Recent developments in deep learning have left most people with no choice but to use them! With the explosion of applications for this technology, there are now almost limitless ways to apply it. Gone are the days when you could create your own neural networks or even use pre-trained models that require extensive training data to work.
Now, anyone can take advantage of these tools easily. There are many free resources available online where you can learn how to implement new architectures and train your own models. The only limit is your computer or GPU device!
At the same time though, using advanced neural network packages like TensorFlow or PyTorch can get expensive due to their large amount of underlying libraries and components.
Luckily, Google has released its AI platform under the name Cloud Machine Learning (CML) which includes all of the aforementioned software as well as more. CML is completely free until you reach the free tier usage which we will discuss later. After that, you will be asked to pay per GB of RAM used which can add up quickly if you are running several instances at once!
If you would like to keep developing with state-of-the-art machine learning techniques, then this article will help you do just that! In this article, I will go over some easy steps to save and restore your trained model within the context of a notebook interface.
Identify the important layers of the model
In recent years, neural networks have become the de-facto standard for many computer vision applications including object detection, image captioning, and speech recognition.
Neural network models are built from several different types of layer that work together to perform specific tasks. Different layers learn distinct features or patterns in your data, which you can then use to classify new examples.
The problem is, when researchers develop their own models, they often lose track of what each individual layer learns. It’s also tricky to know which aspects of a model are stable and effective compared to others.
That’s why it’s so crucial to understand how different layers contribute to the overall performance of a deep learning model.
Export the model
There are two main reasons why you would want to export your trained neural network model. The first is so that you can use it as an online tool, which means you do not need to store the model locally anymore. This could be because you run out of space or resources to train the model on, or because you have access to only limited internet services.
The second reason is so that you can take some time off research and studying, and then re-train the model later when you have more free time. That way, when you come back to study AI and deep learning, you don’t have to start from scratch!
There are several ways to export your current neural netwokr models. Some people may recommend one method over another depending on what kind of file format you wish to save yours in. In this article, we will go through all of the methods, and determine which ones are best for different situations.
Store the model in a file
There are many ways to save your deep learning model. You can use one of these methods to store your current best model!
The easiest way is using Python’s built-in pickle module. All you have to do is open up the saved network architecture as a JSON or XML file, which can then be loaded into another neural network program for use.
You may also want to consider using an online storage service like Google Drive or Dropbox so that other users can access it easily.
Compress the model
Even though you can choose to not compress your models, it is still important to do so since most deep learning software requires at least one of these three compressing methods. There are many free tools that can be used for this purpose as well!
The first step in compression is to run some tests to see if your current settings are adequate. You can use our dedicated tool to test this. Once you have validated that your current settings work well, you can then go onto the next steps which are to reduce the number of layers or dimensions in the network or reduce the amount of dropout per layer.
You can also reduce the batch size or even using early stopping to prevent overfitting.
Share the model
It is very common for researchers to share their trained models after they are done training them. This can be done through pre-existing free or paid machine learning platforms, private GitHub repositories, or even your own personal account!
By sharing your models, you allow others to use your work as a template and add tweaks to fit their needs. If someone else obtains your model, they will likely want to test it on new data so they can see how it works.
There are many reasons why people might like to have your deep neural network (DNN) model. They may need help tweaking the parameters, improving efficiency, or finding better results with more iterations.
While most of these things are great, one thing that could go wrong is someone taking your model and using it for malicious purposes. Luckily, there are ways to protect yourself from this! We will discuss those strategies here.
Move the model to another location
It is very common for deep learning researchers to use Python as their scripting language. Because most of these models are trained using neural networks, they contain large amounts of numerical data.
This can be difficult to manage when exporting your model since it contains many files with various types of information.
Most software used to develop AI requires you to upload your model either through their own interfaces or third-party APIs. This makes sharing your model slightly more complicated as you have to find a way to transfer all of this information.
Luckily, there are some great free online services that allow you to export and re-import models easily! Here are four of our favorites.
Back up your model
It is very important to back up your models before saving them! This will save you if something happens to the notebook or the computer, or someone else accesses the file.
There are several ways to backup deep learning models. Some of the most common ones include using an external hard drive, Google Cloud Storage (GCS), Amazon Web Services (AWS) cloud storage, and Python notebooks with the `save_model` method.
This article will go into detail about how to use AWS as a platform for storing your models. However, first we must discuss why backups matter.
Why should I back up my models?
As mentioned earlier, it is very important to back up your models! This goes beyond just making a copy of the model itself – what kind of information the model contains can be saved too.
For example, when you re-create a trained neural network model, you would like it to perform the same tasks that it did previously. If this isn’t the case, then there may be legal action involved!
Further, even if you don’t have formal legal actions against losing your model, people who depend on the model could potentially lose out due to not being able to utilize it anymore.
Not only does having a backup help protect you from loss, but it also helps you retain knowledge by keeping a record of the model.
Transfer the model to a different machine
Since most of the time it takes for deep learning models to converge is spent running training, saving your current state of the network and deploying this saved network onto another computer or device can save you money and energy!
There are several ways to do this. The easiest way is using one of the free online storage services that offer neural networks as a toolkit. These tools give you access to all the functions of the network you have uploaded.
You can also use Google Cloud Platform (GCP) which offers paid versions of their AI platform with additional features. This article will focus mostly on how to transfer a trained Keras model to GCP via PyCloud.
PyCloud is an open source project that allows you to easily upload and manage any Python library or notebook file from your own account on PyCloud. You then get direct integration into many of the other products and services within Google, making it easy to sync and share files. It even has built-in encryption so no one else but you can view what’s inside.
