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There are 2 prerequisites to training the MLP Deep Learning Model:
Run the following commands to install the necessary dependencies:
conda install numpy
conda install tensorflow
conda install -c conda-forge keras
conda install h5py
conda install -c conda-forge protobuf
conda install -c pytorch pytorch
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 exist
Start dev docker using bash docker/scripts/dev_start.sh
under the apollo folder
Enter dev docker using bash docker/scripts/dev_into.sh
under apollo folder
In docker, under /apollo/
, run bash apollo.sh build
to compile
In 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
and APOLLO/modules/tools/prediction/mlp_train/junctionMLP_train.py
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