train-co2

#!/bin/sh

# Perform training of NN for CO2 close prediction

cd `dirname "$0"`

mkdir -p temp/db-aqua1
mkdir -p temp/db-aqua2

# TODO: Add menu or something...

AQUA1_PH_CLOSING_DB=temp/db-aqua1/ph-closing-state.db
AQUA2_PH_CLOSING_DB=temp/db-aqua2/ph-closing-state.db

if true; then
    rm -f "$AQUA1_PH_CLOSING_DB"
    rm -f "$AQUA2_PH_CLOSING_DB"

    curl http://192.168.1.10:3000/db/ph-closing-state.db -o "$AQUA1_PH_CLOSING_DB" || exit 23
    curl http://192.168.1.11:3000/db/ph-closing-state.db -o "$AQUA2_PH_CLOSING_DB" || exit 24

    cd src/jsclient || exit 10

    npm run build || exit 12

    npm run dumpco2traindata || exit 55

    cd ../../ || exit 9
fi

. ./source-me-to-activate-python-venv || exit 11

# Disable GPU because it's SLOW in our tiny network case (or small dataset)
export CUDA_VISIBLE_DEVICES=-1

python3 ./src/co2nn/trainco2.py || exit 5

tensorflowjs_converter --input_format=keras \
                       --output_format=tfjs_graph_model \
                       data/co2-model.h5 \
                       src/jsclient/server/static-ui/model.dump || exit 6