Using ReLU, Sigmoid and Tanh with PyTorch, Ignite and Lightning
Rectified Linear Unit, Sigmoid and Tanh are three activation functions that play an important role in how neural networks work....
Tag: activation functions
Why nonlinear activation functions improve ML performance – with TensorFlow 2.0 example
Machine Learning is here to stay. More and more organizations are grasping the benefits of the technology, as long as...
Overview of activation functions for neural networks
The neurons of neural networks perform operations that are linear: they multiple an input vector with a weights vector and...
How to use FTSwish with Keras?
Flatten-T Swish is a new (2018) activation function that attempts to find the best of both worlds between traditional ReLU...
What is the FTSwish activation function?
Over the last few years, we’ve seen the rise of a wide range of activation functions – such as FTSwish....
How to use ELU with Keras?
Exponential Linear Unit is a new type of activation function that attempts to resolve both the defects of traditional ReLU...
How to use PReLU with Keras?
Rectified Linear Unit, or ReLU, is considered to be the standard activation function of choice for today’s neural networks. Even...
Beyond Swish: the LiSHT activation function
Deep neural networks perform linear operations to combine weight vectors with input vectors. The values that are the outputs of...
Using Leaky ReLU with TensorFlow 2 and Keras
Even though the traditional ReLU activation function is used quite often, it may sometimes not produce a converging model. This...
Leaky ReLU: improving traditional ReLU
The Leaky ReLU is a type of activation function which comes across many machine learning blogs every now and then....
