@@ -32,31 +32,23 @@ Examples are not only for demonstration of the framework -- you can train them a
## Features:
It's Yet Another TF wrapper, but different in:
1. It's not a model wrapper.
+ There are already too many symbolic function wrappers.
Tensorpack includes only a few common models,
but you can use any other model wrappers within tensorpack, such as sonnet/Keras/slim/tflearn/tensorlayer/....
2. Focus on __training speed__.
+ Speed comes for free with tensorpack -- it uses TensorFlow in the __correct way__, therefore doesn't introduce overhead on its side.
1. Focus on __training speed__.
+ Speed comes for free with tensorpack -- it uses TensorFlow in the __correct way__ with no extra overhead.
On various CNNs, it runs 1.5~1.7x faster than the equivalent Keras code.
+ Data-parallel multi-GPU/distributed training is off-the-shelf to use. It is as fast as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks).
+ See [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks) for some benchmark scripts.
3. Focus on __large datasets__.
2. Focus on __large datasets__.
+ It's painful to read/preprocess data through TF.
Tensorpack helps you load large datasets (e.g. ImageNet) in __pure Python__ with autoparallelization.
It also naturally works with TF Queues or tf.data.
4. 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
+ Monitor GPU utilization
+ Send error rate to your phone
3. It's not a model wrapper.
+ There are already too many symbolic function wrappers.
Tensorpack includes only a few common models,
but you can use any other wrappers within tensorpack, including sonnet/Keras/slim/tflearn/tensorlayer/....
See [tutorials](http://tensorpack.readthedocs.io/en/latest/tutorial/index.html) to know more about these features.
