Keras Library for Deep Learning

On the list that we can focus on a bit is the Keras library. This is going to be one of the best options that we can work with when it comes to deep learning and handling all of that data you want to work with while completing data analysis. Keep in mind here that the two top numerical platforms that we can find with Python, the ones that can provide us with some of the basis that we need for research and development in Deep learning are going to include TensorFlow and Theano.

Both of those two libraries are going to have a lot of power with them, but there are some times when they can be difficult to use in a direct manner in order to create the deep learning models that we want. Because of this, we often need to bring in the Keras library to help handle some of these models. It works on top of the TensorFlow and Theano library, so you still get the power and functionality that comes with these, with some added power and ease of use with Keras to work with deep learning. Keras official doc: https://keras.io

What Is Keras?

To start, we need to take a look at some of the basics of the Keras library, what it can do for us, and the importance of learning some of this library. Keras is considered a minimalist Python library for deep learning that can run along with TensorFlow and Theano. This was a library that was developed to make implementing the models by deep learning as fast and as easy as possible, ensuring that developers and researchers are able to get things done without all of the hassles.

To use this library, the programmer needs to make sure that they have either Python 2.7 or 3.5 available on their computer. The library is also a great one because it can seamlessly execute on CPUs and GPUs given the frameworks that are underlying it. And it has been released thanks to the permissive license of MIT so that other programmers and data scientists are able to get the full benefit of using this.

The Keras library was developed and continues to be maintained by Francois Chollet, who was an engineer for Google. This library is run on the four guiding principles that include:

  1. Modularity: This one goes by the idea that a model can be understood as either a graph alone or a sequence. All of the concerns that come with the model of deep learning are going to be discrete components that the programmer can come in and combine in arbitrary ways.
  2. Minimalism: This library is about cutting down on some of the clutter and keeping things as simple as possible. We find that the Keras library is going to provide us with just enough tools and functions and features to achieve our outcome. The idea is on minimal frills and maximizing the readability.
  3. Extensibility: This one means that the library is set up on purpose so that it is easy to add and then use some of the new components within that framework. This was done so that researchers are able to try out new things and explore some of the new ideas that come their way without issues.
  4. Python. Everything that we do in this language is going to rely on the native Python language. There will be no separate model files with custom file formats. These can be nice in some cases, but often they add in a lot of hassle and confusion that is just not worth it. Having everything work with the Python language can make a big difference.

There are so many benefits that come with working on the Keras library. It was designed to be as simple as possible while making the creation of models of deep learning a bit easier than what we can see with TensorFlow or Theano. Adding it in when you want to create some of your models can really make things easier.

We can also spend some time looking at the basics of installing this library on your system. Without installing this in the proper manner, it is going to be impossible to get all of the efficiency, and all of the model creation functionality, that we want out of this whole library.

First, we need to make sure that the Python library is all set up on our system, but we are going to assume that this is already done and move on to some of the other steps that you need to take in order to make this library work for your needs. Some of the steps that a data scientist or a programmer can use in order to add the Keras library on their system, and then start using it for their needs include:

  1. OpenCV Installation Guides. This is going to be a kind of launchpad that can link to a variety of tutorials that help with the whole installing process. This can work on any of the systems that you are trying to install this library.
  2. Install Keras with TensorFlow: You will be able to use a pip in order to install both the TensorFlow and the Keras libraries in just a few minutes.
  3. You can install these with the Raspberry Pi program if you would like, but this is often hard to do on these because of the limited space, and they are hard to train ahead of time.
  4. Install the libraries of Matplotlib, Scikit-Learn, and imutils. You want to make sure that these are installed on your chosen computer first. If you can, try to get this installed onto the virtual environment as well. It is easy to install these using the pip and the following code:

$ workon # optional
$ pip install --upgrade imutils
$ pip install --upgrade scikit-learn
$ pip install --upgrade matplotlib

With the code that we just wrote above, it is not possible to get the whole program of Keras on your computer, including all of the files and everything else that you need, in order to work with this library. It is now time to try out this library a bit and see what is inside, and how you can benefit from using this library for your own needs.

Keras and TensorFlow

While we are on this topic, we need to take a look at how Keras works with the TensorFlow library. Given that the project for TensorFlow has adopted Keras as a high-level API for the newest release it seems that this library is going to be a winner. This is because so many people are going to work with Kerasalong with the TensorFlow library, that the developers of TensorFlow have seen it as one library that they should welcome.

Remember that Keras is not a library that we are able to work on all on its own. It needs to have either Theano or TensorFlow behind it to finish some of the different things that we want, and to ensure that we are able to create the models of deep learning that are necessary.

While some developers like to work with Theano to get some of this work done, others like to be able to pull up TensorFlow and this is often the choice that people prefer out of all of them. All of the features that come with TensorFlow encourage it to be one of the best out there, and when you can combine it with Keras, there is nothing you can’t do with deep learning. Similar article: TensorFlow Library for Deep Learning

The Principles of Keras

The principles that come with Keras are really unique and are part of the reason why this kind of Python library is going to be so great to work with, even when it comes to neural networks and deep learning models. To start with, Keras was created in order to be more user-friendly, easy to extend when needed, works with Python, and is modular.

One of the key benefits that come with this library is that it is designed for humans to use, rather than machines. Sometimes the API of some of the other coding libraries that we have talked about are too difficult to work with. They are designed without the user in mind and learning how to bring out the functionalities and all of the algorithms can be a pain. But, with the API that is found in Keras, that is not as big of a problem. It was designed to be used for humans, while also following some of the best practices in the industry for reducing the cognitive load.

If you have taken a look at some of the other Python libraries for deep learning models before, you may have worried that the program would be too hard to work with. And this is just not the case. Keras is set up to be easy for anyone to use, and just by snooping around inside of it for a bit of time, you will notice that it is intuitive, and easy to use, no matter how complicated your model will turn out to be.

There are also a lot of standalone modules that come with the Keras library. You will be able to combine these together as needed to create the new models that you need with Python behind them. Some of the examples of the standalone modules that you can enjoy will include the regularization schemes, activation functions, initialization schemes, optimizers, cost functions, and neural layers to name a few.

In addition to combining some of these standalone modules to create some of the new models that you want to work with, it is easy to add new modules. This is usually done as new functions or classes. The models that are done here will be defined in the code of Python, rather than having their own separate model configuration files to work with.

The biggest reasons that you and other programmers would want to work with the Keras library stem from those guiding principles that we talked about before. Primarily, the fact that Keras is so user-friendly is making big waves in this industry. Beyond the ease of learning this, and how easy it makes the process of building models, Keras is going to offer us the advantages of broad adoption, support for a lot of options of production deployment, integration with a minimum of five engines for the backend, and strong support for a lot of GPUs. We even find that it can help out with some of the distributed training that we need to complete.

If you are still uncertain that this is the right library for you, think about all of the companies that are already working with this library. It is not just programmers and a few researchers who are happy with this library and using it for their own needs, but also some of the big names in companies out there are using this library as well. These include Uber, NVIDIA, Apple, Amazon, Microsoft, and Google.

Building a Deep Learning Model with Keras

The final thing that we need to take a look at here is how we are able to work with the Keras library in order to help us build some of our own models of deep learning. The focus on Keras is going to be the idea of a model. The main model that we are able to work within this library is going to be known as a Sequence, and this is a linear stack of layers.

As a programmer, you will start out with a basic sequence, and then you can add in some layers to it, adding them in the order that you wish for them to be computed during the execution. Once you have had some time to define these, you can then compile the model you will use, which basically will make sure of the underlying framework to optimize the computation that you want to see performed by that model. During this time, the programmer or the researcher is able to specify the loss function, and the optimizer, that they want to use.

After we have had some time to go through and compile all of this information, we need to then take the model and find a method to fit it into the data. There are a few methods that we are able to use to get this done. First, you can do the fitting one batch of data at a time, or you can do it by firing off the entire regime for the model training. This is where we will see computing happen in the first place.

Now, the training process is going to take some time, and it may not be as efficient as we may hope. But the more data that we can show to the model, and the more time we take to get this done with some high-quality data, the better the model is going to perform. Once the model is properly trained, you can then use that model to look through new data presented and make some good predictions for you.

While there are a number of steps that have to happen to turn the model into something that you can use for insights and predictions, especially when it is a deep learning model, we are able to summarize this to make things a bit easier. The summary that is needed to help us explain the construction of deep learning models in Keras will include the following steps:

  1. Define the model. This means that we have to start out with creating our own sequence and then adding in the number of layers that we want to work with.
  2. Compile the model. This is the step where the programmer or the researcher is able to specify all of the optimizers, as well as the loss functions.
  3. Fit your model. This is the step where you are able to execute the model with all of the data that has been collected. This is going to include a lot of training to ensure that the model is going to work the way that we want.
  4. Make predictions. After the model has had the time to go through the proper training, we are then able to use that model to help generate some of the predictions that are needed on any new data that is presented. The model will get faster and more efficient at the predictions as time goes on and it receives more data and more feedback.

There is so much to love when it comes to working with the Keras library. Many people worry that it is going to be too hard to work with, or that they shouldn’t waste their time because this library won’t add in any functionalities or extras that we can’t get from Theano or TensorFlow. However, this is a library that is able to stand on its own. While it does rely on one or the other of those libraries above, it can make deep learning model creation easier and will ensure that we are able to see the best results with some of our models in no time.

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