tensorflow tf.train.Supervisor作用
tf.train.Supervisor可以简化编程,避免显示地实现restore操作.通过一个例子看.
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做一个微小的改动.如果模型已经存在,就先读取模型接着训练.tf.train.Supervisor可以简化这个步骤.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 # 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
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.如果存在,会自动读取模型.不用显式地调用restore.
