Sequence prediction using recurrent neural networks(LSTM) with TensorFlow
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.
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) $
(ltsm) $ pip install -r ./requirements.txt
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
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
For more details please look at this blog post Sequence prediction using recurrent neural networks(LSTM) with TensorFlow