# Interpreting the neural network output in R?

I managed to create neural network of my data. But I am not so sure about the interpretation of the R output. I used following command to create neural network:

> net=nnet(formula = category~iplen+date_time, size=0,skip=T,lineout=T)
# weights:  3
initial  value 136242.000000
final  value 136242.000000
converged


Then I used following command to see the output:

    > summary(net)
a 2-0-1 network with 3 weights
options were - skip-layer connections
b->o i1->o i2->o
0.64 -0.46  0.15


So from the above output Can I can conclude the following diagram of neural network?:

Second question is how can I know how useful this diagram is? I mean I wanted to find the category number(target variable) from the independent variables. so now how can I decide if this network really helped me to predict the category(target variable)? What is the final output or how to find that?

Can I conclude the following output from the above n-network? :

category= -0.46(iplen)+0.15(date_time)+0.64


Regards,

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Your interpretation looks correct. You can check it yourself by calling predict on some data and comparing your calculations to predict. I first did this in a spreadsheet, and then I calculated an R neural network using metaprogramming.
By the way, the R package neuralnet draws nice diagrams, but apparently it supports only regression (not classification?).