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Authors: harald @schil.ly, Testing CoCalc, John Jeng, Harald Schilly, Harald Schilly, ℏal Snyder, William A. Stein
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Description: Jupyter html version of r-neuralnet.ipynb
r-neuralnet

R's Neuralnet

Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented.

https://cran.r-project.org/web/packages/neuralnet/index.html

In [1]:
require(neuralnet)
XOR <- c(0,1,1,0)
xor.data <- data.frame(expand.grid(c(0,1), c(0,1)), XOR)
net.xor <- neuralnet( XOR~Var1+Var2, xor.data, hidden=2, rep=5)
plot(net.xor, rep="best")
Loading required package: neuralnet
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