# How to control the learning rate in R nnet? [closed]

I am dealing with the nnet package in R. I know that the momentum $\alpha$ is used to decrease the fluctuations in weight changes over consecutive iterations. The original update of the weights states that:

$$\omega_i(t+1) = \omega_i - \eta\frac{\partial E}{\partial w_i},$$ where $E({\bf w})$ is the error function, ${\bf w}$ - the vector of weights and $\eta$ - learning rate.

The weight decay $\lambda$ penalizes the weight changes in the following way:

$$\omega_i(t+1) = \omega_i - \eta\frac{\partial E}{\partial w_i} - \lambda\eta\omega_i$$

Now, what I see on the nnet package is that I can control the $\lambda$ through the parameter "decay". How can I control the learning rate ($\eta$) instead?

## closed as off-topic by Sycorax, Michael Chernick, Juho Kokkala, kjetil b halvorsen, COOLSerdashJul 1 '18 at 15:13

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