_linear:0')
ret = sess.run(out, feed_dict={input_x: image.eval()})
print(ret)out_pb_path="E:\\python\\image\\code2\\model_10\\frozen_model.pb"
img_path = "E:\\python\\image\\code\\images\\0\\mmexport1540880139708.jpg"
test_model(out_pb_path,img_path)
