TeachNet: Fits neural networks to learn about back propagation Version 0.7

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.

 Author Georg Steinbuss Date of publication 2014-01-06 17:18:41 Maintainer Georg Steinbuss License GPL (>= 2) Version 0.7 Package repository View on CRAN Installation Install the latest version of this package by entering the following in R: install.packages("TeachNet")

Popular man pages

 confusion: Computes confusion matrix createWeights2: Creates random weights fitTeachNet2: One step in backpropagation is.decay: Checks for correct input is.err: Checks for correct input squaredError: Computes squared error transformPrediction: Transforms prediction

Man pages

accuracy.me: Computes accuracy
computeOutput1: Computes output
computeOutput2: Computes output
confusion: Computes confusion matrix
createWeights1: Creates random weights
createWeights2: Creates random weights
crossEntropy: Cross entropy
find.Threshold: Finds best threshold
fitTeachNet1: One step in backpropagation
fitTeachNet2: One step in backpropagation
is.acct: Checks for correct input
is.data: Checks for correct input
is.decay: Checks for correct input
is.err: Checks for correct input
is.learn: Checks for correct input
is.numberOfNeurons: Checks for correct input
is.sample: Checks for correct input
is.sampleLeng: Checks for correct input
is.stepMax: Checks for correct input
is.thres.error: Checks for correct input
logistic: Logistic function
logistic.differential: Differential of logistic function
predict_Weights: Computes prediction
predict_Weights2: Computes prediction
squaredError: Computes squared error
sumCrossEntropy: Sums up cross entropy
sumSquaredError: Sums up squared error
TeachNet: Fits the neural network
TeachNet-package: Fit neural networks with up to 2 hidden layers and one output...
transformPrediction: Transforms prediction
Weights2-class: Weights2 objects
Weights-class: Weights objects

Functions

*,numeric,Weights-method Man page
*,numeric,Weights2-method Man page
+,Weights,Weights-method Man page
+,Weights2,Weights2-method Man page
-,Weights,Weights-method Man page
-,Weights2,Weights2-method Man page
TeachNet Man page Source code
TeachNet-package Man page
Weights-class Man page
Weights2-class Man page
accuracy.me Man page Source code
computeOutput1 Man page Source code
computeOutput2 Man page Source code
confusion Man page Source code
createWeights1 Man page Source code
createWeights2 Man page Source code
crossEntropy Man page Source code
find.Threshold Man page Source code
fitTeachNet1 Man page Source code
fitTeachNet2 Man page Source code
is.acct Man page Source code
is.data Man page Source code
is.decay Man page Source code
is.err Man page Source code
is.learn Man page Source code
is.numberOfNeurons Man page Source code
is.sample Man page Source code
is.sampleLeng Man page Source code
is.stepMax Man page Source code
is.thres.error Man page Source code
logistic Man page Source code
logistic.differential Man page Source code
predict.Weights Man page Source code
predict.Weights2 Man page Source code
squaredError Man page Source code
sumCrossEntropy Man page Source code
sumSquaredError Man page Source code
transformPrediction Man page Source code

Files

NAMESPACE
R
R/crossEntropy.R
R/computeOutput1.R
R/sumSquaredError.R
R/createWeights2.R
R/confusion.R
R/computeOutput2.R
R/is_acct.R
R/predict_Weights.R
R/findThreshold.R
R/is_numberOfNeurons.R
R/Weights-class.R
R/is_data.R
R/is_sample.R
R/is_learn.R
R/is_sampleLeng.R
R/is_stepMax.R
R/accuracyMe.R
R/squaredError.R
R/TeachNet.R
R/predict_Weights2.R
R/fitTeachNet1.R
R/Weights2-class.R
R/transformPrediction.R
R/sumCrossEntropy.R
R/logistic.R
R/logistic_differential.R
R/createWeights1.R
R/is_decay.R
R/fitTeachNet2.R
R/is_thres_error.R
R/is_err.R
MD5
DESCRIPTION
man
man/is.sample.Rd
man/computeOutput1.Rd
man/predict_Weights.Rd
man/createWeights1.Rd
man/Weights2-class.Rd
man/TeachNet.Rd
man/sumCrossEntropy.Rd
man/is.learn.Rd
man/fitTeachNet2.Rd
man/accuracy.me.Rd
man/transformPrediction.Rd
man/is.err.Rd
man/confusion.Rd
man/is.stepMax.Rd
man/logistic.Rd
man/TeachNet-package.Rd
man/sumSquaredError.Rd
man/is.thres.error.Rd
man/is.sampleLeng.Rd
man/fitTeachNet1.Rd
man/find.Threshold.Rd