tensorflow学习——tf.train.Supervisor()与tf.train.saver()

1、tf.train.Supervisor()

import tensorflow as tf
import numpy as np
import os

log_path = 'ckptdir/'
log_name = 'liner.ckpt'
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.1 + 0.3

w = tf.Variable(tf.random_normal([1]))
b = tf.Variable(tf.zeros([1]))
y = w*x_data + b

loss = tf.reduce_mean(tf.square(y-y_data))
train = tf.train.AdamOptimizer(0.5).minimize(loss)

tf.summary.scalar('loss', loss)

saver = tf.train.Saver()
init = tf.global_variables_initializer()
merged = tf.summary.merge_all()

sv = tf.train.Supervisor(logdir=log_path, init_op=init)  # logdir用来保存checkpoint和summary
saver = sv.saver   # 创建saver

with sv.managed_session() as sess:  # 会自动去logdir中去找checkpoint，如果没有的话，自动执行初始化
#    sess.run(init)
#    if len(os.listdir(log_path)) != 0:
#        saver.restore(sess, os.path.join(log_path, log_name))
for step in range(201):
sess.run(train)
if step%50 == 0:
print(step, sess.run(w), sess.run(b))
merged_summary = sess.run(merged)
sv.summary_computed(sess, merged_summary,global_step=step)
saver.save(sess, os.path.join(log_path, 'liner.ckpt'))

从上面代码可以看出，Supervisor帮助我们处理一些事情

（1）自动去checkpoint加载数据或初始化数据

（2）自身有一个Saver，可以用来保存checkpoint

（3）有一个summary_computed用来保存Summary

所以，我们就不需要：

（1）手动初始化或从checkpoint中加载数据

（2）不需要创建Saver，使用sv内部的就可以

（3）不需要创建summary writer

2、tf.train.Saver()

import tensorflow as tf
import numpy as np
import os

log_path = 'ckptdir'
log_name = 'liner.ckpt'
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.1 + 0.3

w = tf.Variable(tf.random_normal([1]))
b = tf.Variable(tf.zeros([1]))
y = w*x_data + b

loss = tf.reduce_mean(tf.square(y-y_data))
train = tf.train.AdamOptimizer(0.5).minimize(loss)
tf.summary.scalar('loss', loss)

saver = tf.train.Saver()
init = tf.global_variables_initializer()
merged = tf.summary.merge_all()

with tf.Session() as sess:
sess.run(init)
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(os.path.join(log_path, log_name))
restore_saver.restore(sess, checkpoint)
#if len(os.listdir(log_path)) != 0:
#    saver.restore(sess, os.path.join(log_path, log_name))
for step in range(201):
sess.run(train)
if step%50 ==0:
print(step, sess.run(w), sess.run(b))
summary_writer = tf.summary.FileWriter(log_path, sess.graph)
summary_all = sess.run(merged)
summary_writer.add_summary(summary_all)
summary_writer.close()

saver.save(sess, os.path.join(log_path, 'liner.ckpt'))