# Connect a dense layer to a LSTM architecture

I am trying to implement an LSTM structure in plain numpy for didactic reason. I clearly understand how to input the data, but not how to output. Suppose I give as inputs a tensor of dimension (n, b, d) where: • n is the length of the sequence • b is the batch size (timestamps in my case) • d the number of features for each example Each example (row) in the dataset is labelled 0-1. However, when I fed the data to the LSTM, I obtain as a result the hidden state h_out which has the same dimension of the hidden size of the network. How can I obtain just a number that can be compared to my labels and properly backpropagated? I read that someone implements another dense layer on top of the LSTM, but it's not clear to me the dimensions that such layer and its weight matrix should have.