Hi I am pretty much new too pytorch and try to do sentimental analysis.

**My Input data is**

array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 8]

[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1],

[ 0, 0, 0, 0, 0, 0, 9, 5, 1, 6, 7],

[ 0, 0, 0, 0, 9, 5, 1, 6, 7, 16, 17],

[ 0, 0, 0, 0, 0, 0, 2, 1, 3, 10, 4],

[ 2, 1, 3, 10, 4, 18, 19, 20, 21, 22, 23],

[11, 1, 6, 7, 24, 12, 5, 1, 13, 25, 26],

[ 2, 2, 2, 2, 8, 3, 3, 3, 4, 4, 4],

[ 0, 0, 0, 0, 0, 0, 0, 14, 1, 27, 14],

[ 0, 11, 1, 15, 2, 28, 12, 13, 15, 3, 4]]

**Output Data**

**tensor([1., 0., 1., 1., 0., 0., 1., 0., 0., 1.]) where 1 is pos and 0 is neg review.**

**My Model**

class RNN(nn.Module):

def

init(self,n_vocab,n_embed,hidden_size):

super().init()

self.hidden_size = hidden_size

self.embedding = nn.Embedding(n_vocab+1,n_embed)

self.rnn = nn.RNN(n_embed, hidden_size, num_layers = 1, batch_first = True)def forward(self,x):

x = x

x = self.embedding(x) # batch-size x seq_length x embedding_dimension

x,_ =self.rnn(x) #batch-size x seq_length x hidden_size

x = torch.sigmoid(x[:,:,-1][:,-1])

return x

n_vocab = len(char_to_int) ## 28

n_embed = 100

hidden_size = 8

model = RNN(n_vocab,n_embed, hidden_size)

I just Need to know that the indexing I did at last is correct or not (**x = torch.sigmoid(x[:,:,-1][:,-1])**).

Sorry for such a Naive question.