What is the correct architecture for a time series predicting LSTM neural network?
architecture, artificial-intelligence, lstm, machine-learning, neural-network
Solution
After talking to some of the professors at my university, I finally got this sorted out.
You should view a LSTM block as a single neuron in your network.
Thus this network would be regarded as a neural network with a single hidden layer with two neurons:
Problem
Concerning the structure of a LSTM network If I wanted to create LSTM network for solving time series predictions, how should I structure the hiddens layers of the neural network? - A `LSTM memory block` would represent a hidden layer and all the nodes in the layer would be represented by `cells`? - Each hidden layer should consist of numerous `LSTM memory blocks`, and a collection of such blocks will form a layer? Graphical representation: Either in this manner: Or like this ?