similar to: Fwd: Help using the exclude option in the neuralnet package

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

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]

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))

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

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

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

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

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 <-

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

Hello I am new to R and wonder whether someone out there could send me an example on how to use the package neuralnet to fit a model to data, following the usual procedure, that is, fitting the model using the training set and then using the validation set to check the model. Moreover, the neural net model is recursive (uses its own output values)-> infinite steps ahead prediction. Many

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)

This is really a feature of SQL, not R. SQL requires that you double quote column names that start with numbers, include spaces, etc., or that are SQL key words. E.g., > d <- data.frame(Order=c("sit","stay","heel"), Where=c("here","there","there"), From=c("me","me","you")) >

I didn't get any responses to this question on stats.SE: - http://stats.stackexchange.com/questions/34415/optimization-of-models-ann-radial-basis-etc-in-r-to-target-predictor-levels What I'm looking for, using neuralnet as an example, is how to guide a model toward an output profile once the model is trained. For example: model1 <- neuralnet(formula=out1 ~ intput1 + input2 + input3 +

I'm having great difficulty debugging this problem.. I'm having intermittant problems connecting to dovecot. This started a couple of days ago following an apt-get update on the server. I'm running Ubuntu 14.04, dovecot 2.2.9. Here's my dovecot -n --- # 2.2.9: /etc/dovecot/dovecot.conf # OS: Linux 3.19.0-59-generic x86_64 Ubuntu 14.04.4 LTS auth_mechanisms = plain login

Dear all, I wrote the following batch script on a iMac, and ran it on a linux mosix cluster. tu <- read.table("cage.mm5.tags.rna_lib.CAA-CAJ.tu-reshape.table") tu_reshaped <- t(reshape(tu[1:50,], direction="wide", timevar="tu", idvar=c("rna","lib"))) write.table(tu_reshaped, "cage.mm5.tags.rna_lib.CAA-CAJ.tu-reshaped.table")

I tried to use calcMin with a function that uses a number of ... arguments (all args from resid on) besides the vector of parameters being fit. Same idea as optim, nlm, nlminb for which this form of ... syntax works. But with calcMin I get an error regarding unused arguments. No partial matches to previous arguments that I can see. Anybody know the reason or fix for this?

Hello, I am having a some problems getting SSHD to run on the Ipv6 interface. Interface/Ipv6 Address: ipv6.open-systems.org [kevin at satan kevin/xp-0.0.15] 536 $ping6 ipv6.open-systems.org PING6(56=40+8+8 bytes) 3ffe:1200:3028:ff01::cab --> 3ffe:1200:3028:ff01::caa 16 bytes from 3ffe:1200:3028:ff01::caa, icmp_seq=0 hlim=64 time=73.96 ms sshd_config: ListenAddress

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