<div dir="ltr">Hi Abhinav,<br> <br> I had also worked on the implementation of this ann module. I shall tell what i have understood, Marcus may correct ,if I am wrong<br><br> Yeah, The feed forward procedure used here is the same as that used in ffn class. The training of a rnn is almost the same except that, one of the input of each layer is a recurrent parameter and the other is the input vector at time t.<br><br>Nikhil Yadala,<br>Btech-cse.</div><div class="gmail_extra"><br><div class="gmail_quote">On Mon, Mar 21, 2016 at 7:12 PM, Abhinav Gupta <span dir="ltr"><<a href="mailto:abhinavgupta440@gmail.com" target="_blank">abhinavgupta440@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">I got the first one. Silly doubt.<br></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><div class="gmail_quote">On Mon, Mar 21, 2016 at 7:07 PM, Abhinav Gupta <span dir="ltr"><<a href="mailto:abhinavgupta440@gmail.com" target="_blank">abhinavgupta440@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><div><div><div><div>Hii Marcus,<br></div> I have few doubts:<br></div>- in mlpack/core/optimizers/sgd/sgd_impl.hpp what is the value of the variable function. I was following mlpack/tests/recurrent_network_tests.cpp line no 95.<br></div>- while training the network we follow the same procedure (net.Train(optimizer) ). So I'm not able to figure out how is the process different for Feed Forward and Recurrent Networks. I read in comments that RecurrentLayer has recurrentParameter instead of inputParameter, but how is it creating the difference. Please can you point me in the right direction.<br><br></div>Thanks,<br></div>Abhinav<br></div>
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