Hi ,
I need to build an RNN (without using nn.RNN) with following specifications :

It should have set of weights [



It is a chanracter RNN.


It should have 1 hidden layer


Wxh (from input layer to hidden layer )


Whh (from the recurrent connection in the hidden layer)


W ho  (from hidden layer to output layer)


I need to use Tanh for hidden layer


I need to use softmax for output layer.


I am implemented the code . I am using CrossEntropyLoss() as loss function .
Which is giving me error as
RuntimeError: multi-target not supported at /opt/conda/conda-bld/pytorch_1513368888240/work/torch/lib/THNN/generic/ClassNLLCriterion.c:22
Here is my code for model :

class CharRNN(torch.nn.Module):

    def __init__(self,input_size,hidden_size,output_size, n_layers = 1):

        super(CharRNN, self).__init__()
        self.input_size  = input_size
        self.hidden_size = hidden_size
        self.n_layers    = 1

        self.x2h_i = torch.nn.Linear(input_size + hidden_size, hidden_size)
        self.x2h_f = torch.nn.Linear(input_size + hidden_size, hidden_size)
        self.x2h_o = torch.nn.Linear(input_size + hidden_size, hidden_size)
        self.x2h_q = torch.nn.Linear(input_size + hidden_size, hidden_size)
        self.h2o   = torch.nn.Linear(hidden_size, output_size)
        self.sigmoid = torch.nn.Sigmoid()
        self.softmax = torch.nn.Softmax()
        self.tanh    = torch.nn.Tanh()

    def forward(self, input, h_t, c_t):

        combined_input = torch.cat((input,h_t),1)

        i_t = self.sigmoid(self.x2h_i(combined_input))
        f_t = self.sigmoid(self.x2h_f(combined_input))
        o_t = self.sigmoid(self.x2h_o(combined_input))
        q_t = self.tanh(self.x2h_q(combined_input))

        c_t_next = f_t*c_t + i_t*q_t
        h_t_next = o_t*self.tanh(c_t_next)

        output = self.softmax(h_t_next)
        return output, h_t, c_t
    
    def initHidden(self):
        return torch.autograd.Variable(torch.zeros(1, self.hidden_size))

    def weights_init(self,model):
    
        classname = model.__class__.__name__
        if classname.find('Linear') != -1:
            model.weight.data.normal_(0.0, 0.02)
            model.bias.data.fill_(0)

and this is the code for training the model :

input_tensor  = torch.autograd.Variable(torch.zeros(seq_length,n_vocab))
target_tensor = torch.autograd.Variable(torch.zeros(seq_length,n_vocab))

model   = CharRNN(input_size = n_vocab, hidden_size = hidden_size, output_size = output_size)
model.apply(model.weights_init)

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr = learning_rate)

for i in range(n_epochs):
    print("Iteration", i)
    
    start_idx    = np.random.randint(0, n_chars-seq_length-1)
    train_data   = raw_text[start_idx:start_idx + seq_length + 1]
    
    input_tensor = torch.autograd.Variable(seq2tensor(train_data[:-1],n_vocab), requires_grad = True)
    target_tensor= torch.autograd.Variable(seq2tensor(train_data[1:],n_vocab), requires_grad = False).long()
    
    loss = 0
    
    h_t = torch.autograd.Variable(torch.zeros(1,hidden_size))
    c_t = torch.autograd.Variable(torch.zeros(1,hidden_size))
    
    for timestep in range(seq_length):
        
        output, h_t, c_t = model(input_tensor[timestep].view(1,n_vocab), h_t, c_t)
        
        loss += criterion(output,target_tensor[timestep].view(1,n_vocab))
        
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()
    
    x_t = input_tensor[0].view(1,n_vocab)
    h_t = torch.autograd.Variable(torch.zeros(1,hidden_size))
    c_t = torch.autograd.Variable(torch.zeros(1,hidden_size))
    
    gen_seq = []
    
    for timestep in range(100):
        output, h_t, c_t = model(x_t, h_t, c_t)
        ix = np.random.choice(range(n_vocab), p=output.data.numpy().ravel())
        x_t = torch.autograd.Variable(torch.zeros(1,n_vocab))
        x_t[0,ix] = 1
        gen_seq.append(idx2char[ix])
        
    txt = ''.join(gen_seq)
    print ('----------------------')
    print (txt)
    print ('----------------------')

Can you please help me ?
Thanks in advance.