Neural Networks: Neurons, Perceptrons, and Sigmoid Neurons
Delve into the fundamentals of neural networks including neurons, perceptrons, and sigmoid neurons. Explore how these components function and interact in artificial intelligence systems for various applications.
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ECE 8443 Pattern Recognition UNVS 0822 Demystifying Technology LECTURE 05: Neural Networks Topics: What is a neuron? Input/output characteristics? Networks of neurons Resources: Artificial Neural Network The Neural Network Zoo
Neural Networks Artificial neural networks (ANNs) are algorithms modeled after how the human brain operates. ANNs today perform a number of functions including feature extraction, signal modeling and language or domain modeling. Neural networks are comprised of nodes, which combine the inputs from the data with a set of coefficients or weights and determine whether the signal will progress further through the network. UNVS 0822: Lecture 05, Slide 1
Nodes in Neural Networks The products between the inputs ?? and the weight ? are summed in the node and the result is passed to the node s activation function, which determines whether the node will fire. Perceptrons are the most fundamental and simplest models of neurons. Node Diagram UNVS 0822: Lecture 05, Slide 2
Perceptrons Perceptrons are very simple. They take several binary inputs and generate a single binary output. Given a set of inputs ??,??,??, and a set of corresponding weights ?, ?, ?, the neuron s output is given by: ? ?? ????? ????????? ?????? = ? ?? ?????> ????????? To simplify, ? and ? can be seen as vectors and the threshold is presented as a bias b, where ???? ????????? ?? ? ? ?????? = ? ?? ? ? + ? ? ? ?? ? ? + ? > ? ?? ?????? ? ?? UNVS 0822: Lecture 05, Slide 3
Sigmoid Neurons Learning algorithms involve the adjustment of the weights and bias If the neuron model used is the perceptron, a small change in a weight or bias can cause output to completely flip (being a binary model), and the flip can cause a change in the behavior of the entire network A new type of artificial neuron, a sigmoid neuron, is commonly used to overcome this issue ? In this case, the output is given by ? ? ?+??, where ? = ? ? + ? This means, the activation function is a logistic function. Other common activation function is the tanh function, given by ???? ? =?? ? ? ??+? ? UNVS 0822: Lecture 05, Slide 4
Networks of Nodes UNVS 0822: Lecture 05, Slide 5
Lab Exercise / Homework #2 Log into neuronix.nedcdata.org Save your old workspace: mv experiment experiment_old Create a new experiment: mkdir experiment cd experiment cp -r /data/isip/exp/theano/exp_0018/scripts . Run: python ./scripts/cpu_scripts/mnist_logistic.py Extract the results from the files and plot in Excel (as shown above): validation_report.txt test_report.txt Homework #2: Run: ./scripts/cpu_scripts/mnist_mlp.py Generate a comparison plot such as that shown above. UNVS 0822: Lecture 05, Slide 6
Summary A neural network attempts to emulate properties observed in the human brain: a simple computational element is used that emulates the basic properties of a neuron. By combining large numbers of these nodes in various topologies, we can solve complex engineering and scientific problems. Nodes can use a variety of activation functions ranging from simply hard limiters to soft mappings such as a sigmoid function. In this class we learned how to interpret the output and run a new network known as a multilayer perceptron. UNVS 0822: Lecture 05, Slide 7
