tensorflow tf.train.Supervisor做用

tf.train.Supervisor能够简化编程,避免显示地实现restore操做.经过一个例子看.

import tensorflow as tf
import numpy as np
import os
log_path = r"D:\Source\model\linear"
log_name = "linear.ckpt"
# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3

# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but TensorFlow will
# figure that out for us.)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b

# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

# Before starting, initialize the variables.  We will 'run' this first.
saver = tf.train.Saver()
init = tf.global_variables_initializer()

# Launch the graph.
sess = tf.Session()
sess.run(init)

if len(os.listdir(log_path)) != 0:  # 已经有模型直接读取
    saver.restore(sess, os.path.join(log_path, log_name))
for step in range(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(W), sess.run(b))
saver.save(sess, os.path.join(log_path, log_name))

这段代码是对tensorflow官网上的demo作一个微小的改动.若是模型已经存在,就先读取模型接着训练.tf.train.Supervisor能够简化这个步骤.看下面的代码.

import tensorflow as tf
import numpy as np
import os
log_path = r"D:\Source\model\supervisor"
log_name = "linear.ckpt"
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3

W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b

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

saver = tf.train.Saver()
init = tf.global_variables_initializer()

sv = tf.train.Supervisor(logdir=log_path, init_op=init)  # logdir用来保存checkpoint和summary
saver = sv.saver  # 建立saver
with sv.managed_session() as sess:  # 会自动去logdir中去找checkpoint，若是没有的话，自动执行初始化
    for i in range(201):
        sess.run(train)
        if i % 20 == 0:
            print(i, sess.run(W), sess.run(b))
    saver.save(sess, os.path.join(log_path, log_name))

sv = tf.train.Supervisor(logdir=log_path, init_op=init)会判断模型是否存在.若是存在,会自动读取模型.不用显式地调用restore.

参考资料

1. tensorflow官方文档
2. tensorflow学习笔记（二十二）：Supervisor