A proof of concept framework for unsupervised learning of language patterns, models, and vector representations (embeddings).

This project depends on the latest tensorflow installation (Python 3.5) along with other dependencies listed in the requirements.txt file provided in this package.

A complete installation would be something as follows:

• Make sure python 3.5 is installed.
• Create a virtualenv using virtualenv or virtualenvwrapper

export WORKON_HOME=~/dev/envs
mkdir -p $WORKON_HOME
source /usr/local/bin/virtualenvwrapper.sh
# or
source /usr/bin/virtualenvwrapper.sh
mkvirtualenv --python=/usr/bin/python3 deepsign

Note: To access the environment anytime just run workon deepsign

To install TensorFlow on the newly setup virtualenv just run the following:

workon deepsign
pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0rc0-cp35-cp35m-linux_x86_64.whl

This is the CPU only version, if you need other versions see TensorFlow installation. This should take care of the tensorflow dependency.

This is my library that simplifies the development of tensorflow models (attemps to reduce the verbose) when creating typical neural network computation graphs.

Either use some IDE like PyCharm to manage these dependencies separately by downloading the latest version of TensorX from GitHub.

Alternatively, install it in the current environment using:

workon deepsign
pip3 install --upgrade git+https://github.com/davidelnunes/tensorx.git

Note: TensorX also depends on TensorFlow, so this assumes tensorflow is already installed in the environment.

Take the requirements.txt file provided and run the following

workon deepsign
pip3 install -r /path/to/requirements.txt

workon deepsign
python -m spacy.en.download

To use a GUI backend for matplotlib

sudo apt-get install tcl-dev tk-dev