![[R logo]](../../../doc/html/Rlogo.svg)
![[Up]](../../../doc/html/left.jpg)
![[Top]](../../../doc/html/up.jpg)
| TeachNet-package | Fit neural networks with up to 2 hidden layers and one output neuron |
| *-method | Weights objects |
| *-method | Weights2 objects |
| +-method | Weights objects |
| +-method | Weights2 objects |
| --method | Weights objects |
| --method | Weights2 objects |
| accuracy.me | Computes accuracy |
| computeGrad1 | Computes a gradient |
| computeGrad2 | Computes a gradient |
| 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 |
| 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 |
| transformPrediction | Transforms prediction |
| Weights-class | Weights objects |
| Weights2-class | Weights2 objects |