Tensorflow Saver似乎会覆盖现有的已保存变量文件 [英] Tensorflow saver seems to overwrite existing saved variable files

问题描述

我正在用Tensorflow编写神经网络代码.我做到了在每1000个纪元中保存变量.因此，我希望为不同的文件保存第1001个纪元，第2001个纪元，第3001个纪元……的变量. 下面的代码是我所做的保存功能.

I am writing neural network code in tensorflow. I made it to save variables in every 1000 epoch. So, I expect to save variables of 1001th epoch, 2001th epoch, 3001th epoch ... for different files. The code below is the save function I made.

def save(self, epoch):
    model_name = "MODEL_save"
    checkpoint_dir = os.path.join(model_name)

    if not os.path.exists(checkpoint_dir):
        os.makedirs(checkpoint_dir)
    self.saver.save(self.sess, checkpoint_dir + '/model', global_step=epoch)
    self.saver.save(self.sess, checkpoint_dir + '/model')
    print("path for saved %s" % checkpoint_dir)

调用该函数后，我将该代码保存了两次.因为我想使用'global_step = epoch'保存每1000个时代的变量历史.并希望将最新的变量保存在文件中而未指定时期. 每当满足纪元条件时，我都会调用此函数，如下所示.

I made this code to save two times once the function is called. Because I wanted to save history of variables for every 1000 epoch by using 'global_step=epoch'. And wanted to save latest variables in the file without epoch specified. I call this function whenever the epoch condition is met like below.

for epoch in xrange(self.m_total_epoch):

    .... CODE FOR NEURAL NETWORK ....

    if epoch%1000 == 1 and epoch != 1:
        self.save(epoch)

假设当前时间段是29326，我希望目录中所有已保存的文件都来自1001、2001、3001 ...29001.但是，只有26001、27001、28001、29001的文件中有一部分.我检查了是否发生了在其他计算机上.这与我的预期不同.为什么会发生?

Assuming current epoch is 29326, I expect all the saved files in the directory from 1001, 2001, 3001 ... 29001. However, there are only partial of files from 26001, 27001, 28001, 29001. I checked it happened in other computers. It is different from what I expected. Why does it happen?

推荐答案

tf.train.Saver在其构造函数中有一个max_to_keep参数，该参数仅保留最新的模型.令人惊讶的是，此max_to_keep参数的默认值为5.因此，默认情况下，您将只有最新的5个模型.

tf.train.Saver has a max_to_keep argument in its constructor that keeps only the latest models saved. And this max_to_keep argument, somewhat suprisingly, has a default value of 5. So by default, you will only have the latest 5 models.

要保留所有模型，请将此变量设置为None:

To keep all models, set this variable to None:

saver = tf.train.Saver(max_to_keep=None)

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