similar to: An example on how to use neuralnet to predict values

Hi, I am having difficulty in training a neural network using the package "neuralnet". My neural network has 2 input neurons (covariates), 1 hidden layer with 2 hidden neurons and 2 output neurons (responses). I am training my neural network with a dataset that has been transformed so that each column is of type "numeric". The difficulty I am facing is that the responses of

Hi R, Here's a question/problem on the 'neuralnet' package for neural networks. I have more than 50 factors in each of my independent variables. When I apply the command 'neuralnet', I get the below warning message, > net.sum <- neuralnet( Sum~Var1+Var2+Var3, b, hidden=0,linear.output=TRUE) Warning message: 'predictions' will not be calculated, as at

Dear all, the exclude and constant.weights options are used as follows: exclude: A matrix with n rows and 3 columns will exclude n weights. The the first column refers to the layer, the second column to the input neuron and the third column to the output neuron of the weight. constant.weights: A vector specifying the values of the weights that are excluded from the training process and treated

Hola a todos, en primer lugar quería agradecer la ayuda recibida desde el foro con respecto a la creación de una red neuronal. Estoy utilizando el paquete Neuralnet, que me parece que es bastante bueno, pero tengo el problema que soy incapaz de hacer las predicciones del modelo. Sé que se hace con el comando "compute", pero no encuentro ningún ejemplo de cómo hacerlo. ¿Alguien me puede

Good Morning, I am using the neuralnet package in R, and am able to produce some basic neural nets, and use the output. I would like to exclude some of the weights and biases from the iteration process and fix their values. However I do not seem to be able to correctly define the exclude and constant.weights vectors. Question: Can someone point me to an example where exclude and

good night Again I ask for help to the community, as I am new at this, I have some basic questions. I am looking for packages on neural networks and so you can search found these two that I think are the most used, neuralnet, nnet. So you can test, and correct me if I'm wrong the neuralnet only accepts as input values ??nomer, did a little test data (iris) library (neuralnet)

good night Again I ask for help to the community, as I am new at this, I have some basic questions. I am looking for packages on neural networks and so you can search found these two that I think are the most used, neuralnet, nnet. So you can test, and correct me if I'm wrong the neuralnet only accepts as input values ??nomer, did a little test data (iris) library (neuralnet)

Hi, I am starting to play around with neural networks and noticed that there are several packages on the CRAN website for neural networks (AMORE, grnnR, neural, neuralnet, maybe more if I missed them). Are any of these packages more well-suited for newbies to neural networks? Are there any relative strengths / weaknesses to the different implementations? If anyone has any advice before I dive

Hello List ! I'm a bright new R user, and I encounter a problem when trying to use the neuralnet package. I have a training set with 8 inputs, and there are 3 outputs (I need 3 distinct neurones as output). Although I read the examples, and the package article, I don't know how to tell R there are 3 outputs (3 outputs neurones). Here is my actual code : # All = input data All <-

Dear Frauke, Thank you very much for taking the time to respond. You explanation was very helpful, and I now have that part figured out! Best Wishes, Dan Frauke Message: 3 Date: Mon, 12 Oct 2020 08:33:44 +0200 (CEST) From: =?UTF-8?Q?Frauke_G=C3=BCnther?= <guenther at leibniz-bips.de> To: "r-help at r-project.org" <r-help at r-project.org> Cc: William Michels <wjm1

Estimados Tengo un problema, R se cuelga (cierra, aborta, etc.) al usar códigos de neuralnet. Copio un ejemplo cualquiera para preguntar si a otros les pasa lo mismo o es un problema puntual en mi computadora (uso R revolution win 10). # install, load package install.packages(NeuralNetTools) library(NeuralNetTools) # create model library(neuralnet) AND <- c(rep(0, 7), 1) OR <- c(0, rep(1,

Muchas gracias Instale R desde CRAN y ejecutar version en R y R revolution, informa lo mismo, pero en el segundo neuralnet se cuelga, paso una captura de pantalla (Rstudio bajo r revolution también tiene problemas, no con R desde CRAN). Javier Rubén Marcuzzi Técnico en Industrias Lácteas Veterinario De: juan(uned) Enviado: martes, 3 de noviembre de 2015 12:48 Para: r-help-es en

Hi all, I am trying to develop a neural network(Multilayer perceptron) with the package 'NEURALNET'. I have some doubts on it, 1. Whether this procedure taken care about categorical input variables- The reason is I could not find any option to describe type of variable in the arguments? 2.The algorithm is providing input variables generalized weights like this, >

PLEASE REMOVE ME FROM THIS LIST SERVE IMMEDIATELY!!!!!! On Wed, Dec 12, 2012 at 6:41 PM, dada <paxkn@nottingham.ac.uk> wrote: > Hi > I would like to do neural netowrk analysis on my data. It look like this: > > drug param1 param2 param3 param4 param5 class > A 111 15 125 40 0.5 1 > B 347 13 280 55 3 2 >

I require some help in debugging this code  library(neuralnet) ir<-read.table(file="iris_data.txt",header=TRUE,row.names=NULL) ir1 <- data.frame(ir[1:100,2:6]) ir2 <- data.frame(ifelse(ir1$Species=="setosa",1,ifelse(ir1$Species=="versicolor",0,""))) colnames(ir2)<-("Output") ir3 <- data.frame(rbind(ir1[1:4],ir2))

I am exploring neural networks (adding non-linearities) to see if I can get more predictive power than a linear regression model I built. I am using the function nnet and following the example of Venables and Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have standardized variables (z-scores) such as assets, age and tenure. I have other variables that are binary (0 or 1). In

Hi, I'm currently trying desperately to get the nnet function for training a neural network (with one hidden layer) to perform a regression task. So I run it like the following: trainednet <- nnet(x=traindata, y=trainresponse, size = 30, linout = TRUE, maxit=1000) (where x is a matrix and y a numerical vector consisting of the target values for one variable) To see whether the network

As a follow-up to my email below: The input data frame to nnet() has dimensions: > dim(coreaff.trn.nn) [1] 5088 8 And the predictions from the neural network (35 records are dropped - see email below for more details) has dimensions: > pred <- predict(coreaff.nn1) > dim(pred) [1] 5053 1 So, the following line of R code does not work as the dimensions are

Sorry for these multiple postings. I solved the problem using na.omit() to drop records with missing values for the time being. I will worry about imputation, etc. later. I calculated the sum of squared errors for 3 models, linear regression, neural networks, and support vector machines. This is the first run. Without doing any parameter tuning on the SVM or playing around with the number of

Hi Dr. Snow, & R-helpers, Thank you for your reply! I hadn't heard of the {microbenchmark} package & was excited to try it! Thank you for the suggestion! I did check the reference source for which() beforehand, which included the statement to remove NAa, and I didn't have any missing values or NAs: sum(is.na(dat$gender2)) sum(is.na(dat$gender)) sum(is.na(dat$y)) [1] 0 [1] 0 [1]