和這篇文章對比https://blog.csdn.net/fanzonghao/article/details/81023730

不希望重復定義圖上的運算，也就是在模型恢復過程中，不想sess.run(init)首先看路徑

lineRegulation_model.py定義線性回歸類：

import tensorflow as tf """ 類定義一些公共量，方便模型載入用 """ class LineRegModel:def __init__(self):with tf.variable_scope('var'):self.a_val=tf.Variable(tf.random_normal(shape=[1]),name='a_val')self.b_val = tf.Variable(tf.random_normal(shape=[1]),name='b_val')self.x_input=tf.placeholder(dtype=tf.float32,name='input_placeholder')self.y_label = tf.placeholder(dtype=tf.float32,name='result_placeholder')self.y_output = tf.add(tf.multiply(self.x_input,self.a_val),self.b_val,name='output')self.loss=tf.reduce_mean(tf.pow(self.y_output-self.y_label,2))def get_saver(self):return tf.train.Saver()def get_op(self):return tf.train.GradientDescentOptimizer(0.01).minimize(self.loss)

model_train.py定義模型訓練過程

import tensorflow as tf import numpy as np from save_and_restore2 import global_variable from save_and_restore2 import lineRegulation_model as model import os if not os.path.exists('./model'):os.makedirs('./model') """ 訓練模型 """ train_x=np.random.rand(5) train_y=train_x*5+3 model=model.LineRegModel()#類要加括號 a_val=model.a_val b_val=model.b_val x_input=model.x_input y_label=model.y_label y_output=model.y_output loss=model.loss optimizer=model.get_op() saver=model.get_saver() if __name__ == '__main__':init=tf.global_variables_initializer()with tf.Session() as sess:sess.run(init)flag=Trueepoch=0while flag:epoch+=1cost,_=sess.run([loss,optimizer],feed_dict={x_input:train_x,y_label:train_y})if cost<1e-6:flag=Falseprint('a={},b={}'.format(a_val.eval(sess),b_val.eval(sess)))print('epoch={}'.format(epoch))print(a_val)# print(a_val.op)saver.save(sess,global_variable.save_path)print('model save finish')

print(a_val)的形式

print(a_val.op)的形式

model_restore.py恢復模型 ，利用恢復圖在恢復權重的方式，可實現更細節的模型恢復

import tensorflow as tf from save_and_restore import global_variable,lineRegulation_model as model """ 恢復模型圖文件 """ saver=tf.train.import_meta_graph('./model/weight.meta') #讀取placeholder和最終的輸出結果 graph=tf.get_default_graph() a_val=graph.get_tensor_by_name('var/a_val:0') b_val=graph.get_tensor_by_name('var/b_val:0')input_placeholder=graph.get_tensor_by_name('input_placeholder:0') labels_placeholder=graph.get_tensor_by_name('result_placeholder:0') y_output=graph.get_tensor_by_name('output:0')with tf.Session() as sess:#具體權重的恢復saver.restore(sess,'./model/weight')result=sess.run(y_output,feed_dict={input_placeholder:[1]})print(result)print(sess.run(a_val))print(sess.run(b_val))

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