update speed test

README.md

@@ -10,4 +10,39 @@ Demo codes in our presentation about MXNet in AWS Seoul Summit 2017
 | mxnet-seq2seq.ipynb        | The sequence-to-sequence learning example |
 | mxnet_seq2seq_cudnn_speed.py | The MXNet side script that uses cudnn accelerated LSTM for seq2seq model |
 | mxnet_seq2seq_native_speed.py | The MXNet side script that uses the native implemented LSTM for seq2seq model |
-| keras_seq2seq_speed.py     | The Keras side script for seq2seq model |
+| keras_seq2seq_speed.py     | The Keras side script for seq2seq model |
+
+You can preview all the notebooks here or using in https://nbviewer.jupyter.org/github/sxjscience/aws-summit-2017-seoul/tree/master/
+
+For the speed comparison, we use these commands:
+
+MXNet with CUDNN accelerated LSTM
+```bash
+python3 mxnet_seq2seq_cudnn_speed.py
+```
+
+MXNet with native LSTM
+```bash
+python3 mxnet_seq2seq_native_speed.py
+```
+
+Keras 2.0.3 with TensorFlow Backend (TensorFlow version 1.0.1)
+```bash
+KERAS_BACKEND=tensorflow python3 keras_seq2seq_speed.py
+```
+
+Keras 2.0.3 with the Theano backend (Theano version 0.9)
+```bash
+KERAS_BACKEND=theano python3 keras_seq2seq_speed.py
+```
+
+We use a single GeForce GTX 1080 GPU + CUDNN V5.1
+
+| Implementation | Time spent |
+| -------------- |:----------:|
+| MXNet with CUDNN LSTM | 5.34s |
+| MXNet with native LSTM |16.96s |
+| Keras with TF backend | 125.59s |
+| Keras with Theano backend | 66.24s |
+
+Also for the Theano speed test, I've run the script twice to eliminate the compilation time.