The Python Libraries for Deep Learning
At this point, we have talked a lot about deep learning and data analysis, and now it is time to take some of that information, and put it to good use. You are probably interested in deep learning, and maybe even in making some of your own Convolutional Neural Networks, but are wondering where you should start. The best step is to pick out the library that you want to use. But this brings up another challenge because there are just so many coding libraries out there that you can choose from, and all of them have some amazing power and features behind them.
To start with, we are going to take a look at some of the best Python libraries that can help with deep learning. There are other languages that can help with things like machine learning and deep learning. But for most of the tasks that you want to do, especially if you are a beginner in data analysis and all of the processes that we have been talking about, then Python is going to be the choice for you. Even within Python, there are a number of libraries that you can choose from to get your deep learning work done. So, with that in mind, let’s dive right in and see some of the best Python deep learning libraries that you can use for your data analysis. Similar article: 20 Deep Learning Applications
It is pretty hard to get started with a look at deep learning libraries through Python without spending some time talking about the Caffe library. In fact, it is likely that if you have done any research on deep learning at all, then you have heard about Caffe and what it can do for some of the projects and models that you want to create.
While Caffe is technically not going to be a Python library, it is going to provide us with some bindings into the Python language. We are going to use these bindings when it is time to deploy the network in the wild, rather than just when we try to train the model. The reason that we are going to include it in this chapter is that it is used pretty much everywhere and on all of the parts of a deep learning model that you need to create.
The next kind of library that we are able to work with is known as Theano. This one has helped to develop and work with a lot of the other deep learning libraries that we have that work with Python. In the same way that a programmer would not be able to have some options like scikit-image, scikit-learn, and SciPy without NumPy, the same thing can be said when we talk about Theano and some of the other higher-level abstractions and libraries that come with deep learning.
When we take a look at the core of this, Theano is going to be one of the Python libraries that not only helps out with deep learning, but can be used to define, optimize, and evaluate a lot of mathematical expressions that will involve multi-dimensional arrays. Theano is going to accomplish this because it is tightly integrated with the NumPy library, and it keeps its use of GPU pretty transparent overall.
While you are able to use the Theano library to help build up some deep learning networks, this one is often seen as the building blocks of these neural networks, just like how the NumPy library is going to serve as the building blocks when we work on scientific computing. In fact, most of the other libraries that we will talk about as we progress through all of this are going to wrap around the Theano library, which really makes it more accessible and convenient than some of the other options.
Similar to what we are able to find with the Theano library, TensorFlow is going to be an option that is open-sourced and can work with numerical computation with the help of a data flow graph. This one was originally developed to be used with research on the Google Brain Team within Google’s Machine Intelligence organization. And this library, since that time, has turned into an open-sourced option so that the general public can use it for their deep learning and data science needs.
One of the biggest benefits that we are going to see with the TensorFlow library, compared to what we see with Theano, is that it is able to work with distributed computing. This is particularly true when we look at multiple GPUs for our project, though Theano is working on improving this one as well.
Many programmers find that they love working with the Keras library when it comes to performing models and other tasks with deep learning. Keras is seen as a modular neural network library that is more minimalistic than some of the others that we talk about. This one is able to use either TensorFlow or Theano as the backend so you can choose the one that works the best for any needs you have. The primary goal that comes with this library is that you should be able to experiment on your models quickly and get from the idea that you have over to the result as fast as possible.
Many programmers like this library because the networks that you architect are going to feel almost natural and really easy, even as a beginner. It is going to include some of the best algorithms out there for optimizers, normalization, and even activation layers so this is a great one to use if your process includes these.
In addition, if you want to spend some time developing your own CNNs, then Keras is a great option to work with. Keras is set up to place a heavy focus on these kinds of neural networks, which can be valuable when you are working from the perspective of computer vision. Keras also allows us to construct both sequence-based networks, which means that the input is going to be able to flow linearly throughout that network and the graph-based network, which is where the inputs are able to skip over some of the layers if needed, only to be concatenated at a later point. This is going to make it easier for us to implement network architectures that are more complex.
One thing to note about this Python library is that it is not going to support some of the multi-GPU environments if you would like to train a network in parallel. If this is something that you want to do, then you may need to choose another library that you want to use. But for some of the work that you want to do, this may not be a big issue.
If you want to get your network trained as fast as possible, working with a library like MXNet may be a better choice. But if you are looking to tune your hyperparameters, then you may want to work with the capability of Keras to set up four independent experiments and then evaluate how the results are similar or different between each of these.
There are going to be times when working with deep learning when you will want to train a CNN end-to-end. And then there are times when this is not needed. Instead, when this is not needed, you can treat your CNN as the feature extractor. This is going to be the most useful with some situations you may encounter where there is just not enough data to train the CNN from scratch. So, with this one, just pass your input images through a popular pre-trained architecture that can include some options like VGGNet, AlexNet, and OverFeat. You can then use these pre-trained options and extract features from the layer that you want, usually the FC layers.
To sum this up, this is exactly what you want to have happened when you bring out this kind of library. You are not able to train a model from scratch with it so if this is your goal, then you will need to look for some other library to focus on. However, it is one of the best options to choose when you want a library that can evaluate whether a particular problem is going to be suitable for deep learning and other processes.
A good library for you to work with is the nolearn library. This is a good one to help out with some initial GPU experiments, especially with a MacBook Pro. It is also a good library to help out with performing some deep learning on an Amazon EC2 GPU instance.
While Keras wraps TensorFlow and Theano into a more user-friendly API, you will find that the nolearn library will be able to do the same, but it will do this with the Lasagna library. In addition, all of the code that we find with nolearn is going to be compatible with Scikit-Learn, which is a big bonus for a lot of the projects that you want to work with.
The first thing to notice with this library is that it isn’t considered a true deep learning library. Although it is written out in Python and it stands for Deep Learning GPU Training System. The reason for this is because this library is more of a web application that can be used for training some of the models of deep learning that you create with the help of Caffe. You could work with the source code a bit to work with a backend other than Caffe, but this is a lot of extra work in the process. And since the Caffe library is pretty good at what it does, and can help with a lot of the deep learning tasks that you want to accomplish, it is really not worth your time.
If you have ever spent some time working with the Caffe library in the past, you can already attest to the fact that it is tedious to define your .prototxt files, generate the set of data for the image, run the network, and babysit the network training with the terminal that you are provided. The good news here is that the DIGITS library aims to fix all of this by allowing you to complete a lot of these tasks, if not all of these tasks, just from your browser. So, it may not be a deep learning library per se, but it does come into use when you struggle with the Caffe library.
In addition to all of the benefits above, the interface that the user gets to interact with is seen as excellent. This is due to the fact that it can provide us with some valuable statistics and graphs to help you rain your model more effectively. You can also easily visualize some of the activation layers of the network to help with various inputs as needed.
And finally, another benefit that is possible with this library is that if you come in with a specific image that you want to test, you have a few options on how to get this done. The first choice is to upload the image over to the DIGITS server, or you can enter in the URL that comes with the image, and then the model you make with Caffe will automatically be able to classify the image and display the results that you want in the browser.
Python is one of the best coding languages available for helping with tasks like deep learning, machine learning, and even with the topic of artificial intelligence, which encompasses both of the other two ideas. There are other languages that can handle the deep learning that we have been talking about, but none are going to be as effective, as powerful, have as many options, or be designed for a beginner in the way that Python can.
And this is exactly why we have focused our attention on the Python language and some of the best libraries that we are able to choose to help with a variety of deep learning tasks. Each of these libraries can come on board with your project and will provide us with a unique set of functions and skills to get the job done. Take a look through some of these libraries and see which one is going to be just right for your data analysis and for providing you with great insights while completing deep learning.