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.
What you are getting as the output is the internal LSTM state.
In order to get value comparable to your labels, add a dense layer on top of it. Output dimension of dense layer would be the number of labels you want result.


If its 0 and 1, only 1 output neuron can work along with sigmoid
If there are 5 label classes, then output dimension of dense layer should also be 5