tensorflow模型的save与restore，以及checkpoint中读取变量

    创建一个NN

import tensorflow as tf
import numpy as np

#fake data
x = np.linspace(-1, 1, 100)[:, np.newaxis]  #shape(100,1)
noise = np.random.normal(0, 0.1, size=x.shape)
y = np.power(x, 2) + noise      #shape(100,1) + noise

tf_x = tf.placeholder(tf.float32, x.shape)  #input x
tf_y = tf.placeholder(tf.float32, y.shape)  #output y
l = tf.layers.dense(tf_x, 10, tf.nn.relu)   #hidden layer
o = tf.layers.dense(l, 1)                   #output layer
loss = tf.losses.mean_squared_error(tf_y, o )   #compute loss
train_op = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)

    1.使用save对模型进行保存  

sess= tf.Session()
sess.run(tf.global_variables_initializer())     #initialize var in graph
saver = tf.train.Saver()    # define a saver for saving and restor