@@ -34,24 +34,26 @@ The examples are not only for demonstration of the framework -- you can train th
Describe your training task with three components:
1. __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](https://github.com/ppwwyyxx/tensorpack/blob/master/examples/load-vgg16.py)).
1. __DataFlow__. process data in Python, with ease and speed.
2. __DataFlow__. tensorpack allows and encourages complex data processing.
+ Allows you to process data in Python without blocking the training, thanks to multiprocess prefetch & TF Queue prefetch.
+ All data producer has a unified interface, so they can be composed and reused to perform complex preprocessing.
+ All data producer has an unified interface, so they can be composed and reused to perform complex preprocessing.
+ Allows you to process data from Python without blocking the training, thanks to multiprocess prefetch & TF Queue prefetch.
Even on a small CNN example, it runs [2x faster](https://gist.github.com/ppwwyyxx/8d95da79f8d97036a7d67c2416c851b6) than the equivalent Keras code.
3. __Callbacks__, including everything you want to do apart from the training iterations, such as:
2. __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
3. __Model__, or graph. `models/` has some scoped abstraction of common models, but you can just use
symbolic functions available in tensorflow, or in slim/tflearn/tensorlayer.
`LinearWrap` and `argscope` simplify large models ([e.g. vgg example](https://github.com/ppwwyyxx/tensorpack/blob/master/examples/load-vgg16.py)).
With the above components defined, tensorpack trainer will run the training iterations for you.
Trainer was written with performance in mind.
Even on a small CNN example, the training runs [2x faster](https://gist.github.com/ppwwyyxx/8d95da79f8d97036a7d67c2416c851b6) 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).
