Sequence prediction using recurrent neural networks(LSTM) with TensorFlow

This post tries to demonstrates how to approximate a sequence of vectors using a recurrent neural networks, in particular I will be using the LSTM architecture, The complete code used for this post could be found here. Most of the examples I found in the internet apply the LSTM architecture to natural language processing problems, and I couldn't find an example where this architecture could be used to predict continuous values. So the task here is to predict a sequence of real numbers based on previous observations. The traditional neural networks architectures can't do this, this is why recurrent neural networks were made to address this issue, as they allow to store previous information to predict future event. First of all let's build our model, lstm_model, the model is a list of stacked lstm cells of different time steps followed by a dense layers.