Hi, I am trying to build a VERY basic neural network as a practice before hopefully increasing my scope. To do so, I have been using package "neural" and the MLP related functions (mlp and mlptrain) within that package.
So far, I have created a basic network, but I have been unable to change the default activation function. If someone has a suggestion, please advise. The goal of the network is to properly classify a number as positive or negative. Simple 1-layer network with a single neuron in each layer. Rcode: trainInput <- matrix(rnorm(10)) trainAnswers <- ifelse(trainInput <0, -1, 1) trainNeurons <- 1 trainingData <- mlptrain(inp=trainInput, neurons=trainNeurons, out=trainAnswers, it=1000) ## To call this network, we can see how it works on a set of known positive and negative values testInput <- matrix(-2:2) mlp(testInput, trainingData$weight, trainingData$dist, trainingData$neurons, trainingData$actfns) Will vary - but output on my computer was: [,1] [1,] 0.001043291 [2,] 0.001045842 [3,] 0.072451270 [4,] 0.950744548 [5,] 0.950931168 So it's instead classifying the negatives as 0 and positives as 1 (getting close to, anyhow - increasing the number of iterations, ie it=5000, makes that more clear) This results in a neural net with activation function 1/(1+exp(-x)) - which will never result in the -1 value that the answers contain. The documentation for package neural specifies the parameter "actfns", which should be a list containing the numeric code for the activation functions of each layer - however, anytime I try to put in a value for "actfns" (such as actfns=2 for hyperbolic tangent), I get the error: "Different activation function and active layer number" If anyone can shed light on what I'm doing wrong here with the activation functions or how to change the activation functions, I'd really appreciate it. Thanks! [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.