Keras Frontend

Installing Dependencies

Updating pip

The pip version required in this subproject must be >= 19.3.

To upgrade pip:

pip install --upgrade pip

To check installed pip version:

pip -V

Importing Conda Environment:

conda env create -f keras_environment.yml --name ${KERAS_ENV_NAME}

Note: pip version MUST be > 19.3

Activating Conda Environment:

conda activate ${KERAS_ENV_NAME}

Building and Installing Frontend:

python setup.py build

python setup.py install

Download CNN Model Files

The weight (model) and data files to use with the CNN benchmarks are hosted on Git LFS and need to separately downloaded. This can be done using:

git lfs fetch 
git lfs checkout 

NOTE: Data donwload is necesary before running benchmarks

Running Benchmaks

Benchmarks under ./src/

List of benchmarks and the expected accuracies:

Activate conda environment (above) before running benchmarks

Synopsis

python src/${BENCH_NAME}.py  [hpvm_reload|keras_reload]  [frontend|keras_dump] 

Parameters:

hpvm_reload : Reloads HPVM weights (format used in model_params found here: [ADD link to Google Drive]) from directory specified in Benchmark constructor.

keras_reload: Reloads weights in Keras .h5 file format

frontend: Invokes the HPVM frontend and dumps weights in directory specified in constructor

keras_dump: Dumps keras .h5 format model weights in directory specified in constructor

Building New Benchmarks

All benchmarks inherit from the commom parent Benchmark class. Each benchmark overrides virtual functions for building the model, training, inference, and data preprocessing.

def buildModel(self): returns a keras model

def data_preprocess(self): returns X_train, y_train, X_test, y_test, X_tuner, and y_tuner data — in that order; this data will be directly used later for training and inference

def trainModel(self, model, X_train, y_train, X_test, y_test): returns a trained keras model