Issues in your tutorial on Classifying Names with a Character-Level RNN

[Coderx7]

Looking at the following diagram and the code you wrote , which is :

import torch.nn as nn

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()

        self.hidden_size = hidden_size

        self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
        self.i2o = nn.Linear(input_size + hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, input, hidden):
        combined = torch.cat((input, hidden), 1)
        hidden = self.i2h(combined)
        output = self.i2o(combined)
        output = self.softmax(output)
        return output, hidden

    def initHidden(self):
        return torch.zeros(1, self.hidden_size)

n_hidden = 128
rnn = RNN(n_letters, n_hidden, n_categories)

I believe there are two issues here if you were trying to model vanilla RNN which its formulation is as follows:

which are based on Elman(vanilla RNN) network :

• First it does not introduce a nonlinear transformation while calculating the new hidden state,

• and Second it dose not consider the $h_t$ for the calculation of the output!

  So my question is, were you implementing the Elman network or was it a completely new variation of the RNN? In case I'm wrong what am I missing here? If you could kindly clarify this, I'd appreciate it .