Saving neural network models is an important thing to do! It can be for many reasons, but mostly because you want to quickly start using them again or because you run out of space where to store your model.
When you are starting with deep learning, it’s very common to use pre-trained networks that have been trained on similar data sets as yours. These networks may already work quite well, so you don’t need to spend time tuning them before using them.
There are several ways to save a pretrained network. You could take a look at the layer names and figure out what parts of the network correspond to each other, then just copy those into your own network. Or you could grab the weights for individual layers and put them into another network similarly connected.
This article will talk about how to save a small convolutional neural network (CNN) in particular, which has become pretty popular lately. This network was first published by Microsoft research back in 2015 and goes by the name VGG16.
You will learn two methods for saving this CNN model: raw weight export and protobuff export. Both of these formats are widely used and supported, making this one of the more practical options if you are looking to share your model.
Store the model using the h5 file format
Recent developments in deep learning have led to very powerful networks that can perform impressive tasks. Networks like VGGNet, ResNets, Inception modules, and so forth have shown how powerful neural network architectures are.
However, deploying these models onto production servers requires more than just uploading the weights and files. You also need to save the model in a certain way to ensure it will work properly.
The most common ways to do this is by storing the weight files as either an HD or JSON file, converting the weights into binary digits (bits) or base 10 numbers, then merging all of the layers together into one big layer. More advanced ways to store the weights include hexadecimal strings or even byte arrays!
This article will go over some basic ways to store your trained model efficiently in order to use them later.
Use checkpoints to save model progress
Saving models is an important thing to do!
While it may sound tedious, saving a neural network architecture can actually have significant benefits for your model.
By using this technique, you don’t need to re-train your whole network from scratch every time you want to test how well your model works. You can instead use past versions of your network as starting points, effectively creating many different networks that are all slightly modified copies of the previous one.
This is called checkpointing or warmstarting. The term “checkpoint” comes from when computer scientists used them with computational geometry algorithms back in the 1980s!
Checkpoints are not new concepts, but they haven’t really taken off in other areas of AI until very recently. Nowadays though, they are making their way into applications like image classification and natural language processing (NLP).
In fact, some state of the art NLP systems these days will actively use several pre-trained models as part of their overall system! This article will talk more about why you should be doing it too.
We will also go over three easy ways to do it in PyTorch, a popular deep learning framework. These strategies include training with batch normalization, weight decay, and early stopping. All three of these things have been shown to help prevent overfitting and improve performance on most datasets.
Use cloud storage
One of the most important things when it comes to saving any kind of files is using a good file hosting site or service.
This will ensure that your precious models are protected, and you are not spending money on expensive subscriptions if something happens.
Amazon has their own suite of tools called Amazon Machine Learning which includes an option to save your model as another binary file.
Google Cloud Storage is also a very common solution for those looking to save neural networks.
Both sites offer free levels of access which can be upgraded depending on how much data you have.
2) Pick A Friendly Format
Now that you have saved your network, you will want to pick one file format to put it into!
The easiest way to do this is use Google’s gzip compression.
gzip + zstd = win – ZinziQ
Zstd is a newer, more optimized version of gzip which uses delta encoding instead of copying chunks from next frame like gzip does.
It is almost always faster than gzip too!
3) Get An Account
After picking your file type, now you need to find a place to upload them!
You can either use pre-existing accounts such as google or amazons, or create your own with whatever tool you prefer.
Use version control
One of the most important things you can do as an aspiring computer scientist is use proper version control! This article will talk about some ways to save your models locally before transferring them to another server or giving up completely.
Version control is software that allows you to keep track of all changes made to a file, and easily restore such files at any time. Almost every piece of major software (think Microsoft Word, Photoshop, etc.) uses this technology to ensure people do not lose their work due to a hard drive crash, system error, or power outage.
In the field of AI, one of the biggest culprits of data loss is losing a model! If someone else has already trained a similar task using the same set of features, they may have saved the cost of re-training those features again. Data sets are very expensive, especially for tasks like image classification where there are lots of examples!
By using appropriate version control software, you will be able to recover all of your models quickly and effortlessly. Some popular softwares that offer feature versions include GitHub, SourceForge, Google Drive, and Amazon AWS.
Export the model to disk
A very common task when it comes to developing AI applications is saving all of your models! This includes any kind of neural network you have trained, such as for image classification or natural language processing.
Typically, people export their model using one of two methods. They either use JSON files to save the weights and biases, which are then used to reconstruct the model later, or they just copy the binary code of the model directly into another file.
The first option can be done through an interactive tool like TensorFlow’s Serving interface, whereas copying the binary code is usually performed with software that supports this (like PyTorch).
Back up your model
It is very important to back up your models when you are done using them!
Running into issues where a computer cannot find or access your trained neural network will hurt your performance.
If for some reason your laptop stops working, you will be stuck!
Luckily, there are several ways to back up your deep learning models. You can use cloud-based services like Google Cloud Storage (GCS) or Amazon Web Services (AWS).
You can also use free software such as Microsoft’s OneDrive that allows you to sync files across all of your devices.
This article will go more in depth about how to backup your models using GCS and OneDrive. For other methods, you can read our previous article here.
Make a duplicate
In deep learning, there are many different architectures or ways to organize how information is processed. Different models work better for certain types of images or videos, so it is important to know which models exist and what they do!
A common way to save a model is to make a copy you can modify. This modified version will perform almost as well as the original, but with some changes. You can then improve the modified version to be even more efficient!
There are several strategies for doing this. One is using an already trained neural network as a starting point. By changing the architecture, creating your own new model, you can get much better results than relying on someone else’s!
This article will go into detail about three different approaches to saving a pre-trained neural net in PyTorch. They are all very similar, but have different nuances depending on whether you are training from scratch or improving an old one.
Store the model using PyTorch
Recent years have seen significant growth of applications for deep learning, especially with the introduction of convolutional neural networks (CNNs). CNNs are layers of neurons that learn features of images by themselves. This has led to powerful architectures such as VGGNet, which is now widely used across many industries.
However, deploying these models comes with several hurdles. First, they can be very large — often requiring hundreds of gigabytes or even tens of terabytes of storage! Second, it can take hours or days to train them due to their computationally intensive nature.
This article will go into detail about how you can save your trained neural network using PyTorch. You will also find tips and tricks for storing pretrained models in the internet.