Neural Network Toolbox on TensorFlow
Still in development, but usable.
See some interesting examples to learn about the framework:
- DoReFa-Net: training binary / low bitwidth CNN
- Double-DQN for playing Atari games
- ResNet for Cifar10 classification
- IncpetionV3 on ImageNet
- char-rnn language model
Abstract your training task into three components:
-
Model, or graph.
models/has some scoped abstraction of common models. This part is roughly an equivalent of slim/tflearn/tensorlayer.LinearWrapandargscopemakes large models look simpler. -
Data. tensorpack allows and encourages complex data processing.
- All data producer has an unified
DataFlowinterface, allowing them to be composed to perform complex preprocessing. - Use Python to easily handle your own data format, yet still keep a good training speed thanks to multiprocess prefetch & TF Queue prefetch. For example, InceptionV3 can run in the same speed as the official code which reads data using TF operators.
- All data producer has an unified
-
Callbacks, including everything you want to do apart from the training iterations. Such as:
- Change hyperparameters during training
- Print some variables of interest
- Run inference on a test dataset
- Run some operations once a while
- Send the accuracy 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.
- Python 2 or 3
- TensorFlow >= 0.8
- Python bindings for OpenCV
- other requirements:
pip install --user -r requirements.txt
pip install --user -r opt-requirements.txt (some optional dependencies, you can install later if needed)
- Use tcmalloc whenever possible
- Enable
import tensorpack:
export PYTHONPATH=$PYTHONPATH:`readlink -f path/to/tensorpack`