转载：tensorflow保存训练后的模型

训练完一个模型，为了以后复用，我们通常需要保存模型的结果。如果用它来实现神经网络，只需要保存神经网络中的权重值即可。建议可以使用 Saver 类保存和加载模型的结果。

1、使用tf.train.Saver.save()方法保存模型

tf.train.Saver.save(sess, , =None, =None, =’meta’, =True, =True)

2、使用 tf.train.Saver 的价值模型。方法

tf.train.Saver.(sess, )

下面的代码演示了如何使用这两个函数

import tensorflow as tf
import numpy as np
x = tf.placeholder(tf.float32, shape=[None, 1])
y = 4 * x + 4
w = tf.Variable(tf.random_normal([1], -1, 1))
b = tf.Variable(tf.zeros([1]))
y_predict = w * x + b
loss = tf.reduce_mean(tf.square(y - y_predict))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
isTrain = False
train_steps = 100
checkpoint_steps = 50
checkpoint_dir = ''
saver = tf.train.Saver()  # defaults to saving all variables - in this case w and b
x_data = np.reshape(np.random.rand(10).astype(np.float32), (10, 1))
with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    if isTrain:
        for i in xrange(train_steps):
            sess.run(train, feed_dict={x: x_data})
            if (i + 1) % checkpoint_steps == 0:
                saver.save(sess, checkpoint_dir + 'model.ckpt', global_step=i+1)
    else:
        ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess, ckpt.model_checkpoint_path)
        else:
            pass
        print(sess.run(w))
        print(sess.run(b))