
## Keras + Tensorpack

Use Keras to define a model a train it with efficient tensorpack trainers.

### Why?
Keras alone has various overhead. In particular, it is not efficient when working on large models.
The article [Towards Efficient Multi-GPU Training in Keras with TensorFlow](https://medium.com/rossum/towards-efficient-multi-gpu-training-in-keras-with-tensorflow-8a0091074fb2)
has mentioned some of it.

Even on a single GPU, tensorpack can run [1.2~2x faster](https://github.com/tensorpack/benchmarks/tree/master/other-wrappers)
than the equivalent Keras code. The gap becomes larger when you scale.
Tensorpack and [horovod](https://github.com/uber/horovod/blob/master/examples/keras_imagenet_resnet50.py)
are the only two tools I know that can scale the training of a large Keras model.

### Simple Examples:

[mnist-keras.py](mnist-keras.py): a simple MNIST model written mostly in tensorpack style, but use Keras model as symbolic functions.

[mnist-keras-v2.py](mnist-keras-v2.py): the same MNIST model written in Keras style.

### ImageNet Example:

[imagenet-resnet-keras.py](imagenet-resnet-keras.py):
reproduce exactly the same setting of [tensorpack ResNet example](../ResNet) on ImageNet.
It has:

+ ResNet-50 model modified from [keras.applications](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/_impl/keras/applications/resnet50.py).
	(We put stride on 3x3 conv in each bottleneck, which is different from certain other implementations).
+ Multi-GPU data-parallel __training and validation__ which scales
	+ Finished 100 epochs in 19.5 hours on 8 V100s, with >90% GPU utilization.
	+ Still slightly slower than native tensorpack examples.
+ Good accuracy (same as [tensorpack ResNet example](../ResNet))

