AI News, Difference between revisions of "Artificial Neural Networks/Activation Functions"
- On Thursday, October 4, 2018
- By Read More
Difference between revisions of "Artificial Neural Networks/Activation Functions"
There are a number of common activation functions in use with neural networks.
The output is a certain value, A1, if the input sum is above a certain threshold and A0 if the input sum is below a certain threshold.
These kinds of step activation functions are useful for binary classification schemes.
In other words, when we want to classify an input pattern into one of two groups, we can use a binary classifier with a step activation function.
Each identifier would be a small network that would output a 1 if a particular input feature is present, and a 0 otherwise.
Combining multiple feature detectors into a single network would allow a very complicated clustering or classification problem to be solved.
linear combination is where the weighted sum input of the neuron plus a linearly dependant bias becomes the system output.
In these cases, the sign of the output is considered to be equivalent to the 1 or 0 of the step function systems, which enables the two methods be to equivalent if
This is called the log-sigmoid because a sigmoid can also be constructed using the hyperbolic tangent function instead of this relation, in which case it would be called a tan-sigmoid.
Sigmoid functions in this respect are very similar to the input-output relationships of biological neurons, although not exactly the same.
Sigmoid functions are also prized because their derivatives are easy to calculate, which is helpful for calculating the weight updates in certain training algorithms.
The softmax activation function is useful predominantly in the output layer of a clustering system.
- On Thursday, September 19, 2019
Activation Functions in Neural Networks (Sigmoid, ReLU, tanh, softmax)
ActivationFunctions #ReLU #Sigmoid #Softmax #MachineLearning Activation Functions in Neural Networks are used to contain the output between fixed values ...
Neural Network Calculation (Part 2): Activation Functions & Basic Calculation
From In this part we see how to calculate one section of a neural network. This calculation will be repeated many times to ..
In a neural network, the output value of a neuron is almost always transformed in some way using a function. A trivial choice would be a linear transformation ...
Deep Learning with Tensorflow - Activation Functions
Enroll in the course for free at: Deep Learning with TensorFlow Introduction The majority of data ..
Derivative of the sigmoid activation function, 9/2/2015
Activation Function using Sigmoid & ReLU using TensorFlow
Impact of Bias on the Sigmoid Activation function
A sigmoid function is a mathematical function having an "S" shape (sigmoid curve). Often, sigmoid function refers to the special case of the logistic function ...
OptimizersLossesAndMetrics - Keras
Here I go over the nitty-gritty parts of models, including the optimizers, the losses and the metrics. I first go over the usage of optimizers. Optimizers are ...
Neural network tutorial: The back-propagation algorithm (Part 1)
In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but ...