Neural Network Toolbox on TensorFlow
Project description
Neural Network Toolbox on TensorFlow
Tutorials are not fully finished. See some examples to learn about the framework:
Vision:
Reinforcement Learning:
Unsupervised Learning:
Generative Adversarial Network(GAN) variants, including DCGAN, InfoGAN, Conditional GAN, WGAN, Image to Image.
Speech / NLP:
The examples are not only for demonstration of the framework – you can train them and reproduce the results in papers.
Features:
Describe your training task with three components:
Model, or graph. models/ has some scoped abstraction of common models, but you can simply use any symbolic functions available in tensorflow, or most functions in slim/tflearn/tensorlayer. LinearWrap and argscope simplify large models (vgg example).
DataFlow. tensorpack allows and encourages complex data processing.
All data producer has an unified interface, allowing them to be composed to perform complex preprocessing.
Use Python to easily handle any data format, yet still keep good performance thanks to multiprocess prefetch & TF Queue prefetch. For example, InceptionV3 can run in the same speed as the official code which reads data by TF operators.
Callbacks, including everything you want to do apart from the training iterations, such as:
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
With the above components defined, tensorpack trainer will run the training iterations for you. Multi-GPU training is off-the-shelf by simply switching the trainer. You can also define your own trainer for non-standard training (e.g. GAN).
Install:
Dependencies:
Python 2 or 3
TensorFlow >= 1.0.0rc0
Python bindings for OpenCV
pip install --user -U git+https://github.com/ppwwyyxx/tensorpack.git
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