123456789101112131415161718192021222324252627282930313233343536373839 |
- #! bin/bash
- # file which contains model names we want to use for simulation
- simulate_models="simulate_models_keras_corr.csv"
- # selection of four scenes (only maxwell)
- scenes="A, D, G, H"
- metric="lab"
- for label in {"0","1"}; do
- for highest in {"0","1"}; do
- for nb_zones in {4,6,8,10,12}; do
- for size in {5,10,15,20,25,30,35,40}; do
- for mode in {"svd","svdn","svdne"}; do
- FILENAME="data/deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
- MODEL_NAME="deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
- CUSTOM_MIN_MAX_FILENAME="N${size}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}_min_max"
- if grep -xq "${MODEL_NAME}" "${simulate_models}"; then
- echo "Run simulation for model ${MODEL_NAME}"
- # by default regenerate model
- python generate_data_model_random.py --output ${FILENAME} --interval "${start_index},${end_index}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
- python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
- python predict_seuil_expe_maxwell_curve.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
- python save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric}
- fi
- done
- done
- done
- done
- done
|