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use CANDLE_DATA_DIR
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Pilot1/Uno/test.sh

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set -x
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# AUC prediction model
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if [ ! -f "../../Data/Pilot1/Uno/top_21_auc_1fold.uno.h5" ]; then
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if [ ! -f "$CANDLE_DATA_DIR/Pilot1/Uno/top_21_auc_1fold.uno.h5" ]; then
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# download from http://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Pilot1/uno/top_21_auc_1fold.uno.h5
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python -c 'from candle import get_file;get_file(fname="top_21_auc_1fold.uno.h5", origin="https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Pilot1/uno/top_21_auc_1fold.uno.h5", cache_subdir="Pilot1/Uno", datadir="../../Data")'
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python -c 'from candle import get_file;get_file(fname="top_21_auc_1fold.uno.h5", origin="https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Pilot1/uno/top_21_auc_1fold.uno.h5", cache_subdir="Pilot1/Uno")'
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fi
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python uno_baseline_keras2.py --config_file uno_auc_model.txt --use_exported_data ../../Data/Pilot1/Uno/top_21_auc_1fold.uno.h5 -e 3 --save_weights save/saved.model.weights.h5
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python uno_infer.py --data ../../Data/Pilot1/Uno/top_21_auc_1fold.uno.h5 \
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python uno_baseline_keras2.py --config_file uno_auc_model.txt --use_exported_data "$CANDLE_DATA_DIR/Pilot1/Uno/top_21_auc_1fold.uno.h5" -e 3 --save_weights save/saved.model.weights.h5
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python uno_infer.py --data "$CANDLE_DATA_DIR/Pilot1/Uno/top_21_auc_1fold.uno.h5 \
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--model_file save/'uno.A=relu.B=32.E=3.O=adamax.LR=0.0001.CF=r.DF=d.DR=0.1.wu_lr.re_lr.L1000.D1=1000.D2=1000.D3=1000.D4=1000.D5=1000.FD1=1000.FD2=1000.FD3=1000.FD4=1000.FD5=1000.model.json' \
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--weights_file save/saved.model.weights.h5 \
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--partition val \

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