Hi!
I am writing a neural network in pytorch and I am running into some issues. I am implementing an attention layer, however I get a runtime error when I want to repeat my hidden layer. My model is as follows:
class Model_P(nn.Module):
def init(self, hidden_size, input_size):
super(Model_P, self).init()
self.hidden_size = hidden_size
self.input_size = input_size
self.lstm = nn.LSTM(input_size, hidden_size, 1, bias=False, batch_first=True, bidirectional=True)
self.dropout = nn.Dropout(0.5)
self.softsign_1 = nn.Softsign()
self.attention_hidden_vector = nn.Linear(hidden_size + 2 * hidden_size, hidden_size)
self.attention_scoring_fn = nn.Linear(hidden_size, 1, bias=False)
self.dense = nn.Linear(hidden_size,1)
self.relu_1 = nn.ReLU()
def forward(self, x):
    #model en data parameters must be the same, thus convert to float
    x.float()
    output, hidden = self.lstm(x.float())
    output = self.softsign_1(output.float())
    src_len = output.shape[0]
    hidden = hidden.repeat(src_len, 1, 1)
    attn_hidden = torch.tanh(self.attn_hidden_vector(torch.cat((hidden, output), dim=2)))
    attn_scoring_vector = self.attn_scoring_fn(attn_hidden).squeeze(2)
    attn_scoring_vector = attn_scoring_vector.permute(1, 0)
    output = F.softmax(attn_scoring_vector, dim=1)
    output = self.dense(output)
    output = self.relu_1(output)

    return output

I get the following error: AttributeError: ‘tuple’ object has no attribute ‘repeat’
I have already tried to convert the tuple ‘hidden’ to a tensor, by converting the tuple to an numpy array, and then creating a tensor, via the following code:
hidden = np.asarray(hidden)
hidden = torch.from_numpy(hidden)
However this raises other erros, which I have not succeeded to solve. Does anybody know of a solution? Many thanks in advance!