Package: TeachNet 0.7.1

Georg Steinbuss

TeachNet: Fits Neural Networks to Learn About Backpropagation

Can fit neural networks with up to two hidden layer and two different error functions. Also able to handle a weight decay. But just able to compute one output neuron and very slow.

Authors:Georg Steinbuss

TeachNet_0.7.1.tar.gz
TeachNet_0.7.1.tar.gz(r-4.5-noble)TeachNet_0.7.1.tar.gz(r-4.4-noble)
TeachNet_0.7.1.tgz(r-4.4-emscripten)TeachNet_0.7.1.tgz(r-4.3-emscripten)
TeachNet.pdf |TeachNet.html✨
TeachNet/json (API)
NEWS

# Install 'TeachNet' in R:
install.packages('TeachNet', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 1 stars 3 scripts 119 downloads 31 exports 0 dependencies

Last updated 6 years agofrom:45202a95b3. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 15 2024
R-4.5-linuxNOTEDec 15 2024

Exports:accuracy.mecomputeGrad1computeGrad2computeOutput1computeOutput2confusioncreateWeights1createWeights2crossEntropyfind.ThresholdfitTeachNet1fitTeachNet2is.acctis.datais.decayis.erris.learnis.numberOfNeuronsis.sampleis.sampleLengis.stepMaxis.thres.errorlogisticlogistic.differentialpredict.Weightspredict.Weights2squaredErrorsumCrossEntropysumSquaredErrorTeachNettransformPrediction

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Fit neural networks with up to 2 hidden layers and one output neuronTeachNet-package
Computes accuracyaccuracy.me
Computes a gradientcomputeGrad1
Computes a gradientcomputeGrad2
Computes outputcomputeOutput1
Computes outputcomputeOutput2
Computes confusion matrixconfusion
Creates random weightscreateWeights1
Creates random weightscreateWeights2
Cross entropycrossEntropy
Finds best thresholdfind.Threshold
One step in backpropagationfitTeachNet1
One step in backpropagationfitTeachNet2
Logistic functionlogistic
Differential of logistic functionlogistic.differential
Computes predictionpredict.Weights
Computes predictionpredict.Weights2
Computes squared errorsquaredError
Sums up cross entropysumCrossEntropy
Sums up squared errorsumSquaredError
Fits the neural networkTeachNet
Transforms predictiontransformPrediction
Weights objects*,numeric,Weights-method +,Weights,Weights-method -,Weights,Weights-method Weights-class
Weights2 objects*,numeric,Weights2-method +,Weights2,Weights2-method -,Weights2,Weights2-method Weights2-class