How to train the MLP Deep Learning Model¶
Prerequisites¶
There are 2 prerequisites to training the MLP Deep Learning Model:
Install Dependencies¶
Run the following commands to install the necessary dependencies:
Install numpy:
conda install numpy
Install tensorflow:
conda install tensorflow
Install keras:
conda install -c conda-forge keras
Install h5py:
conda install h5py
Install protobuf:
conda install -c conda-forge protobuf
Install PyTorch:
conda install -c pytorch pytorch
Train the Model¶
The following steps are to be followed in order to train the MLP model using the released demo data. For convenience, we denote APOLLO
as the path of the local apollo repository, for example, /home/username/apollo
Create a folder to store offline prediction data using the command
mkdir APOLLO/data/prediction
if it does not existStart dev docker using
bash docker/scripts/dev_start.sh
under the apollo folderEnter dev docker using
bash docker/scripts/dev_into.sh
under apollo folderIn docker, under
/apollo/
, runbash apollo.sh build
to compileIn docker, under
/apollo/
, copy the demo record into/apollo/data/prediction
by the command:cp /apollo/docs/demo_guide/demo_3.5.record /apollo/data/prediction/
In docker, under
/apollo/
, run the bash script for feature extraction:bash modules/tools/prediction/mlp_train/feature_extraction.sh /apollo/data/prediction/ apollo/data/prediction/
, then the feature files will be generated in the folder/apollo/data/prediction/
.Exit docker, train the cruise model and junction model according to
APOLLO/modules/tools/prediction/mlp_train/cruiseMLP_train.py
andAPOLLO/modules/tools/prediction/mlp_train/junctionMLP_train.py