by mouradmourafiq

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow

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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.

This example has been updated with a new version compatible with the tensrflow-1.1.0. This new version is using a library polyaxon that provides an API to create deep learning models and experiments based on tensorflow.

Install and Run

Create a Virtual Environment

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

To use python3

$ mkvirtualenv -p python3 ltsm
(ltsm) $

To use python2

$ mkvirtualenv ltsm
(ltsm) $

Install Requirements

Requirements for tensorflow-1.1.0 and polyaxon

(ltsm) $ pip install -r ./requirements.txt

Requirement for the old code

The old version of the code 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 ./old_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

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