Question

有没有办法通过键盘中断来打破张量流会话，并且可以选择在那时保存模型？我目前离开会议一夜之间运行，但需要停止它，以便我可以在白天释放内存供PC使用。随着训练的进行，每个时期变得更慢，因此有时我可能需要等待数小时才能在程序中进行下一次计划的保存。我希望能够在任何时候进入运行并从那时起保存的功能。我甚至无法找到是否可能。会很感激指针。

Answer 1

一个选项是子类化tf.Session对象并创建一个__exit__函数，在键盘中断通过时保存当前状态。这仅在新对象作为with块的一部分调用时才有效。

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)

来自TensorFlow mnist演练的示例用法。

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，我让它运行了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，我确实拥有所有已保存的文件，包括发送到系统的键盘中断保存。

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']

键盘中断张量流在此时运行并保存

1 个答案: