ReLU has been the best activation function in the deep learning community for a long time, but Google’s brain team announced Swish as an alternative to ReLU in 2017. Research by the authors of the papers shows that simply be substituting ReLU units with Swish units improves the classification accuracy on ImageNet by 0.6% for Inception-ResNet-v2, hence, it outperforms ReLU in many deep neural nets.
Swish Activation function:
- Mathematical formula: Y = X * sigmoid(X)
- Bounded below but Unbounded above: Y approach to constant value at X approaches negative infinity but Y approach to infinity as X approaches infinity.
- Derivative of Swish, Y’ = Y + sigmoid(X) * (1-Y)
- Soft curve and non-monotonic function.
Advantages over RelU Activation Function:
Having no bounds is desirable for activation functions as it avoids problems when gradients are nearly zero. The ReLU function is bounded above but when we consider the below region then being bounded below may regularize the model up to an extent, also functions that approach zero in a limit to negative infinity are great at regularization because large negative inputs are discarded. The swish function provides it along with being non-monotonous which enhances the expression of input data and weight to be learnt.
Below is the performance metric of Swish function over many community dominant activation functions like ReLU, SeLU, Leaky ReLU and others.
Implementation of Swish activation function in keras:
Swish is implemented as a custom function in Keras, which after defining has to be registered with a key in the Activation Class.
Now We will be creating a custom function named Swish which can give the output according to the mathematical formula of Swish activation function as follows:
Now as we have the custom-designed function which can process the input as Swish activation, we need to register this custom object with Keras. For this, we pass it in a dictionary with a key of what we want to call it and the activation function for it. The Activation class will actually build the function.
Code: Implementing the custom-designed activation function
- keras.fit() and keras.fit_generator()
- Keras vs PyTorch
- ML | Word Encryption using Keras
- Colorization Autoencoders using Keras
- Keras.Conv2D Class
- Softmax Regression Using Keras
- Building an Auto-Encoder using Keras
- Python | Image Classification using keras
- Building a Generative Adversarial Network using Keras
- ML - Saving a Deep Learning model in Keras
- Creating a Keras Callback to send notifications on WhatsApp
- OpenCV and Keras | Traffic Sign Classification for Self-Driving Car
- Choose optimal number of epochs to train a neural network in Keras
- Using Google Cloud Function to generate data for Machine Learning model
- Python | Get a google map image of specified location using Google Static Maps API
- Download Anything to Google Drive using Google colab
- How to use Google Colab
- How to use Vision API from Google Cloud | Set-2
- How Does Google Use Machine Learning?
- Search Google Using Python Selenium
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.