Demo codes in our presentation about MXNet in AWS Seoul Summit 2017
| File Name | Description |
|---|---|
| mxnet-logistic_regression_diabetes.ipynb | The logistic regression example |
| mxnet-2hidden_fnn_diabetes.ipynb | The classification example using FNN with 2 hidden layers |
| mxnet-mnist_deep_cnn.ipynb | Example of classifying MNIST digits with a CNN |
| 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 |
You can preview all the notebooks here or using nbviewer.
Also, you can refer to the code and youtube tutorials in DeepLearningZeroToAll for more explanation.
For the speed comparison, we use these commands:
MXNet with CUDNN accelerated LSTM (MXNet using latest master)
python3 mxnet_seq2seq_cudnn_speed.pyMXNet with native LSTM
python3 mxnet_seq2seq_native_speed.pyKeras 2.0.3 with TensorFlow Backend (TensorFlow version 1.0.1)
KERAS_BACKEND=tensorflow python3 keras_seq2seq_speed.pyKeras 2.0.3 with the Theano backend (Theano version 0.9)
KERAS_BACKEND=theano python3 keras_seq2seq_speed.pyWe use a single GeForce Titan X GPU (Maxwell) + CUDNN V5.1
| Implementation | Time spent |
|---|---|
| MXNet with CUDNN LSTM | 3.70s |
| MXNet with native LSTM | 10.83s |
| Keras with TF backend | 48.68s |
| Keras with Theano backend | 49.88s |
Also for the Theano speed test, I've run the script twice and report the time took in the second turn.