Two Ways to Implement LSTM Network using Python - with TensorFlow and Keras - Rubik's Code

In the previous article, we talked about the way that powerful type of Recurrent Neural Networks – Long Short-Term Memory (LSTM) Networks function. They are not keeping just propagating output information to the next time step, but they are also storing and propagating the state of the so-called LSTM cell. This cell is holding four neural networks inside – gates, which are used to decide which information will be stored in cell state and pushed to output. So, the output of the network at one time step is not depending only on the previous time step but depends on n previous time steps. Ok, that is enough to get us up to speed with theory, and prepare us for the practical part – implementation of this kind of networks.