TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks.

It is greatly used for Machine Learning Application, Developed in 2015 by the Google Brain Team and Written in Python and C++.

I will be introducing you to 15 opensource TensorFlow projects, you would like either as a Beginner in Machine Learning, an expert or a Python/C++ Developer, exploring new possibilities.

1. SenseNet

SenseNet is a sensorimotor and touch simulator to teach AIs how to interact with their environments via sensorimotor systems and touch neurons. SenseNet is meant as a research framework for machine learning researchers and theoretical computational neuroscientists.

Features

Reinforcement learning

Supports Mac OS X and Linux (ubuntu 14.04), Rarely works on Windows(If you build Windows application, you should consider contributing to this.)

Docker and Vagrant/Virtualbox images for all platform that supports it

Has it own Dataset and you can import yours.

Link

2. Tensorflow Project Template

A simple and well-designed structure is essential for any Deep Learning project, so after a lot of practice and contributing to TensorFlow projects.

Features

OOP Focused Design

Folder Structure inline with Best Practices

Simplicity

Lets you focus on the Core part of your Project.

Link

3. Pretty Tensor – Fluent Neural Networks in TensorFlow

Features

Full power of TensorFlow is easy to use

Plays well with other libraries

Code matches model

Extensible

Link

4. TensorFlow White Paper Notes

Features

Notes broken down section by section, as well as subsection by subsection

Relevant links to documentation, resources, and references throughout

SVG versions of figures/graphs

Link

5. DeepOSM

Classify roads and features in satellite imagery, by training neural networks with OpenStreetMap (OSM) data.

Features

Download a chunk of satellite imagery

Download OSM data that shows roads/features for that area

Generate training and evaluation data

Display predictions of mis-registered roads in OSM data, or display raw predictions of ON/OFF

Link

6. DQN-tensorflow

Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

Features

Deep Q-network and Q-learning

Experience replay memory to reduce the correlations between consecutive updates

Network for Q-learning targets are fixed for intervals to reduce the correlations between target and predicted Q-values



Link

7. TensorLayer

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extensive collection of customizable neural layers to build complex AI models.

Features

Simplicity

Flexibility

Zero-cost Abstraction

Link

8. Tensorforce

Tensorforce is an open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice.

Features

Modular component-based design

Separation of RL algorithm and application

Full-on TensorFlow models

Link

9. TensorFlowOnSpark

TensorFlowOnSpark enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the number of code changes required to run existing TensorFlow programs on a shared grid.

Features

Launches the Tensorflow main function on the executors, along with listeners for data/control messages.

Leverages TensorFlow’s built-in APIs to read data files directly from HDFS.

Sends Spark RDD data to the TensorFlow nodes via a TFNode.DataFeed class.

class. Shuts down the Tensorflow workers and PS nodes on the executors.

Link

10. StellarGraph

The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data.

Features

Representation learning for nodes and edges, to be used for visualization and various downstream machine learning tasks;

Classification and attribute inference of nodes or edges;

Classification of whole graphs;

Link prediction;

Interpretation of node classification

Link

11. TensorPack

A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility

Features

Focus on training speed .

. Focus on large datasets .

. It’s not a model wrapper.

Link

12. Tensorflow Shakespeare

Neural machine translation between the writings of Shakespeare and modern English using TensorFlow

Features

word embeddings

beam search

language model reranking

Link

13. Thingscoop

Search and filter videos based on objects that appear in them using convolutional neural networks

Features

Generate a video compilation of the regions in the <files> that match <query>

that match Create a compilation video showing examples for every label recognized in the video (in alphabetic order).

Print every label that appears in <file> along with the number of times it appears.

Link

14. FastText in Tensorflow

This project is based on the ideas in Facebook’s FastText but implemented in Tensorflow. However, it is not an exact replica of fastText.

Classification is done by embedding each word, taking the mean embedding over the full text and classifying that using a linear classifier.

Features

classification of text using word embeddings

char ngrams, hashed to n bins

training and prediction program

serve models on tensorflow serving

preprocess facebook format, or text input into tensorflow records

Link

15. Tensornets

High level network definitions with pre-trained weights in TensorFlow

Features

Applicability : Many people already have their own ML workflows, and want to put a new model on their workflows.

: Many people already have their own ML workflows, and want to put a new model on their workflows. Manageability : Models are written in tf.contrib.layers , which is lightweight like PyTorch and Keras, and allows for ease of accessibility to every weight and end-point.

: Models are written in , which is lightweight like PyTorch and Keras, and allows for ease of accessibility to every weight and end-point. Readability. With recent TensorFlow APIs, more factoring and less indenting can be possible.

With recent TensorFlow APIs, more factoring and less indenting can be possible. Reproducibility. You can always reproduce the original results with simple APIs including feature extractions.

Link

Conclusion

For Beginners in Deep Learning, Machine Learning and Artificial Intelligence using TensorFlow, Contributing to these open source projects or reading the codes can help you learn a lot.