Sequence prediction using recurrent neural networks(LSTM) with TensorFlow
Jupyter Notebook Python
Latest commit df84145 Jan 4, 2017 @mouradmourafiq committed on GitHub fix docstring example

README.md

tensorflow-lstm-regression

This is an example of a regressor based on recurrent networks:

The objective is to predict continuous values, sin and cos functions in this example, based on previous observations using the LSTM architecture.

Install and Run

Create a Virtual Environment

It is reccomended that you create a virtualenv for the setup since this example is highly dependant on the versions set in the requirements file.

$ virtualenv ~/python/ltsm
$ source ~/python/ltsm/bin/activate
(ltsm) $

Install Requirements

This example depends on tensorflow-0.11.0 to work. You will first need to install the requirements. You will need the appropriate version of tensorflow for your platform, this example is for mac. For more details goto TAG tensorflow-0.11.0 Setup

(ltsm) $ pip install -U https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.11.0-py3-none-any.whl
(ltsm) $ pip install -r ./requirements.txt

Running on Jupyter

Three Jupyter notebooks are provided as examples on how to use lstm for predicting shapes. They will be available when you start up Jupyter in the project dir.

(ltsm) $ jupyter notebook

Other Reading

For more details please look at this blog post Sequence prediction using recurrent neural networks(LSTM) with TensorFlow