43: SWISH Activation with TensorFlow Tutorial

In this tutorial, we will learn about the SWISH activation function and how to implement it using TensorFlow. SWISH is a new activation function that has been gaining popularity in the machine learning community for its performance improvements over traditional activation functions such as ReLU.

What is SWISH Activation?

SWISH stands for “Self-gated Activation Function” and was introduced by researchers at Google in a paper published in 2017. The SWISH function is defined as:

SWISH(x) = x * sigmoid(x)

where sigmoid(x) is the sigmoid function, which outputs values between 0 and 1. The SWISH function has been shown to outperform ReLU and other activation functions in certain deep learning tasks.

Implementing SWISH Activation with TensorFlow

To implement the SWISH activation function in TensorFlow, we can define a custom activation function using the Keras API. Here’s a simple example:


import tensorflow as tf
from tensorflow.keras.layers import Layer

class Swish(Layer):
def __init__(self):
super(Swish, self).__init__()

def call(self, inputs):
return inputs * tf.sigmoid(inputs)


In this code snippet, we define a custom layer called Swish that implements the SWISH activation function. We then use this custom layer in our neural network model like any other activation function.

Conclusion

In this tutorial, we learned about the SWISH activation function and how to implement it using TensorFlow. SWISH has been shown to outperform traditional activation functions in certain deep learning tasks, and it’s worth exploring in your own projects. Give it a try and see if it improves the performance of your neural network models!