tensorflow10 《TensorFlow实战Google深度学习框架》笔记-05-03模型持久化code

01 ckpt文件保存方法

# 《TensorFlow实战Google深度学习框架》05 minist数字识别问题# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:ts05.09.py # ckpt文件保存方法import tensorflow as tf# 1. 保存计算两个变量和的模型v1 = tf.Variable(tf.constant(1.0, shape=[1]), name = "v1")v2 = tf.Variable(tf.constant(2.0, shape=[1]), name = "v2")result = v1 + v2init_op = tf.global_variables_initializer()saver = tf.train.Saver()with tf.Session() as sess:    sess.run(init_op)    # 需要在本python脚本文件下存在Saved_model目录    # 否则提示错误 ValueError: Parent directory of Saved_model/model.ckpt doesn't exist, can't save.    saver.save(sess, "Saved_model/model.ckpt")# 2. 加载保存了两个变量和的模型with tf.Session() as sess:    saver.restore(sess, "Saved_model/model.ckpt")    print(sess.run(result)) # [3.]# 3. 直接加载持久化的图saver = tf.train.import_meta_graph("Saved_model/model.ckpt.meta")with tf.Session() as sess:    saver.restore(sess, "Saved_model/model.ckpt")    print(sess.run(tf.get_default_graph().get_tensor_by_name("add:0"))) # [3.]# 4. 变量重命名v1 = tf.Variable(tf.constant(1.0, shape=[1]), name = "other-v1")v2 = tf.Variable(tf.constant(2.0, shape=[1]), name = "other-v2")saver = tf.train.Saver({"v1": v1, "v2": v2})

02 滑动平均类的保存

# 《TensorFlow实战Google深度学习框架》05 minist数字识别问题# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:ts05.10.py # 滑动平均类的保存import tensorflow as tf# 1. 使用滑动平均v = tf.Variable(0, dtype=tf.float32, name="v")for variables in tf.global_variables():    print(variables.name)'''v:0'''ema = tf.train.ExponentialMovingAverage(0.99)maintain_averages_op = ema.apply(tf.global_variables())for variables in tf.global_variables():    print(variables.name)'''v:0v/ExponentialMovingAverage:0'''# 2. 保存滑动平均模型saver = tf.train.Saver()with tf.Session() as sess:    init_op = tf.global_variables_initializer()    sess.run(init_op)    sess.run(tf.assign(v, 10))    sess.run(maintain_averages_op)    # 保存的时候会将v:0  v/ExponentialMovingAverage:0这两个变量都存下来。    saver.save(sess, "Saved_model/model2.ckpt")    print(sess.run([v, ema.average(v)]))'''[10.0, 0.099999905]'''# 3. 加载滑动平均模型v = tf.Variable(0, dtype=tf.float32, name="v")# 通过变量重命名将原来变量v的滑动平均值直接赋值给v。saver = tf.train.Saver({"v/ExponentialMovingAverage": v})with tf.Session() as sess:    saver.restore(sess, "Saved_model/model2.ckpt")    print(sess.run(v))'''0.0999999'''

03 variables_to_restore函数的使用样例

# 《TensorFlow实战Google深度学习框架》05 minist数字识别问题# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:ts05.11.py # variables_to_restore函数的使用样例import tensorflow as tfv = tf.Variable(0, dtype=tf.float32, name="v")ema = tf.train.ExponentialMovingAverage(0.99)print(ema.variables_to_restore())'''{'v/ExponentialMovingAverage': <tensorflow.python.ops.variables.Variable object at 0x000001F0CFB260F0>}'''saver = tf.train.Saver({"v/ExponentialMovingAverage": v})with tf.Session() as sess:    saver.restore(sess, "Saved_model/model2.ckpt")    print(sess.run(v))'''0.0999999'''

04 pb文件保存方法

# 《TensorFlow实战Google深度学习框架》05 minist数字识别问题# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:ts05.12.py # pb文件保存方法import tensorflow as tf# 1. pb文件的保存方法import tensorflow as tffrom tensorflow.python.framework import graph_utilv1 = tf.Variable(tf.constant(1.0, shape=[1]), name = "v1")v2 = tf.Variable(tf.constant(2.0, shape=[1]), name = "v2")result = v1 + v2init_op = tf.global_variables_initializer()with tf.Session() as sess:    sess.run(init_op)    graph_def = tf.get_default_graph().as_graph_def()    output_graph_def = graph_util.convert_variables_to_constants(sess, graph_def, ['add'])    with tf.gfile.GFile("Saved_model/combined_model.pb", "wb") as f:           f.write(output_graph_def.SerializeToString())# 2. 加载pb文件from tensorflow.python.platform import gfilewith tf.Session() as sess:    model_filename = "Saved_model/combined_model.pb"    with gfile.FastGFile(model_filename, 'rb') as f:        graph_def = tf.GraphDef()        graph_def.ParseFromString(f.read())    result = tf.import_graph_def(graph_def, return_elements=["add:0"])    print(sess.run(result)) # [array([ 3.], dtype=float32)]