Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

Neural Network Toolbox on TensorFlow

Project Description

Neural Network Toolbox on TensorFlow.

See some examples to learn about the framework:

Speech / NLP:

The examples are not only for demonstration of the framework – you can train them and reproduce the results in papers.

Features:

It’s Yet Another TF wrapper, but different in: 1. Not focus on models. + There are already too many symbolic function wrappers. Tensorpack includes only a few common models, and helpful tools such as LinearWrap to simplify large models. But you can use any other wrappers within tensorpack, such as sonnet/Keras/slim/tflearn/tensorlayer/….

  1. Focus on training speed.
    • Speed comes for free with tensorpack. Even on a tiny CNN example, the training runs 1.6x faster than the equivalent Keras code.
    • Data-parallel multi-GPU training is off-the-shelf to use. It is as fast as Google’s official benchmark.
    • Data-parallel distributed training is off-the-shelf to use. It is as slow as Google’s official benchmark.
  2. Focus on large datasets.
    • It’s painful to read/preprocess data through TF. Use DataFlow to load large datasets (e.g. ImageNet) in pure Python with autoparallelization.
    • DataFlow has a unified interface, so you can compose and reuse them to perform complex preprocessing.
  3. Interface of extensible Callbacks. Write a callback to implement everything you want to do apart from the training iterations, and enable it with one line of code. Common examples include:
    • Change hyperparameters during training
    • Print some tensors of interest
    • Run inference on a test dataset
    • Run some operations once a while
    • Send loss to your phone

See tutorials to know more about these features.

Install:

Dependencies:

  • Python 2 or 3

  • TensorFlow >= 1.0.0 (>=1.1.0 for Multi-GPU)

  • Python bindings for OpenCV (Optional, but required by a lot of features)

    pip install -U git+https://github.com/ppwwyyxx/tensorpack.git
    # or add `--user` to avoid system-wide installation.
    
Release History

Release History

This version
History Node

0.4.0

History Node

0.3.0

History Node

0.2.0

History Node

0.1.9

History Node

0.1.8

History Node

0.1.7

History Node

0.1.6

History Node

0.1.5

Download Files

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
tensorpack-0.4.0-py2.py3-none-any.whl (215.3 kB) Copy SHA256 Checksum SHA256 3.6 Wheel Aug 10, 2017
tensorpack-0.4.0.tar.gz (152.6 kB) Copy SHA256 Checksum SHA256 Source Aug 10, 2017

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting