Simple Analysis of Model Preservation and Recycling in Tensorflow

How to carry on the simple analysis of model saving and recycling in Tensorflow, I believe that many inexperienced people are at a loss about this. Therefore, this paper summarizes the causes and solutions of the problem. Through this article, I hope you can solve this problem.

Today we are going to talk about how to use TensorFlow to save our model files and how to recycle (read) model files. When I first came into contact with TensorFlow, I didn't care about the use of model files, as long as I could run through the code without getting out of bug, everything would be all right, but with the increase of the amount of data and the increase of training time, in case of various reasons (such as the video card cable was broken, the power cable was broken, and the hand was disabled. Oh, yeah, it's all my problems. . / stall.sh) interrupted unexpectedly without saving the model file, and at that moment I felt like shit.

So the question is, do we need to start training the model all over again? the answer is definitely no, but only if the model file is saved. First of all, let's say that this model file is usually saved in binary format, so what is in it is actually the parameter values calculated according to the network structure of the training data. When we need it again, we can just extract it.

The model preservation of TensorFlow is mainly controlled by the Saver class, and then I'll give a chestnut to show how to use the Saver class. In the following code, I will mention some basic questions by the way. Students who know about it can look at the last two pictures directly.

After reading the above, have you mastered the method of simple analysis of model preservation and recycling in Tensorflow? If you want to learn more skills or want to know more about it, you are welcome to follow the industry information channel, thank you for reading!