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.