TensorFlow模型保存和提取的方法

 # 本文件程序为配合教材及学习进度渐进进行，请按照注释分段执行 # 执行时要注意IDE的当前工作过路径，最好每段重启控制器一次，输出结果更准确 # Part1: 通过tf.train.Saver类实现保存和载入神经网络模型 # 执行本段程序时注意当前的工作路径 import tensorflow as tf v1 = tf.Variable(tf.constant(1.0, shape=[1]), name="v1") v2 = tf.Variable(tf.constant(2.0, shape=[1]), name="v2") result = v1 + v2 saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) saver.save(sess, "Model/model.ckpt") # Part2: 加载TensorFlow模型的方法 import tensorflow as tf v1 = tf.Variable(tf.constant(1.0, shape=[1]), name="v1") v2 = tf.Variable(tf.constant(2.0, shape=[1]), name="v2") result = v1 + v2 saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, "./Model/model.ckpt") # 注意此处路径前添加"./" print(sess.run(result)) # [ 3.] # Part3: 若不希望重复定义计算图上的运算，可直接加载已经持久化的图 import tensorflow as tf saver = tf.train.import_meta_graph("Model/model.ckpt.meta") with tf.Session() as sess: saver.restore(sess, "./Model/model.ckpt") # 注意路径写法 print(sess.run(tf.get_default_graph().get_tensor_by_name("add:0"))) # [ 3.] # Part4： tf.train.Saver类也支持在保存和加载时给变量重命名 import tensorflow as tf # 声明的变量名称name与已保存的模型中的变量名称name不一致 u1 = tf.Variable(tf.constant(1.0, shape=[1]), name="other-v1") u2 = tf.Variable(tf.constant(2.0, shape=[1]), name="other-v2") result = u1 + u2 # 若直接生命Saver类对象，会报错变量找不到 # 使用一个字典dict重命名变量即可，{"已保存的变量的名称name": 重命名变量名} # 原来名称name为v1的变量现在加载到变量u1（名称name为other-v1）中 saver = tf.train.Saver({"v1": u1, "v2": u2}) with tf.Session() as sess: saver.restore(sess, "./Model/model.ckpt") print(sess.run(result)) # [ 3.] # Part5: 保存滑动平均模型 import tensorflow as tf 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:0 # v/ExponentialMovingAverage:0 saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) sess.run(tf.assign(v, 10)) sess.run(maintain_averages_op) saver.save(sess, "Model/model_ema.ckpt") print(sess.run([v, ema.average(v)])) # [10.0, 0.099999905] # Part6: 通过变量重命名直接读取变量的滑动平均值 import tensorflow as tf v = tf.Variable(0, dtype=tf.float32, name="v") saver = tf.train.Saver({"v/ExponentialMovingAverage": v}) with tf.Session() as sess: saver.restore(sess, "./Model/model_ema.ckpt") print(sess.run(v)) # 0.0999999 # Part7: 通过tf.train.ExponentialMovingAverage的variables_to_restore()函数获取变量重命名字典 import tensorflow as tf v = tf.Variable(0, dtype=tf.float32, name="v") # 注意此处的变量名称name一定要与已保存的变量名称一致 ema = tf.train.ExponentialMovingAverage(0.99) print(ema.variables_to_restore()) # {'v/ExponentialMovingAverage': } # 此处的v取自上面变量v的名称name="v" saver = tf.train.Saver(ema.variables_to_restore()) with tf.Session() as sess: saver.restore(sess, "./Model/model_ema.ckpt") print(sess.run(v)) # 0.0999999 # Part8: 通过convert_variables_to_constants函数将计算图中的变量及其取值通过常量的方式保存于一个文件中 import tensorflow as tf from tensorflow.python.framework import graph_util v1 = tf.Variable(tf.constant(1.0, shape=[1]), name="v1") v2 = tf.Variable(tf.constant(2.0, shape=[1]), name="v2") result = v1 + v2 with tf.Session() as sess: sess.run(tf.global_var<i style="color:transparent">来源gaodai$ma#com搞$$代**码网</i>iables_initializer()) # 导出当前计算图的GraphDef部分，即从输入层到输出层的计算过程部分 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("Model/combined_model.pb", 'wb') as f: f.write(output_graph_def.SerializeToString()) # Part9: 载入包含变量及其取值的模型 import tensorflow as tf from tensorflow.python.platform import gfile with tf.Session() as sess: model_filename = "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)]