Ok, so you read a bunch of stuff on how to do Neural Networks and how many layers or nodes you should add, and etc... But when you start to implement the actual Neural Network you face a ton of dummy errors that stop your beautiful inspirational programming.

This post talks about some errors you might face when using the neuralnet package in R.

First, remember, to user the package you should install it:

install.packages("neuralnet")

Then type

library("neuralnet")

to load the package.

Error 1

One error that might happen training your neural network is this:

nn <- neuralnet(formula1,data=new_data, hidden=c(5,3))

Error in terms.formula(formula) : invalid model formula in ExtractVars

This happens when the name of the variables in formula "formula1" are in a non desired format. For example if you named your columns (variables) as numbers (!) you would get this error. So change your column names and re-run the model!

Exemple:

label ~ 1 + 2 + 3 + 4 + 5

Change to:

label ~ v1 + v2 + v3 + v4 + v5

Error 2

Another error you might get is the following:

nn <- neuralnet(f, data=train[,-1], hidden=c(3,3))

Warning message:

algorithm did not converge in 1 of 1 repetition(s) within the stepmax

To solve this, you can increase the size of "stepmax" parameter:

nn <- neuralnet(f, data=train[,-1], hidden=c(3,3), stepmax=1e6)

If that doesn't work, you might have to change other parameters to make it converge. Try reduce the number of hidden nodes or layers. Or changing your training data size.

Error 3

The third error I want to discuss happens when actually computing the output of the neural network:

`net.compute <- compute(net, matrix.train2[,1:10])`

`Error in neurons[[i]] %*% weights[[i]] : non-conformable arguments`

This error occurs when the number of columns in the dataframe you using to predict is different from the columns used to train the neural network. The data frames used in neuralnet and compute should have the same columns and the same names!

That is it! If you faced any other dummy error with the neuralnet package send me and I can add it to the post! Good luck! :D

© 2020 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central