tensorflow 保存模型和取出中间权重例子

今天小编就为大家分享一篇tensorflow 保存模型和取出中间权重例子，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

下面代码的功能是先训练一个简单的模型，然后保存模型，同时保存到一个pb文件当中，后续可以从pd文件里读取权重值。

import tensorflow as tf import numpy as np import os import h5py import pickle from tensorflow.python.framework import graph_util from tensorflow.python.platform import gfile #设置使用指定GPU os.environ['CUDA_VISIBLE_DEVICES'] = '1' #下面这段代码是在训练好之后将所有的权重名字和权重值罗列出来，训练的时候需要注释掉 reader = tf.train.NewCheckpointReader('./model.ckpt-100') variables = reader.get_variable_to_shape_map() for ele in variables: print(ele) print(reader.get_tensor(ele)) x = tf.placeholder(tf.float32, shape=[None, 1]) y = 4 * x + 4 w = tf.Variable(tf.random_normal([1], -1, 1)) b = tf.Variable(tf.zeros([1])) y_predict = w * x + b loss = tf.reduce_mean(tf.square(y - y_predict)) optimizer = tf.train.GradientDescentOptimizer(0.5) train = optimizer.minimize(loss) isTrain = False#设成True去训练模型 train_steps = 100 checkpoint_steps = 50 checkpoint_dir = '' saver = tf.train.Saver() # defaults to saving all variables - in this case w and b x_data = np.reshape(np.random.rand(10).astype(np.float32), (10, 1)) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) if isTrain: for i in xrange(train_steps): sess.run(train, Feed_dict={x: x_data}) if (i + 1) % checkpoint_steps == 0: saver.save(sess, checkpoint_dir + 'model.ckpt', global_step=i+1) else: ckpt = tf.train.get_checkpoint_state(checkpoint_dir) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) else: pass print(sess.run(w)) print(sess.run(b)) graph_def = tf.get_default_graph().as_graph_def() #通过修改下面的函数，个人觉得理论上能够实现修改权重，但是很复杂，如果哪位有好办法，欢迎指教 output_graph_def = graph_util.convert_variables_to_constants(sess, graph_def, ['Variable']) with tf.gfile.FastGFile('./test.pb', 'wb') as f: f.write(output_graph_def.SerializetoString()) with tf.Session() as sess: #对应最后一部分的写，这里能够将对应的变量取出来 with gfile.FastGFile('./test.pb', 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) res = tf.import_graph_def(graph_def, return_elements=['Variable:0']) print(sess.run(res)) print(sess.run(graph_def))