键盘中断张量流运行并在此时保存

有没有办法通过键盘中断来中断张量流会话，并可以选择在此时保存模型？我目前让会话运行过夜，但需要停止它，以便我可以释放内存供电脑在白天使用。随着训练的进行，每个时期都会变慢，因此有时我可能需要等待几个小时才能进行程序中的下一个计划保存。我想要能够随时进入运行并从该点保存的功能。我什至不知道这是否可能。希望能得到指点。

一种选择是子类化tf.Session对象并创建一个__exit__当键盘中断通过时保存当前状态的函数。仅当新对象作为对象的一部分被调用时，这才有效。with block.

这是子类：

import tensorflow as tf

class SessionWithExitSave(tf.Session):
    def __init__(self, *args, saver=None, exit_save_path=None, **kwargs):
        self.saver = saver
        self.exit_save_path = exit_save_path
        super().__init__(*args, **kwargs)

    def __exit__(self, exc_type, exc_value, exc_tb):
        if exc_type is KeyboardInterrupt:
            if self.saver:
                self.saver.save(self, self.exit_save_path)
                print('Output saved to: "{}./*"'.format(self.exit_save_path))
        super().__exit__(exc_type, exc_value, exc_tb)

TensorFlow mnist 演练中的示例用法。

import tensorflow as tf
import datetime as dt
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('U:/mnist/', one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(cross_entropy)

saver = tf.train.Saver()

with SessionWithExitSave(
        saver=saver, 
        exit_save_path='./tf-saves/_lastest.ckpt') as sess:
    sess.run(tf.global_variables_initializer())
    total_epochs = 50
    for epoch in range(1, total_epochs+1):
        for _ in range(1000):
            batch_xs, batch_ys = mnist.train.next_batch(100)
            sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
        # Test trained model
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        print(f'Epoch {epoch} of {total_epochs} :: accuracy = ', end='')
        print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
        save_time = dt.datetime.now().strftime('%Y%m%d-%H.%M.%S')
        saver.save(sess, f'./tf-saves/mnist-{save_time}.ckpt')

在从键盘发送中断信号之前，我让它运行 10 个纪元。这是输出：

Epoch 1 of 50 :: accuracy = 0.9169
Epoch 2 of 50 :: accuracy = 0.919
Epoch 3 of 50 :: accuracy = 0.9205
Epoch 4 of 50 :: accuracy = 0.9221
Epoch 5 of 50 :: accuracy = 0.92
Epoch 6 of 50 :: accuracy = 0.9229
Epoch 7 of 50 :: accuracy = 0.9234
Epoch 8 of 50 :: accuracy = 0.9234
Epoch 9 of 50 :: accuracy = 0.9252
Epoch 10 of 50 :: accuracy = 0.9248
Output saved to: "./tf-saves/_lastest.ckpt./*"
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
...
--> 768   elif item[0].cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE:
    769     return item[1]._is_present_in_parent
    770   else:
KeyboardInterrupt:

事实上，我确实拥有所有保存的文件，包括发送到系统的键盘中断的保存。

import os

os.listdir('./tf-saves/')
# returns:
['checkpoint',
 'mnist-20171207-23.05.18.ckpt.data-00000-of-00001',
 'mnist-20171207-23.05.18.ckpt.index',
 'mnist-20171207-23.05.18.ckpt.meta',
 'mnist-20171207-23.05.22.ckpt.data-00000-of-00001',
 'mnist-20171207-23.05.22.ckpt.index',
 'mnist-20171207-23.05.22.ckpt.meta',
 'mnist-20171207-23.05.26.ckpt.data-00000-of-00001',
 'mnist-20171207-23.05.26.ckpt.index',
 'mnist-20171207-23.05.26.ckpt.meta',
 'mnist-20171207-23.05.31.ckpt.data-00000-of-00001',
 'mnist-20171207-23.05.31.ckpt.index',
 '_lastest.ckpt.data-00000-of-00001',
 '_lastest.ckpt.index',
 '_lastest.ckpt.meta']