Neural Network Toolbox on TensorFlow
Project description
Neural Network Toolbox on TensorFlow
Tutorials are not 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:
DataFlow. process data in Python, with ease and speed.
Allows you to process data in Python without blocking the training, by multiprocess prefetch & TF Queue prefetch.
All data producer has a unified interface, you can compose and reuse them to perform complex preprocessing.
Callbacks, like tf.train.SessionRunHook, plugins, or extensions. Write a callback to implement 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
Model, or graph. models/ has some scoped abstraction of common models, but you can just use symbolic functions in tensorflow or slim/tflearn/tensorlayer/etc. LinearWrap and argscope simplify large models (e.g. vgg example).
With the above components defined, tensorpack trainer runs the training iterations for you. Even on a small CNN example, the training runs 2x faster than the equivalent Keras code.
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 >= 1.0.0
Python bindings for OpenCV
pip install -U git+https://github.com/ppwwyyxx/tensorpack.git # or add `--user` to avoid system-wide installation.
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