Looking at Swish we can see it is defined as the following: Let us go ahead and define the math behind each of these methods. However, in the world of ML branching can be too costly sometimes. As software developers we don’t think much about branching statements. This branching conditional check is expensive when compared to its linear relatives. The ELUcalculation used is dependent on the value of x. However there is one glaring issue with this function. ELU typically out preforms ReLU and its leaky cousin. The Leaky ReLU and ELU functions both try to account for the fact that just returning 0 isn’t great for training the network. There are functions that try to address this problem like the Leaky ReLU or the ELU. Once they hit 0 it is rare for the weight to recover and will remain 0 going forward. There would be no way to know one was closer to 0 than the other one because we removed this information during the forward pass. What happens during this backward pass between two neurons one of which returned a negative number really close to 0 and another one that had a large negative number? During this backward pass they would be treated as the same. In machine learning we learn from our errors at the end of our forward path, then during the backward pass update the weights and bias of our network on each layer to make better predictions. 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. 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. 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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