Neuron class provides LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neurons learned with Gradient descent or LeLevenberg–Marquardt algorithm

Last update: Dec 17, 2022

Overview

Neuron class

Dependencies

Neuron class needs pandas and numpy to work propertly.

Example of usage

Consider Y are targets and X are inputs.

## LNUGD

neuron = LNUGD()
prediction = 1
yn, w, e, Wall, MSE = neuron.train(Y_train, X_train, epochs=2, prediction=prediction)
yn, w, Wall, MSE, e = neuron.countSerie(Y, X, logging=False, prediction=prediction)

QNULM

neuron = QNULM()
prediction = 1
yn, w, e, Wall, MSE = neuron.train(Y_train, X_train, epochs=10, prediction=prediction)
yn, w, MSE, e = neuron.countSerie(Y, X, logging=False, prediction=prediction)

RBF

neuron = RBF()
prediction = 1
neuron.train(Y_train, X_train, prediction=prediction)
yn = neuron.count(Y,X, logging=True, beta=0.01, prediction=prediction)

MLPGD

neuron = MLPGD()
prediction = 1
yn = neuron.count(Y_train, X_train, prediction=prediction, epochs=5)
yn = neuron.count(Y, X, prediction=prediction, epochs=1)

MLPELM

neuron = MLPELM()
prediction = 1
yn = neuron.count(Y_train, X_train, prediction = prediction, epochs = 10)
yn = neuron.count(Y, X, prediction = prediction)

MLPLMWL

neuron = MLPLMWL()
prediction = 1
yn = neuron.count(Y, X, learningWindow = 50, overLearn = 10,  prediction = prediction)

Support me

If you find this useful, consider supporting independent open-source development and buy me a coffee.

Neuron class provides LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neurons learned with Gradient descent or LeLevenberg–Marquardt algorithm

Related tags

Overview

Neuron class

Dependencies

Example of usage

QNULM

RBF

MLPGD

MLPELM

MLPLMWL

Support me

Owner

Filip Molcik

Python Fanduel API (2021) - Lineup Automation

SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images

Code for paper "Document-Level Argument Extraction by Conditional Generation". NAACL 21'

FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset (CVPR2022)

EZ graph is an easy to use AI solution that allows you to make and train your neural networks without a single line of code.

Chunkmogrify: Real image inversion via Segments

Sound-guided Semantic Image Manipulation - Official Pytorch Code (CVPR 2022)

Power Core Simulator!

Github Traffic Insights as Prometheus metrics.

Code for HodgeNet: Learning Spectral Geometry on Triangle Meshes, in SIGGRAPH 2021.

Monify: an Expense tracker Program implemented in a Graphical User Interface that allows users to keep track of their expenses

Semantic Segmentation with SegFormer on Drone Dataset.

Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

Code for KHGT model, AAAI2021

An official TensorFlow implementation of “CLCC: Contrastive Learning for Color Constancy” accepted at CVPR 2021.

This repository contains all data used for writing a research paper Multiple Object Trackers in OpenCV: A Benchmark, presented in ISIE 2021 conference in Kyoto, Japan.

RAMA: Rapid algorithm for multicut problem

Official PyTorch implementation for paper "Efficient Two-Stage Detection of Human–Object Interactions with a Novel Unary–Pairwise Transformer"

LyaNet: A Lyapunov Framework for Training Neural ODEs

A toolset for creating Qualtrics-based IAT experiments