There is one glaring issue to the Relu function. This is mostly due to how fast it is to run the max function. This simple gatekeeping function has become arguably the most popular of activation functions. Yup that is it! Simple making sure the value returned doesn’t go below 0. This is executed by the programming function max(0, x). What kind of complex mathematics is going on that determine this gatekeeping function? Let us take a look at the Rectified Linear Unitreferred to as ReLU. As gate keepers they affect what data gets though to the next layer if any data at all is allowed to pass them. They sit at the end of your layers as little gate keepers. Let us do a quick recap just to make sure we know why we might want a custom one.Īctivation functions are quite important to your layers. If you are new to machine learning you might have heard of activation functions but not quite sure how they work outside of just setting the typical softmax or ReLU on your layers.
DEFINE SWISH SWICH CODE
This can be a great option to save reusable code written in Keras and to prototype changes to your network in a high level framework that allows you to move quick.
DEFINE SWISH SWICH HOW TO
It is at this point TensorFlow’s website will point you to their “expert” articles and start teaching you how to use TensorFlow’s low level api’s to build neural networks without the limitations of Keras.īefore jumping into this lower level you might consider extending Keras before moving past it. All without changing any code just a configuration file.Īt some point in your journey you will get to a point where Keras starts limiting what you are able to do. Then when you are ready for production you can swap out the backend for TensorFlow and have it serving predictions on a Linux server. If using Keras directly you can use PlaidML backend on MacOS with GPU support while developing and creating your ML model. This kind of backend agnostic framework is great for developers. Although one of my favorite libraries PlaidML have built their own support for Keras.
Using Keras you can swap out the “backend” between many frameworks in eluding TensorFlow, Theano, or CNTK officially. Keras is called a “front-end” api for machine learning. TensorFlow is even replacing their high level API with Keras come TensorFlow version 2. Keras is a favorite tool among many in Machine Learning.
Implementing Swish Activation Function in Keras
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