similar to: neural networks function in R

HI, Dear R community, I am using the nnet to fit a neural network model to do classification on binary target variable (0, 1). I am using the following codes: nnet.fit<-nnet(as.factor(out) ~ ., data=train, size=5, rang=0.3, decay=5e-4, maxit=500) I want to know what is the activation function for the original inputs, is it sigmoid activation function? and what is the output activation

Hi, Please bear with me as I never use NN in R before. I have a network whose my output has, says K node. I would like to put a set of constraints on this layer. Indeed, I have two type of constraints. The first type is that their outputs should sum up to one. The second type is monotonic increasing from the first output node to the K-th node. How can I achieve this? Thank you so much in advance.

I am using the iris example came with nnet package to test AMORE. I can see the outcomes are similar to nnet with adaptative gradient descent. However, when I changed the method in the newff to the batch gradient descent, even by setting the epoch numbers very large, I still found all the iris expected class=2 being classified as class=3. In addition, all those records in the outcomes (y) are the

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

Hi folks, I want to apply a neural network to a data set to classify the observations in the different classes from a concrete response variable. The idea is to prove different models from network modifying the number of neurons of the hidden layer to control overfitting. But, to select the best model how I can choose the relevant variables? How I can eliminate those that are not significant for

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

Dear list, I am new to R and am using it to develop and test my own neural network codes. I need some training datasets that have the prediction results that should (approximately) appear when the datasets are passed through a good neural network, in order to test whether my code is working according to standards or not. Currently I am using nnet() and predict() function in nnet package to

Hi! I am studying to use some R libraries which are applied for working with artificial neural neworks (amore, nnet). Can you recommend some useful, reliable and easy to get example data to use in R for creating and testing a neural network? __________________________________________________________________ Make your browsing faster, safer, and easier with the new Internet

Hi, I'm working on a classification problem and one of the methods I'd like to use are neural networks. I've been using nnet to build a classification network. However I would like to have the probabilities associated with the prediction. Are there any implementations of probabilistic neural networks available in R? thanks,

Hello! We are a group of three students at Bielefeld University currently working on a statistical projects about neural networks. Within the framework of this project we are supposed to use the nnet-function in R and explain how it works. Since anyone of us has much experience in using R we hoped to find some information on your homepage. Unfortunatelly, we haven't been very successfull so

Hi, I am would like to ask few questions. I am trying to forecast hourly electricity prices by 24 hours ahead. I have hourly data starting from 2015*12*18 to 2017-10-24 and I have defined the data as time series as written in the code below. Then I am trying do neural network with 23 non-seasonal dummies and 1 seasonal dummy. But I don?t know whether training set is enough.( Guess it is 50

Dear R users, I'd like to ask your guidance regarding the following two questions: (i) I just finished reading Chris Bishop's book "Neural Networks for Pattern Recognition". Although the book gave me good theoretical foundation about NN, I'm now looking for something more practical regarding architecture selection strategies. Is there any good reference about "best

hi all , has anyone tried to predict a univariate time series by a neural networks packages ? please help me in this problem . I am new in R and I did not found any document that explains this problem. thanks in advance David [[alternative HTML version deleted]]

Hello everybody, I'm looking for a way to build an RBF classification network with R but I can't find any. I know there is the 'neural' package, but apparently the RBF networks I can build with that are for approximation tasks only. Is there any package I can use to build an RBF network for a classification task? I've also looked on CRAN but couldn't find any. Thank you for

Hi, I am learning CNN using MXNet R package. I am following this great tutorial about olivetti_faces reconnaissance <https://www.r-bloggers.com/image-recognition-tutorial-in-r-using-deep-convolutional-neural-networks-mxnet-package/>. In the end after building model and testing the final score was 0.975. It is great score but what can do after with this model? How can use this model for

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

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

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 all, I am trying to develop a neural network with single target variable and 5 input variables to predict the importance of input variables using R. I used the packages nnet and RSNNS. But unfortunately I could not interpret the out put properly and the documentation of that packages also not giving proper direction. Please help me to find a good package with a proper documentation for neural

Hi. I started with a file which was a sparse 982x923 matrix and where the last column was a variable to be predicted. I did principle component analysis on it and arrived at a new 982x923 matrix. Then I ran the code below to get a neural network using nnet and then wanted to get a confusion matrix or at least know how accurate the neural net was. I used the first 22 principle components only for