├── .gitignore ├── LICENSE ├── README.md ├── datasets └── CMAPSS │ ├── README.md │ ├── cmapss_preprocessing.py │ └── download_cmapss.sh ├── environment.yml ├── evaluate.py ├── evaluate.sh ├── models ├── abstract_model.py ├── bayesian_conv2_pool2.py ├── bayesian_conv5_dense1.py ├── bayesian_dense3.py ├── frequentist_conv2_pool2.py ├── frequentist_conv5_dense1.py └── frequentist_dense3.py ├── results ├── CMAPSS │ ├── FD001 │ │ └── min-max │ │ │ ├── bayesian_conv2_pool2 │ │ │ ├── bayesian_conv2_pool2_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── bayesian_conv2_pool2_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1 │ │ │ ├── bayesian_conv5_dense1_0.125 │ │ │ │ ├── bayesian_conv5_dense1_0.125_0 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_1 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_2 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_3 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_4 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_5 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_6 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_7 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_8 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_conv5_dense1_0.125_9 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.25 │ │ │ │ ├── bayesian_conv5_dense1_0.25_0 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_1 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_2 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_3 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_4 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_5 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_6 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_7 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_8 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_conv5_dense1_0.25_9 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.50 │ │ │ │ ├── bayesian_conv5_dense1_0.50_0 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_1 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_2 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_3 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_4 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_5 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_6 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_7 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_8 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_conv5_dense1_0.50_9 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ └── bayesian_conv5_dense1_1.00 │ │ │ │ ├── bayesian_conv5_dense1_1.00_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_conv5_dense1_1.00_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3 │ │ │ ├── bayesian_dense3_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── bayesian_dense3_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2 │ │ │ ├── frequentist_conv2_pool2_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── frequentist_conv2_pool2_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1 │ │ │ ├── frequentist_conv5_dense1_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── frequentist_conv5_dense1_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── frequentist_dense3 │ │ │ ├── frequentist_dense3_0 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_1 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_2 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_3 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_4 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_5 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_6 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_7 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_8 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ └── frequentist_dense3_9 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ ├── FD002 │ │ └── min-max │ │ │ ├── bayesian_conv2_pool2 │ │ │ ├── bayesian_conv2_pool2_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── bayesian_conv2_pool2_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1 │ │ │ ├── bayesian_conv5_dense1_0.125 │ │ │ │ ├── bayesian_conv5_dense1_0.125_0 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_1 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_2 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_3 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_4 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_5 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_6 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_7 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.125_8 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_conv5_dense1_0.125_9 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.25 │ │ │ │ ├── bayesian_conv5_dense1_0.25_0 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_1 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_2 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_3 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_4 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_5 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_6 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_7 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.25_8 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_conv5_dense1_0.25_9 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.50 │ │ │ │ ├── bayesian_conv5_dense1_0.50_0 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_1 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_2 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_3 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_4 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_5 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_6 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_7 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_0.50_8 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_conv5_dense1_0.50_9 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ └── bayesian_conv5_dense1_1.00 │ │ │ │ ├── bayesian_conv5_dense1_1.00_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_conv5_dense1_1.00_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_conv5_dense1_1.00_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3 │ │ │ ├── bayesian_dense3_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── bayesian_dense3_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2 │ │ │ ├── frequentist_conv2_pool2_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── frequentist_conv2_pool2_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1 │ │ │ ├── frequentist_conv5_dense1_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── frequentist_conv5_dense1_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── frequentist_dense3 │ │ │ ├── frequentist_dense3_0 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_1 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_2 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_3 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_4 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_5 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_6 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_7 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_8 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ └── frequentist_dense3_9 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ ├── FD003 │ │ └── min-max │ │ │ ├── bayesian_conv2_pool2 │ │ │ ├── bayesian_conv2_pool2_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv2_pool2_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── bayesian_conv2_pool2_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1 │ │ │ ├── bayesian_conv5_dense1_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── bayesian_conv5_dense1_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3 │ │ │ ├── bayesian_dense3_0.125 │ │ │ │ ├── bayesian_dense3_0.125_0 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.125_1 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.125_2 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.125_3 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.125_4 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.125_5 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.125_6 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.125_7 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.125_8 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_dense3_0.125_9 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_0.25 │ │ │ │ ├── bayesian_dense3_0.25_0 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.25_1 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.25_2 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.25_3 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.25_4 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.25_5 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.25_6 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.25_7 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.25_8 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_dense3_0.25_9 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_dense3_0.50 │ │ │ │ ├── bayesian_dense3_0.50_0 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.50_1 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.50_2 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.50_3 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.50_4 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.50_5 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.50_6 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.50_7 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_0.50_8 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_dense3_0.50_9 │ │ │ │ │ ├── log_evaluate.txt │ │ │ │ │ └── log_train.txt │ │ │ └── bayesian_dense3_1.00 │ │ │ │ ├── bayesian_dense3_1.00_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_1.00_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_1.00_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_1.00_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_1.00_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_1.00_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_1.00_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_1.00_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ ├── bayesian_dense3_1.00_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ │ └── bayesian_dense3_1.00_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2 │ │ │ ├── frequentist_conv2_pool2_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv2_pool2_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── frequentist_conv2_pool2_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1 │ │ │ ├── frequentist_conv5_dense1_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── frequentist_conv5_dense1_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── frequentist_conv5_dense1_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── frequentist_dense3 │ │ │ ├── frequentist_dense3_0 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_1 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_2 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_3 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_4 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_5 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_6 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_7 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── frequentist_dense3_8 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ └── frequentist_dense3_9 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ └── FD004 │ │ └── min-max │ │ ├── bayesian_conv2_pool2 │ │ ├── bayesian_conv2_pool2_0 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_conv2_pool2_1 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_conv2_pool2_2 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_conv2_pool2_3 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_conv2_pool2_4 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_conv2_pool2_5 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_conv2_pool2_6 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_conv2_pool2_7 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_conv2_pool2_8 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ └── bayesian_conv2_pool2_9 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_conv5_dense1 │ │ ├── bayesian_conv5_dense1_0.125 │ │ │ ├── bayesian_conv5_dense1_0.125_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.125_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.125_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.125_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.125_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.125_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.125_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.125_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.125_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── bayesian_conv5_dense1_0.125_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ ├── bayesian_conv5_dense1_0.25 │ │ │ ├── bayesian_conv5_dense1_0.25_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.25_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.25_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.25_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.25_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.25_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.25_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.25_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.25_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── bayesian_conv5_dense1_0.25_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ ├── bayesian_conv5_dense1_0.50 │ │ │ ├── bayesian_conv5_dense1_0.50_0 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.50_1 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.50_2 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.50_3 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.50_4 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.50_5 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.50_6 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.50_7 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_0.50_8 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ │ └── bayesian_conv5_dense1_0.50_9 │ │ │ │ ├── log_evaluate.txt │ │ │ │ └── log_train.txt │ │ └── bayesian_conv5_dense1_1.00 │ │ │ ├── bayesian_conv5_dense1_1.00_0 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_1.00_1 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_1.00_2 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_1.00_3 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_1.00_4 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_1.00_5 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_1.00_6 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_1.00_7 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ ├── bayesian_conv5_dense1_1.00_8 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ │ └── bayesian_conv5_dense1_1.00_9 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_dense3 │ │ ├── bayesian_dense3_0 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_dense3_1 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_dense3_2 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_dense3_3 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_dense3_4 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_dense3_5 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_dense3_6 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_dense3_7 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── bayesian_dense3_8 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ └── bayesian_dense3_9 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv2_pool2 │ │ ├── frequentist_conv2_pool2_0 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv2_pool2_1 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv2_pool2_2 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv2_pool2_3 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv2_pool2_4 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv2_pool2_5 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv2_pool2_6 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv2_pool2_7 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv2_pool2_8 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ └── frequentist_conv2_pool2_9 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv5_dense1 │ │ ├── frequentist_conv5_dense1_0 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv5_dense1_1 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv5_dense1_2 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv5_dense1_3 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv5_dense1_4 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv5_dense1_5 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv5_dense1_6 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv5_dense1_7 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ ├── frequentist_conv5_dense1_8 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ └── frequentist_conv5_dense1_9 │ │ │ ├── log_evaluate.txt │ │ │ └── log_train.txt │ │ └── frequentist_dense3 │ │ ├── frequentist_dense3_0 │ │ ├── log_evaluate.txt │ │ └── log_train.txt │ │ ├── frequentist_dense3_1 │ │ ├── log_evaluate.txt │ │ └── log_train.txt │ │ ├── frequentist_dense3_2 │ │ ├── log_evaluate.txt │ │ └── log_train.txt │ │ ├── frequentist_dense3_3 │ │ ├── log_evaluate.txt │ │ └── log_train.txt │ │ ├── frequentist_dense3_4 │ │ ├── log_evaluate.txt │ │ └── log_train.txt │ │ ├── frequentist_dense3_5 │ │ ├── log_evaluate.txt │ │ └── log_train.txt │ │ ├── frequentist_dense3_6 │ │ ├── log_evaluate.txt │ │ └── log_train.txt │ │ ├── frequentist_dense3_7 │ │ ├── log_evaluate.txt │ │ └── log_train.txt │ │ ├── frequentist_dense3_8 │ │ ├── log_evaluate.txt │ │ └── log_train.txt │ │ └── frequentist_dense3_9 │ │ ├── log_evaluate.txt │ │ └── log_train.txt └── results.ipynb ├── run_experiments.sh ├── train.py ├── train.sh └── utils ├── dataloader.py ├── distributions.py ├── layers.py └── visualization.py /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints 2 | __pycache__/ 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Luca Della Libera 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | 23 | 24 | This project incorporates components from the projects listed below. The original copyright notices are set forth below. 25 | 26 | 1. kumar-shridhar/PyTorch-BayesianCNN (https://github.com/kumar-shridhar/PyTorch-BayesianCNN/tree/28d63e0d7dce28c674edbeaae24065f2599a3baf) 27 | 2. charlesq34/pointnet2 (https://github.com/charlesq34/pointnet2/tree/42926632a3c33461aebfbee2d829098b30a23aaa) 28 | 29 | %% kumar-shridhar/PyTorch-BayesianCNN NOTICES AND INFORMATION BEGIN HERE 30 | ========================================= 31 | MIT License 32 | 33 | Copyright (c) 2019 Kumar Shridhar 34 | 35 | Permission is hereby granted, free of charge, to any person obtaining a copy 36 | of this software and associated documentation files (the "Software"), to deal 37 | in the Software without restriction, including without limitation the rights 38 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 39 | copies of the Software, and to permit persons to whom the Software is 40 | furnished to do so, subject to the following conditions: 41 | 42 | The above copyright notice and this permission notice shall be included in all 43 | copies or substantial portions of the Software. 44 | 45 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 46 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 47 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 48 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 49 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 50 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 51 | SOFTWARE. 52 | ========================================= 53 | END OF kumar-shridhar/PyTorch-BayesianCNN NOTICES AND INFORMATION 54 | 55 | %% charlesq34/pointnet2 NOTICES AND INFORMATION BEGIN HERE 56 | ========================================= 57 | PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. 58 | 59 | Copyright (c) 2017, Geometric Computation Group of Stanford University 60 | 61 | The MIT License (MIT) 62 | 63 | Copyright (c) 2017 Charles R. Qi 64 | 65 | Permission is hereby granted, free of charge, to any person obtaining a copy 66 | of this software and associated documentation files (the "Software"), to deal 67 | in the Software without restriction, including without limitation the rights 68 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 69 | copies of the Software, and to permit persons to whom the Software is 70 | furnished to do so, subject to the following conditions: 71 | 72 | The above copyright notice and this permission notice shall be included in all 73 | copies or substantial portions of the Software. 74 | 75 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 76 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 77 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 78 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 79 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 80 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 81 | SOFTWARE. 82 | ========================================= 83 | END OF charlesq34/pointnet2 NOTICES AND INFORMATION 84 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # A Comparative Study between Bayesian and Frequentist Neural Networks for Remaining Useful Life Estimation in Condition-Based Maintenance 2 | 3 | Official implementation of https://arxiv.org/abs/1911.06256. Bayesian and frequentist deep learning models for remaining useful life (RUL) estimation are evaluated on simulated run-to-failure data. Implemented in PyTorch, developed and tested on Ubuntu 18.04 LTS. All the experiments were run on a publicly available Google Compute Engine Deep Learning VM instance with 2 vCPUs, 13 GB RAM, 1 NVIDIA Tesla K80 GPU and *PyTorch 1.2 + fast.ai 1.0 (CUDA 10.0)* framework. 4 | 5 | --------------------------------------------------------------------------------------------------------- 6 | 7 | ## Requirements 8 | 9 | Anaconda Python >= 3.6.4 (see https://www.anaconda.com/distribution/) 10 | 11 | --------------------------------------------------------------------------------------------------------- 12 | 13 | ## Installation 14 | 15 | Clone or download the repository, open a terminal in the root directory and run the following commands: 16 | 17 | ```conda env create -f environment.yml``` 18 | 19 | ```conda activate bayesian-deep-rul``` 20 | 21 | Now the virtual environment *bayesian-deep-rul* is active. To deactivate it, run: 22 | 23 | ```conda deactivate``` 24 | 25 | When you do not need it anymore, run the following command to remove it: 26 | 27 | ```conda remove --name bayesian-deep-rul --all``` 28 | 29 | --------------------------------------------------------------------------------------------------------- 30 | 31 | ## Dataset 32 | 33 | The models were tested on the four simulated turbofan engine degradation subsets in the publicly available *Commercial Modular Aero-Propulsion System Simulation* (C-MAPSS) dataset. Check *datasets/CMAPSS/README.md* for instructions on how to download the dataset. 34 | 35 | --------------------------------------------------------------------------------------------------------- 36 | 37 | ## Usage 38 | 39 | Open a terminal in the root directory, activate the virtual environment and run one of the following commands: 40 | 41 | * `sh train.sh` to train the selected model. Parameters can be modified by editing *train.sh* 42 | 43 | * `sh evaluate.sh` to evaluate the selected model. Parameters can be modified by editing *evaluate.sh* 44 | 45 | * `sh run_experiments.sh` to replicate the experiments on the C-MAPSS dataset 46 | 47 | --------------------------------------------------------------------------------------------------------- 48 | 49 | ## TensorBoard 50 | 51 | Open a terminal in the root directory, activate the virtual environment and run `tensorboard --logdir .` to monitor the training process with TensorBoard. If you are training on a remote server, connect through SSH and forward a port from the remote server to your local computer (`gcloud compute ssh --zone= -- -L 6006:localhost:6006` on a Google Compute Engine Deep Learning VM instance). 52 | 53 | --------------------------------------------------------------------------------------------------------- 54 | 55 | ## Results 56 | 57 | Training and evaluation logs of the experimental results are provided for verification. Run *results/results.ipynb* in Jupyter Notebook to check the results by yourself. TensorBoard logging was disabled to speed up training. 58 | 59 | --------------------------------------------------------------------------------------------------------- 60 | 61 | ## Citation 62 | 63 | If you find this work useful in your research, please consider citing: 64 | ``` 65 | @article{libera2019comparative, 66 | title={A Comparative Study between Bayesian and Frequentist Neural Networks for Remaining Useful Life Estimation in Condition-Based Maintenance}, 67 | author={Luca Della Libera}, 68 | year={2019}, 69 | journal={arXiv preprint arXiv:1911.06256}, 70 | eprint={1911.06256}, 71 | archivePrefix={arXiv}, 72 | primaryClass={cs.LG} 73 | } 74 | ``` 75 | --------------------------------------------------------------------------------------------------------- 76 | 77 | ## Contact 78 | 79 | luca310795@gmail.com 80 | 81 | --------------------------------------------------------------------------------------------------------- 82 | -------------------------------------------------------------------------------- /datasets/CMAPSS/README.md: -------------------------------------------------------------------------------- 1 | # Download the dataset 2 | 3 | Open a terminal in this directory and run: 4 | 5 | * `sh download_cmapss.sh` to download the C-MAPSS dataset from https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/; 6 | 7 | * `python3 cmapss_preprocessing` to preprocess the raw data. 8 | 9 | --------------------------------------------------------------------------------------------------------- 10 | -------------------------------------------------------------------------------- /datasets/CMAPSS/download_cmapss.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | rm -rf CMAPSSData.zip 3 | wget -O CMAPSSData.zip https://ti.arc.nasa.gov/c/6/ 4 | -------------------------------------------------------------------------------- /evaluate.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | readonly DATASET="CMAPSS/FD001" 3 | readonly MODEL="frequentist_dense3" 4 | readonly LOG_PATH="log/$DATASET/min-max/$MODEL/${MODEL}_test" 5 | readonly DUMP_PATH="dump/$DATASET/min-max$MODEL/${MODEL}_test" 6 | readonly BATCH_SIZE=512 7 | readonly MAX_RUL=125 8 | readonly NUM_MC=1 9 | 10 | python3 evaluate.py --dataset $DATASET --model $MODEL --model_path $LOG_PATH/checkpoint.pth.tar --dump_dir $DUMP_PATH --batch_size $BATCH_SIZE --num_mc $NUM_MC 11 | -------------------------------------------------------------------------------- /models/bayesian_conv2_pool2.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """BayesianConv2Pool2 definition. Softplus for positive output.""" 3 | 4 | import torch.nn as nn 5 | 6 | from utils.layers import BayesianConv2d, BayesianLinear, Flatten 7 | from models.abstract_model import AbstractModel 8 | 9 | 10 | class BayesianConv2Pool2(AbstractModel): 11 | 12 | def __init__(self, input_size): 13 | """ 14 | Parameters 15 | ---------- 16 | input_size : (int, int, int) 17 | Input size. 18 | """ 19 | super(BayesianConv2Pool2, self).__init__(input_size) 20 | 21 | self.layers = nn.ModuleList([ 22 | BayesianConv2d(input_size[0], 8, kernel_size=(5, 14)), 23 | nn.Sigmoid(), 24 | nn.AvgPool2d(kernel_size=(2, 1)), 25 | BayesianConv2d(8, 14, kernel_size=(2, 1)), 26 | nn.Sigmoid(), 27 | nn.AvgPool2d(kernel_size=(2, 1)), 28 | Flatten(14 * int((((input_size[1] - 4) / 2) - 1) / 2) * (input_size[2] - 13)), 29 | BayesianLinear(14 * int((((input_size[1] - 4) / 2) - 1) / 2) * (input_size[2] - 13), 1), 30 | nn.Softplus() 31 | ]) 32 | -------------------------------------------------------------------------------- /models/bayesian_conv5_dense1.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """BayesianConv5Dense1 definition. Sigmoid and softplus instead of tanh for positive output. No dropout.""" 3 | 4 | import torch.nn as nn 5 | 6 | from utils.layers import BayesianConv2d, BayesianLinear, Flatten 7 | from models.abstract_model import AbstractModel 8 | 9 | 10 | class BayesianConv5Dense1(AbstractModel): 11 | 12 | def __init__(self, input_size): 13 | """ 14 | Parameters 15 | ---------- 16 | input_size : (int, int, int) 17 | Input size. 18 | """ 19 | super(BayesianConv5Dense1, self).__init__(input_size) 20 | 21 | self.layers = nn.ModuleList([ 22 | BayesianConv2d(input_size[0], 10, kernel_size=(10, 1), padding=(5, 0)), 23 | nn.Sigmoid(), 24 | BayesianConv2d(10, 10, kernel_size=(10, 1), padding=(4, 0)), 25 | nn.Sigmoid(), 26 | BayesianConv2d(10, 10, kernel_size=(10, 1), padding=(5, 0)), 27 | nn.Sigmoid(), 28 | BayesianConv2d(10, 10, kernel_size=(10, 1), padding=(4, 0)), 29 | nn.Sigmoid(), 30 | BayesianConv2d(10, 1, kernel_size=(3, 1), padding=(1, 0)), 31 | nn.Softplus(), 32 | Flatten(1 * input_size[1] * input_size[2]), 33 | BayesianLinear(1 * input_size[1] * input_size[2], 100), 34 | nn.Softplus(), 35 | BayesianLinear(100, 1), 36 | nn.Softplus() 37 | ]) 38 | -------------------------------------------------------------------------------- /models/bayesian_dense3.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """BayesianDense3 definition.""" 3 | 4 | import torch.nn as nn 5 | 6 | from utils.layers import BayesianLinear, Flatten 7 | from models.abstract_model import AbstractModel 8 | 9 | 10 | class BayesianDense3(AbstractModel): 11 | 12 | def __init__(self, input_size): 13 | """ 14 | Parameters 15 | ---------- 16 | input_size : (int, int, int) 17 | Input size. 18 | """ 19 | super(BayesianDense3, self).__init__(input_size) 20 | 21 | self.layers = nn.ModuleList([ 22 | Flatten(input_size[0] * input_size[1] * input_size[2]), 23 | BayesianLinear(input_size[0] * input_size[1] * input_size[2], 100), 24 | nn.Sigmoid(), 25 | BayesianLinear(100, 100), 26 | nn.Sigmoid(), 27 | BayesianLinear(100, 100), 28 | nn.Sigmoid(), 29 | BayesianLinear(100, 1), 30 | nn.Softplus() 31 | ]) 32 | -------------------------------------------------------------------------------- /models/frequentist_conv2_pool2.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """FrequentistConv2Pool2 definition.""" 3 | 4 | import torch.nn as nn 5 | 6 | from utils.layers import Flatten 7 | from models.abstract_model import AbstractModel 8 | 9 | 10 | class FrequentistConv2Pool2(AbstractModel): 11 | 12 | def __init__(self, input_size): 13 | """ 14 | Parameters 15 | ---------- 16 | input_size : (int, int, int) 17 | Input size. 18 | """ 19 | super(FrequentistConv2Pool2, self).__init__(input_size) 20 | 21 | self.layers = nn.ModuleList([ 22 | nn.Conv2d(input_size[0], 8, kernel_size=(5, 14), bias=False), 23 | nn.Sigmoid(), 24 | nn.AvgPool2d(kernel_size=(2, 1)), 25 | nn.Conv2d(8, 14, kernel_size=(2, 1), bias=False), 26 | nn.Sigmoid(), 27 | nn.AvgPool2d(kernel_size=(2, 1)), 28 | Flatten(14 * int((((input_size[1] - 4) / 2) - 1) / 2) * (input_size[2] - 13)), 29 | nn.Linear(14 * int((((input_size[1] - 4) / 2) - 1) / 2) * (input_size[2] - 13), 1, bias=False) 30 | ]) 31 | -------------------------------------------------------------------------------- /models/frequentist_conv5_dense1.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """FrequentistConv5Dense1 definition. Last tanh removed due to vanishing gradient.""" 3 | 4 | import torch.nn as nn 5 | 6 | from utils.layers import Flatten 7 | from models.abstract_model import AbstractModel 8 | 9 | 10 | class FrequentistConv5Dense1(AbstractModel): 11 | 12 | def __init__(self, input_size): 13 | """ 14 | Parameters 15 | ---------- 16 | input_size : (int, int, int) 17 | Input size. 18 | """ 19 | super(FrequentistConv5Dense1, self).__init__(input_size) 20 | 21 | self.layers = nn.ModuleList([ 22 | nn.Conv2d(input_size[0], 10, kernel_size=(10, 1), padding=(5, 0), bias=False), 23 | nn.Tanh(), 24 | nn.Conv2d(10, 10, kernel_size=(10, 1), padding=(4, 0), bias=False), 25 | nn.Tanh(), 26 | nn.Conv2d(10, 10, kernel_size=(10, 1), padding=(5, 0), bias=False), 27 | nn.Tanh(), 28 | nn.Conv2d(10, 10, kernel_size=(10, 1), padding=(4, 0), bias=False), 29 | nn.Tanh(), 30 | nn.Conv2d(10, 1, kernel_size=(3, 1), padding=(1, 0), bias=False), 31 | nn.Tanh(), 32 | Flatten(1 * input_size[1] * input_size[2]), 33 | nn.Dropout(0.5), 34 | nn.Linear(1 * input_size[1] * input_size[2], 100, bias=False), 35 | nn.Linear(100, 1, bias=False) 36 | #nn.Tanh() 37 | ]) 38 | 39 | def weights_init(m): 40 | """Xavier initialization. 41 | 42 | Parameters 43 | ---------- 44 | m : Module 45 | Layer. 46 | """ 47 | classname = m.__class__.__name__ 48 | if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d): 49 | nn.init.xavier_normal_(m.weight.data) 50 | # Xavier initialization not defined for scalar values 51 | #nn.init.xavier_normal_(m.bias.data) 52 | 53 | self.apply(weights_init) 54 | -------------------------------------------------------------------------------- /models/frequentist_dense3.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """FrequentistDense3 definition.""" 3 | 4 | import torch.nn as nn 5 | 6 | from utils.layers import Flatten 7 | from models.abstract_model import AbstractModel 8 | 9 | 10 | class FrequentistDense3(AbstractModel): 11 | 12 | def __init__(self, input_size): 13 | """ 14 | Parameters 15 | ---------- 16 | input_size : (int, int, int) 17 | Input size. 18 | """ 19 | super(FrequentistDense3, self).__init__(input_size) 20 | 21 | self.layers = nn.ModuleList([ 22 | Flatten(input_size[0] * input_size[1] * input_size[2]), 23 | nn.Linear(input_size[0] * input_size[1] * input_size[2], 100, bias=False), 24 | nn.Sigmoid(), 25 | nn.Linear(100, 100, bias=False), 26 | nn.Sigmoid(), 27 | nn.Linear(100, 100, bias=False), 28 | nn.Sigmoid(), 29 | nn.Linear(100, 1, bias=False) 30 | ]) 31 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_0/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_0', model='bayesian_dense3', model_path='log/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_0/checkpoint.pth.tar', normalization='min-max', num_mc=150) 2 | pid: 29568 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring BayesianDense3... 6 | Done. 7 | **** start time: 2019-09-27 15:08:41.428328 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | BayesianLinear-2 [-1, 100] 84,000 13 | Sigmoid-3 [-1, 100] 0 14 | BayesianLinear-4 [-1, 100] 20,000 15 | Sigmoid-5 [-1, 100] 0 16 | BayesianLinear-6 [-1, 100] 20,000 17 | Sigmoid-7 [-1, 100] 0 18 | BayesianLinear-8 [-1, 1] 200 19 | Softplus-9 [-1, 1] 0 20 | ================================================================ 21 | Total params: 124,200 22 | Trainable params: 124,200 23 | Non-trainable params: 0 24 | ________________________________________________________________ 25 | 2019-09-27 15:08:41.437811 26 | ground truth | pred +/- std: 27 | 77.00 | 122.36 +/- 1.27 28 | 57.00 | 72.91 +/- 2.81 29 | 124.00 | 95.58 +/- 6.46 30 | 90.00 | 120.52 +/- 1.49 31 | 93.00 | 115.46 +/- 2.55 32 | 37.00 | 42.85 +/- 1.37 33 | 28.00 | 26.86 +/- 1.18 34 | 91.00 | 110.86 +/- 2.97 35 | 113.00 | 93.92 +/- 5.00 36 | 58.00 | 81.15 +/- 3.55 37 | 84.00 | 82.60 +/- 3.02 38 | 20.00 | 17.37 +/- 1.55 39 | 142.00 | 123.13 +/- 1.20 40 | 126.00 | 110.20 +/- 3.71 41 | 11.00 | 10.41 +/- 1.32 42 | 114.00 | 78.85 +/- 4.06 43 | 69.00 | 47.71 +/- 1.23 44 | 26.00 | 34.07 +/- 1.62 45 | 137.00 | 113.64 +/- 2.95 46 | 28.00 | 33.23 +/- 1.60 47 | 21.00 | 19.10 +/- 1.28 48 | 9.00 | 9.19 +/- 1.25 49 | 50.00 | 58.12 +/- 2.02 50 | 135.00 | 122.73 +/- 1.15 51 | 97.00 | 116.23 +/- 2.48 52 | 96.00 | 85.99 +/- 4.31 53 | 89.00 | 95.86 +/- 5.32 54 | 79.00 | 83.15 +/- 3.76 55 | 20.00 | 20.59 +/- 1.38 56 | 18.00 | 23.89 +/- 1.55 57 | 111.00 | 118.51 +/- 1.99 58 | 90.00 | 102.70 +/- 4.96 59 | 114.00 | 116.45 +/- 2.59 60 | 111.00 | 122.97 +/- 1.30 61 | 103.00 | 102.78 +/- 4.23 62 | 119.00 | 120.67 +/- 1.46 63 | 82.00 | 71.66 +/- 3.13 64 | 59.00 | 54.92 +/- 1.67 65 | 82.00 | 90.45 +/- 4.51 66 | 115.00 | 115.25 +/- 2.71 67 | 106.00 | 118.29 +/- 2.29 68 | 50.00 | 50.69 +/- 1.86 69 | 19.00 | 21.85 +/- 1.55 70 | 94.00 | 92.33 +/- 4.12 71 | 63.00 | 120.76 +/- 1.57 72 | 97.00 | 96.54 +/- 5.54 73 | 145.00 | 119.68 +/- 1.82 74 | 83.00 | 88.02 +/- 4.73 75 | 128.00 | 106.64 +/- 4.55 76 | 10.00 | 11.14 +/- 1.17 77 | 95.00 | 74.50 +/- 2.86 78 | 21.00 | 29.30 +/- 1.50 79 | 72.00 | 84.59 +/- 3.67 80 | 115.00 | 122.23 +/- 1.32 81 | 91.00 | 101.55 +/- 4.26 82 | 54.00 | 39.97 +/- 1.81 83 | 66.00 | 99.17 +/- 4.66 84 | 8.00 | 4.23 +/- 1.17 85 | 92.00 | 115.84 +/- 2.42 86 | 47.00 | 40.37 +/- 1.53 87 | 137.00 | 122.80 +/- 1.34 88 | 7.00 | 3.91 +/- 1.10 89 | 8.00 | 7.58 +/- 1.16 90 | 118.00 | 122.19 +/- 1.31 91 | 85.00 | 59.74 +/- 2.28 92 | 107.00 | 122.93 +/- 1.12 93 | 109.00 | 117.40 +/- 2.19 94 | 121.00 | 122.36 +/- 1.24 95 | 14.00 | 18.36 +/- 1.40 96 | 113.00 | 122.97 +/- 1.22 97 | 38.00 | 27.76 +/- 1.67 98 | 100.00 | 104.00 +/- 4.15 99 | 55.00 | 75.83 +/- 2.72 100 | 136.00 | 121.24 +/- 1.43 101 | 117.00 | 120.71 +/- 1.24 102 | 28.00 | 27.76 +/- 1.25 103 | 118.00 | 113.27 +/- 4.54 104 | 97.00 | 68.76 +/- 2.90 105 | 137.00 | 121.64 +/- 1.37 106 | 50.00 | 50.77 +/- 2.18 107 | 16.00 | 12.54 +/- 1.20 108 | 21.00 | 15.11 +/- 1.22 109 | 112.00 | 121.04 +/- 1.56 110 | 34.00 | 30.02 +/- 1.32 111 | 95.00 | 112.68 +/- 2.89 112 | 131.00 | 107.24 +/- 4.54 113 | 10.00 | 9.63 +/- 1.25 114 | 107.00 | 122.53 +/- 1.31 115 | 8.00 | 6.60 +/- 1.07 116 | 29.00 | 30.30 +/- 1.54 117 | 116.00 | 121.80 +/- 1.25 118 | 15.00 | 21.86 +/- 1.33 119 | 59.00 | 66.89 +/- 3.12 120 | 98.00 | 122.89 +/- 1.32 121 | 128.00 | 122.14 +/- 1.26 122 | 20.00 | 20.87 +/- 1.37 123 | 114.00 | 107.77 +/- 4.17 124 | 28.00 | 20.97 +/- 1.24 125 | 48.00 | 53.93 +/- 2.20 126 | 87.00 | 96.58 +/- 4.62 127 | eval mean loss: 115.21 128 | eval rmse: 15.16 129 | eval mae: 10.96 130 | eval score: 664.03 131 | epistemic: 7.31 132 | epoch: 249 133 | ground truth std: 41.56 134 | pred std: 41.53 135 | eval time: 0:00:02.294308 136 | **** end time: 2019-09-27 15:08:43.745032 **** 137 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_1/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_1', model='bayesian_dense3', model_path='log/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_1/checkpoint.pth.tar', normalization='min-max', num_mc=150) 2 | pid: 29637 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring BayesianDense3... 6 | Done. 7 | **** start time: 2019-09-27 15:11:41.113562 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | BayesianLinear-2 [-1, 100] 84,000 13 | Sigmoid-3 [-1, 100] 0 14 | BayesianLinear-4 [-1, 100] 20,000 15 | Sigmoid-5 [-1, 100] 0 16 | BayesianLinear-6 [-1, 100] 20,000 17 | Sigmoid-7 [-1, 100] 0 18 | BayesianLinear-8 [-1, 1] 200 19 | Softplus-9 [-1, 1] 0 20 | ================================================================ 21 | Total params: 124,200 22 | Trainable params: 124,200 23 | Non-trainable params: 0 24 | ________________________________________________________________ 25 | 2019-09-27 15:11:41.122601 26 | ground truth | pred +/- std: 27 | 77.00 | 122.56 +/- 2.06 28 | 57.00 | 71.48 +/- 2.91 29 | 124.00 | 101.37 +/- 5.89 30 | 90.00 | 114.52 +/- 3.19 31 | 93.00 | 106.36 +/- 4.52 32 | 37.00 | 41.28 +/- 1.76 33 | 28.00 | 24.98 +/- 1.34 34 | 91.00 | 108.60 +/- 3.75 35 | 113.00 | 100.39 +/- 4.97 36 | 58.00 | 84.26 +/- 4.52 37 | 84.00 | 82.06 +/- 3.17 38 | 20.00 | 18.09 +/- 1.58 39 | 142.00 | 122.97 +/- 2.06 40 | 126.00 | 118.68 +/- 2.34 41 | 11.00 | 12.91 +/- 1.23 42 | 114.00 | 78.77 +/- 4.13 43 | 69.00 | 43.80 +/- 1.56 44 | 26.00 | 30.24 +/- 1.64 45 | 137.00 | 116.46 +/- 2.31 46 | 28.00 | 33.61 +/- 1.82 47 | 21.00 | 19.57 +/- 1.24 48 | 9.00 | 9.59 +/- 1.05 49 | 50.00 | 58.02 +/- 2.29 50 | 135.00 | 122.62 +/- 2.19 51 | 97.00 | 118.23 +/- 1.98 52 | 96.00 | 97.05 +/- 4.58 53 | 89.00 | 92.32 +/- 5.20 54 | 79.00 | 86.77 +/- 3.99 55 | 20.00 | 20.69 +/- 1.53 56 | 18.00 | 24.02 +/- 1.54 57 | 111.00 | 112.45 +/- 3.83 58 | 90.00 | 109.50 +/- 4.80 59 | 114.00 | 102.24 +/- 4.49 60 | 111.00 | 122.74 +/- 2.15 61 | 103.00 | 95.42 +/- 4.56 62 | 119.00 | 116.32 +/- 3.13 63 | 82.00 | 75.71 +/- 3.24 64 | 59.00 | 53.54 +/- 2.15 65 | 82.00 | 85.07 +/- 3.62 66 | 115.00 | 120.76 +/- 2.36 67 | 106.00 | 115.59 +/- 3.17 68 | 50.00 | 51.02 +/- 2.11 69 | 19.00 | 22.13 +/- 1.44 70 | 94.00 | 98.88 +/- 5.13 71 | 63.00 | 118.29 +/- 2.28 72 | 97.00 | 105.47 +/- 4.61 73 | 145.00 | 122.15 +/- 2.06 74 | 83.00 | 103.46 +/- 4.58 75 | 128.00 | 105.63 +/- 4.94 76 | 10.00 | 10.57 +/- 1.10 77 | 95.00 | 70.77 +/- 3.24 78 | 21.00 | 30.13 +/- 1.55 79 | 72.00 | 87.25 +/- 3.69 80 | 115.00 | 121.75 +/- 1.76 81 | 91.00 | 98.48 +/- 3.07 82 | 54.00 | 41.42 +/- 1.85 83 | 66.00 | 96.87 +/- 4.27 84 | 8.00 | 4.64 +/- 1.12 85 | 92.00 | 102.02 +/- 5.33 86 | 47.00 | 37.96 +/- 1.75 87 | 137.00 | 122.83 +/- 2.10 88 | 7.00 | 3.23 +/- 1.06 89 | 8.00 | 7.54 +/- 1.19 90 | 118.00 | 122.77 +/- 2.08 91 | 85.00 | 50.64 +/- 2.48 92 | 107.00 | 122.58 +/- 2.08 93 | 109.00 | 120.49 +/- 2.23 94 | 121.00 | 121.76 +/- 2.00 95 | 14.00 | 16.18 +/- 1.34 96 | 113.00 | 122.99 +/- 2.27 97 | 38.00 | 28.24 +/- 1.74 98 | 100.00 | 117.06 +/- 3.19 99 | 55.00 | 76.88 +/- 2.98 100 | 136.00 | 121.42 +/- 2.14 101 | 117.00 | 117.86 +/- 2.31 102 | 28.00 | 28.27 +/- 1.56 103 | 118.00 | 116.92 +/- 2.99 104 | 97.00 | 62.29 +/- 2.02 105 | 137.00 | 122.08 +/- 2.06 106 | 50.00 | 50.94 +/- 2.19 107 | 16.00 | 13.81 +/- 1.05 108 | 21.00 | 16.14 +/- 1.11 109 | 112.00 | 121.82 +/- 1.82 110 | 34.00 | 28.33 +/- 1.45 111 | 95.00 | 101.16 +/- 4.78 112 | 131.00 | 108.27 +/- 4.34 113 | 10.00 | 9.78 +/- 1.10 114 | 107.00 | 121.22 +/- 2.07 115 | 8.00 | 5.33 +/- 1.11 116 | 29.00 | 29.83 +/- 1.88 117 | 116.00 | 121.51 +/- 2.15 118 | 15.00 | 22.95 +/- 1.52 119 | 59.00 | 66.14 +/- 3.33 120 | 98.00 | 122.79 +/- 2.10 121 | 128.00 | 121.20 +/- 2.21 122 | 20.00 | 22.04 +/- 1.46 123 | 114.00 | 92.90 +/- 3.12 124 | 28.00 | 19.14 +/- 1.22 125 | 48.00 | 59.75 +/- 2.14 126 | 87.00 | 85.14 +/- 4.28 127 | eval mean loss: 115.08 128 | eval rmse: 15.15 129 | eval mae: 11.03 130 | eval score: 590.46 131 | epistemic: 8.40 132 | epoch: 249 133 | ground truth std: 41.56 134 | pred std: 41.46 135 | eval time: 0:00:02.316877 136 | **** end time: 2019-09-27 15:11:43.452033 **** 137 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_2/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_2', model='bayesian_dense3', model_path='log/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_2/checkpoint.pth.tar', normalization='min-max', num_mc=150) 2 | pid: 29703 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring BayesianDense3... 6 | Done. 7 | **** start time: 2019-09-27 15:14:36.660336 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | BayesianLinear-2 [-1, 100] 84,000 13 | Sigmoid-3 [-1, 100] 0 14 | BayesianLinear-4 [-1, 100] 20,000 15 | Sigmoid-5 [-1, 100] 0 16 | BayesianLinear-6 [-1, 100] 20,000 17 | Sigmoid-7 [-1, 100] 0 18 | BayesianLinear-8 [-1, 1] 200 19 | Softplus-9 [-1, 1] 0 20 | ================================================================ 21 | Total params: 124,200 22 | Trainable params: 124,200 23 | Non-trainable params: 0 24 | ________________________________________________________________ 25 | 2019-09-27 15:14:36.670446 26 | ground truth | pred +/- std: 27 | 77.00 | 122.51 +/- 1.66 28 | 57.00 | 70.13 +/- 2.17 29 | 124.00 | 92.75 +/- 5.17 30 | 90.00 | 113.29 +/- 3.03 31 | 93.00 | 103.93 +/- 4.44 32 | 37.00 | 33.83 +/- 1.29 33 | 28.00 | 28.57 +/- 1.37 34 | 91.00 | 121.36 +/- 1.87 35 | 113.00 | 98.15 +/- 4.47 36 | 58.00 | 77.37 +/- 3.36 37 | 84.00 | 78.10 +/- 2.64 38 | 20.00 | 18.92 +/- 1.31 39 | 142.00 | 122.80 +/- 1.81 40 | 126.00 | 119.43 +/- 2.11 41 | 11.00 | 12.51 +/- 1.13 42 | 114.00 | 84.73 +/- 3.80 43 | 69.00 | 43.54 +/- 1.53 44 | 26.00 | 28.53 +/- 1.28 45 | 137.00 | 117.04 +/- 2.35 46 | 28.00 | 38.14 +/- 1.80 47 | 21.00 | 20.43 +/- 1.04 48 | 9.00 | 8.92 +/- 1.04 49 | 50.00 | 57.44 +/- 1.88 50 | 135.00 | 122.32 +/- 1.73 51 | 97.00 | 116.53 +/- 2.99 52 | 96.00 | 79.28 +/- 3.90 53 | 89.00 | 93.47 +/- 4.84 54 | 79.00 | 90.00 +/- 4.43 55 | 20.00 | 21.60 +/- 1.31 56 | 18.00 | 23.05 +/- 1.21 57 | 111.00 | 115.95 +/- 3.00 58 | 90.00 | 97.05 +/- 6.22 59 | 114.00 | 104.41 +/- 4.86 60 | 111.00 | 122.53 +/- 1.86 61 | 103.00 | 107.07 +/- 3.49 62 | 119.00 | 120.15 +/- 2.17 63 | 82.00 | 72.11 +/- 2.23 64 | 59.00 | 53.25 +/- 1.47 65 | 82.00 | 74.43 +/- 3.93 66 | 115.00 | 110.46 +/- 3.65 67 | 106.00 | 120.83 +/- 2.06 68 | 50.00 | 49.84 +/- 1.55 69 | 19.00 | 22.61 +/- 1.29 70 | 94.00 | 101.52 +/- 4.77 71 | 63.00 | 120.46 +/- 1.86 72 | 97.00 | 93.97 +/- 4.56 73 | 145.00 | 120.03 +/- 2.00 74 | 83.00 | 113.11 +/- 2.82 75 | 128.00 | 78.78 +/- 2.93 76 | 10.00 | 12.53 +/- 1.10 77 | 95.00 | 78.41 +/- 2.82 78 | 21.00 | 27.39 +/- 1.28 79 | 72.00 | 83.80 +/- 3.08 80 | 115.00 | 121.40 +/- 1.80 81 | 91.00 | 111.70 +/- 3.28 82 | 54.00 | 40.98 +/- 1.68 83 | 66.00 | 101.90 +/- 4.45 84 | 8.00 | 5.29 +/- 1.08 85 | 92.00 | 101.80 +/- 4.92 86 | 47.00 | 43.68 +/- 1.45 87 | 137.00 | 122.18 +/- 1.72 88 | 7.00 | 5.01 +/- 0.99 89 | 8.00 | 10.88 +/- 1.21 90 | 118.00 | 122.39 +/- 1.59 91 | 85.00 | 51.18 +/- 2.08 92 | 107.00 | 122.43 +/- 1.80 93 | 109.00 | 121.33 +/- 1.66 94 | 121.00 | 120.98 +/- 1.69 95 | 14.00 | 18.95 +/- 1.40 96 | 113.00 | 122.30 +/- 1.53 97 | 38.00 | 27.08 +/- 1.52 98 | 100.00 | 108.04 +/- 3.89 99 | 55.00 | 85.19 +/- 2.93 100 | 136.00 | 114.62 +/- 3.65 101 | 117.00 | 115.55 +/- 3.31 102 | 28.00 | 29.29 +/- 1.24 103 | 118.00 | 109.28 +/- 4.56 104 | 97.00 | 65.41 +/- 2.32 105 | 137.00 | 121.56 +/- 1.81 106 | 50.00 | 53.34 +/- 1.95 107 | 16.00 | 14.54 +/- 1.08 108 | 21.00 | 14.43 +/- 1.09 109 | 112.00 | 121.44 +/- 1.69 110 | 34.00 | 30.66 +/- 1.17 111 | 95.00 | 101.86 +/- 4.00 112 | 131.00 | 105.61 +/- 4.08 113 | 10.00 | 9.85 +/- 1.08 114 | 107.00 | 119.03 +/- 1.94 115 | 8.00 | 7.34 +/- 1.11 116 | 29.00 | 28.88 +/- 1.38 117 | 116.00 | 122.17 +/- 1.89 118 | 15.00 | 22.42 +/- 1.53 119 | 59.00 | 70.31 +/- 3.40 120 | 98.00 | 122.31 +/- 1.83 121 | 128.00 | 122.10 +/- 1.68 122 | 20.00 | 22.12 +/- 1.39 123 | 114.00 | 96.32 +/- 3.86 124 | 28.00 | 23.45 +/- 1.14 125 | 48.00 | 59.72 +/- 1.85 126 | 87.00 | 80.04 +/- 3.58 127 | eval mean loss: 133.49 128 | eval rmse: 16.32 129 | eval mae: 11.76 130 | eval score: 730.65 131 | epistemic: 7.29 132 | epoch: 249 133 | ground truth std: 41.56 134 | pred std: 40.91 135 | eval time: 0:00:02.251201 136 | **** end time: 2019-09-27 15:14:38.934384 **** 137 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_3/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_3', model='bayesian_dense3', model_path='log/CMAPSS/FD001/min-max/bayesian_dense3/bayesian_dense3_3/checkpoint.pth.tar', normalization='min-max', num_mc=150) 2 | pid: 29791 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring BayesianDense3... 6 | Done. 7 | **** start time: 2019-09-27 15:17:34.602582 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | BayesianLinear-2 [-1, 100] 84,000 13 | Sigmoid-3 [-1, 100] 0 14 | BayesianLinear-4 [-1, 100] 20,000 15 | Sigmoid-5 [-1, 100] 0 16 | BayesianLinear-6 [-1, 100] 20,000 17 | Sigmoid-7 [-1, 100] 0 18 | BayesianLinear-8 [-1, 1] 200 19 | Softplus-9 [-1, 1] 0 20 | ================================================================ 21 | Total params: 124,200 22 | Trainable params: 124,200 23 | Non-trainable params: 0 24 | ________________________________________________________________ 25 | 2019-09-27 15:17:34.611764 26 | ground truth | pred +/- std: 27 | 77.00 | 122.53 +/- 2.31 28 | 57.00 | 66.91 +/- 2.59 29 | 124.00 | 95.36 +/- 5.78 30 | 90.00 | 113.98 +/- 3.57 31 | 93.00 | 110.09 +/- 3.41 32 | 37.00 | 39.06 +/- 1.84 33 | 28.00 | 23.82 +/- 1.50 34 | 91.00 | 108.53 +/- 3.85 35 | 113.00 | 96.78 +/- 5.01 36 | 58.00 | 86.10 +/- 3.98 37 | 84.00 | 82.70 +/- 3.64 38 | 20.00 | 18.41 +/- 1.33 39 | 142.00 | 122.98 +/- 2.23 40 | 126.00 | 113.24 +/- 2.92 41 | 11.00 | 12.70 +/- 1.27 42 | 114.00 | 80.05 +/- 3.86 43 | 69.00 | 50.70 +/- 1.92 44 | 26.00 | 33.47 +/- 1.54 45 | 137.00 | 117.35 +/- 2.41 46 | 28.00 | 38.54 +/- 1.86 47 | 21.00 | 19.78 +/- 1.17 48 | 9.00 | 8.21 +/- 1.12 49 | 50.00 | 62.58 +/- 2.66 50 | 135.00 | 123.26 +/- 2.37 51 | 97.00 | 115.46 +/- 2.95 52 | 96.00 | 75.77 +/- 3.89 53 | 89.00 | 100.66 +/- 5.96 54 | 79.00 | 84.45 +/- 4.02 55 | 20.00 | 20.37 +/- 1.38 56 | 18.00 | 23.48 +/- 1.32 57 | 111.00 | 119.95 +/- 2.64 58 | 90.00 | 104.09 +/- 5.12 59 | 114.00 | 109.08 +/- 4.29 60 | 111.00 | 122.79 +/- 2.28 61 | 103.00 | 103.56 +/- 3.32 62 | 119.00 | 120.60 +/- 2.47 63 | 82.00 | 63.61 +/- 2.46 64 | 59.00 | 57.87 +/- 2.12 65 | 82.00 | 76.47 +/- 4.37 66 | 115.00 | 104.19 +/- 4.70 67 | 106.00 | 111.66 +/- 4.23 68 | 50.00 | 53.22 +/- 2.10 69 | 19.00 | 22.60 +/- 1.28 70 | 94.00 | 80.41 +/- 4.14 71 | 63.00 | 116.48 +/- 2.77 72 | 97.00 | 89.75 +/- 5.33 73 | 145.00 | 120.28 +/- 2.27 74 | 83.00 | 99.71 +/- 5.83 75 | 128.00 | 105.16 +/- 4.55 76 | 10.00 | 11.45 +/- 0.97 77 | 95.00 | 68.13 +/- 3.69 78 | 21.00 | 30.57 +/- 1.46 79 | 72.00 | 96.38 +/- 4.27 80 | 115.00 | 120.53 +/- 2.43 81 | 91.00 | 106.30 +/- 3.60 82 | 54.00 | 43.48 +/- 1.62 83 | 66.00 | 105.66 +/- 4.37 84 | 8.00 | 6.30 +/- 1.11 85 | 92.00 | 114.75 +/- 3.50 86 | 47.00 | 40.12 +/- 1.58 87 | 137.00 | 123.09 +/- 2.22 88 | 7.00 | 5.01 +/- 0.98 89 | 8.00 | 7.65 +/- 0.97 90 | 118.00 | 122.25 +/- 2.22 91 | 85.00 | 54.80 +/- 2.55 92 | 107.00 | 123.42 +/- 2.11 93 | 109.00 | 115.52 +/- 3.13 94 | 121.00 | 122.28 +/- 2.01 95 | 14.00 | 17.72 +/- 1.22 96 | 113.00 | 122.50 +/- 2.17 97 | 38.00 | 26.89 +/- 1.51 98 | 100.00 | 117.78 +/- 2.52 99 | 55.00 | 69.68 +/- 2.53 100 | 136.00 | 121.11 +/- 2.37 101 | 117.00 | 119.86 +/- 2.11 102 | 28.00 | 28.96 +/- 1.60 103 | 118.00 | 117.51 +/- 3.15 104 | 97.00 | 69.83 +/- 3.12 105 | 137.00 | 120.66 +/- 2.18 106 | 50.00 | 59.21 +/- 2.79 107 | 16.00 | 12.98 +/- 1.11 108 | 21.00 | 13.63 +/- 0.95 109 | 112.00 | 121.84 +/- 2.05 110 | 34.00 | 27.66 +/- 1.43 111 | 95.00 | 97.90 +/- 4.36 112 | 131.00 | 102.35 +/- 5.60 113 | 10.00 | 9.35 +/- 1.00 114 | 107.00 | 116.58 +/- 2.83 115 | 8.00 | 5.97 +/- 1.08 116 | 29.00 | 27.75 +/- 1.70 117 | 116.00 | 120.76 +/- 2.36 118 | 15.00 | 19.30 +/- 1.57 119 | 59.00 | 69.50 +/- 3.80 120 | 98.00 | 122.74 +/- 2.32 121 | 128.00 | 121.99 +/- 2.42 122 | 20.00 | 22.28 +/- 1.28 123 | 114.00 | 98.07 +/- 4.11 124 | 28.00 | 21.85 +/- 1.31 125 | 48.00 | 57.75 +/- 2.21 126 | 87.00 | 81.33 +/- 4.20 127 | eval mean loss: 122.02 128 | eval rmse: 15.60 129 | eval mae: 11.66 130 | eval score: 588.90 131 | epistemic: 8.91 132 | epoch: 249 133 | ground truth std: 41.56 134 | pred std: 41.02 135 | eval time: 0:00:02.318772 136 | **** end time: 2019-09-27 15:17:36.943550 **** 137 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_0/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_0', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_0/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 31482 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-09-27 16:40:55.397229 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:40:55.402368 25 | ground truth | pred +/- std: 26 | 77.00 | 104.65 +/- 0.00 27 | 57.00 | 98.89 +/- 0.00 28 | 124.00 | 98.16 +/- 0.00 29 | 90.00 | 84.99 +/- 0.00 30 | 93.00 | 101.75 +/- 0.00 31 | 37.00 | 65.86 +/- 0.00 32 | 28.00 | 52.88 +/- 0.00 33 | 91.00 | 89.42 +/- 0.00 34 | 113.00 | 104.44 +/- 0.00 35 | 58.00 | 68.39 +/- 0.00 36 | 84.00 | 104.69 +/- 0.00 37 | 20.00 | 52.50 +/- 0.00 38 | 142.00 | 104.72 +/- 0.00 39 | 126.00 | 96.37 +/- 0.00 40 | 11.00 | 52.37 +/- 0.00 41 | 114.00 | 62.00 +/- 0.00 42 | 69.00 | 64.87 +/- 0.00 43 | 26.00 | 52.87 +/- 0.00 44 | 137.00 | 104.71 +/- 0.00 45 | 28.00 | 52.62 +/- 0.00 46 | 21.00 | 53.68 +/- 0.00 47 | 9.00 | 53.95 +/- 0.00 48 | 50.00 | 89.09 +/- 0.00 49 | 135.00 | 103.74 +/- 0.00 50 | 97.00 | 104.40 +/- 0.00 51 | 96.00 | 98.36 +/- 0.00 52 | 89.00 | 95.35 +/- 0.00 53 | 79.00 | 104.20 +/- 0.00 54 | 20.00 | 52.62 +/- 0.00 55 | 18.00 | 92.59 +/- 0.00 56 | 111.00 | 102.51 +/- 0.00 57 | 90.00 | 95.17 +/- 0.00 58 | 114.00 | 103.42 +/- 0.00 59 | 111.00 | 104.65 +/- 0.00 60 | 103.00 | 92.05 +/- 0.00 61 | 119.00 | 104.70 +/- 0.00 62 | 82.00 | 104.06 +/- 0.00 63 | 59.00 | 81.74 +/- 0.00 64 | 82.00 | 83.32 +/- 0.00 65 | 115.00 | 86.34 +/- 0.00 66 | 106.00 | 84.71 +/- 0.00 67 | 50.00 | 58.82 +/- 0.00 68 | 19.00 | 52.46 +/- 0.00 69 | 94.00 | 94.79 +/- 0.00 70 | 63.00 | 104.61 +/- 0.00 71 | 97.00 | 101.89 +/- 0.00 72 | 145.00 | 104.70 +/- 0.00 73 | 83.00 | 104.69 +/- 0.00 74 | 128.00 | 104.68 +/- 0.00 75 | 10.00 | 63.18 +/- 0.00 76 | 95.00 | 98.80 +/- 0.00 77 | 21.00 | 88.95 +/- 0.00 78 | 72.00 | 92.88 +/- 0.00 79 | 115.00 | 102.31 +/- 0.00 80 | 91.00 | 103.89 +/- 0.00 81 | 54.00 | 57.07 +/- 0.00 82 | 66.00 | 104.51 +/- 0.00 83 | 8.00 | 52.37 +/- 0.00 84 | 92.00 | 101.62 +/- 0.00 85 | 47.00 | 52.94 +/- 0.00 86 | 137.00 | 104.50 +/- 0.00 87 | 7.00 | 52.71 +/- 0.00 88 | 8.00 | 52.37 +/- 0.00 89 | 118.00 | 104.41 +/- 0.00 90 | 85.00 | 52.65 +/- 0.00 91 | 107.00 | 104.72 +/- 0.00 92 | 109.00 | 101.54 +/- 0.00 93 | 121.00 | 104.69 +/- 0.00 94 | 14.00 | 52.37 +/- 0.00 95 | 113.00 | 104.72 +/- 0.00 96 | 38.00 | 53.61 +/- 0.00 97 | 100.00 | 97.98 +/- 0.00 98 | 55.00 | 61.94 +/- 0.00 99 | 136.00 | 102.05 +/- 0.00 100 | 117.00 | 104.72 +/- 0.00 101 | 28.00 | 76.96 +/- 0.00 102 | 118.00 | 104.10 +/- 0.00 103 | 97.00 | 94.64 +/- 0.00 104 | 137.00 | 104.24 +/- 0.00 105 | 50.00 | 53.09 +/- 0.00 106 | 16.00 | 52.53 +/- 0.00 107 | 21.00 | 53.29 +/- 0.00 108 | 112.00 | 104.72 +/- 0.00 109 | 34.00 | 75.77 +/- 0.00 110 | 95.00 | 100.37 +/- 0.00 111 | 131.00 | 104.71 +/- 0.00 112 | 10.00 | 52.36 +/- 0.00 113 | 107.00 | 104.64 +/- 0.00 114 | 8.00 | 52.37 +/- 0.00 115 | 29.00 | 56.41 +/- 0.00 116 | 116.00 | 104.72 +/- 0.00 117 | 15.00 | 52.84 +/- 0.00 118 | 59.00 | 80.59 +/- 0.00 119 | 98.00 | 103.47 +/- 0.00 120 | 128.00 | 104.72 +/- 0.00 121 | 20.00 | 53.58 +/- 0.00 122 | 114.00 | 104.72 +/- 0.00 123 | 28.00 | 60.89 +/- 0.00 124 | 48.00 | 66.97 +/- 0.00 125 | 87.00 | 104.67 +/- 0.00 126 | eval mean loss: 385.84 127 | eval rmse: 27.78 128 | eval mae: 22.72 129 | eval score: 4517.31 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 21.57 134 | eval time: 0:00:01.536608 135 | **** end time: 2019-09-27 16:40:56.939280 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_1/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_1', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_1/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 31526 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-09-27 16:42:08.577662 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:42:08.582729 25 | ground truth | pred +/- std: 26 | 77.00 | 104.65 +/- 0.00 27 | 57.00 | 98.84 +/- 0.00 28 | 124.00 | 98.21 +/- 0.00 29 | 90.00 | 84.90 +/- 0.00 30 | 93.00 | 101.64 +/- 0.00 31 | 37.00 | 65.97 +/- 0.00 32 | 28.00 | 52.86 +/- 0.00 33 | 91.00 | 89.52 +/- 0.00 34 | 113.00 | 104.44 +/- 0.00 35 | 58.00 | 68.35 +/- 0.00 36 | 84.00 | 104.69 +/- 0.00 37 | 20.00 | 52.50 +/- 0.00 38 | 142.00 | 104.71 +/- 0.00 39 | 126.00 | 96.35 +/- 0.00 40 | 11.00 | 52.36 +/- 0.00 41 | 114.00 | 62.07 +/- 0.00 42 | 69.00 | 64.90 +/- 0.00 43 | 26.00 | 52.85 +/- 0.00 44 | 137.00 | 104.71 +/- 0.00 45 | 28.00 | 52.61 +/- 0.00 46 | 21.00 | 53.64 +/- 0.00 47 | 9.00 | 53.94 +/- 0.00 48 | 50.00 | 88.95 +/- 0.00 49 | 135.00 | 103.75 +/- 0.00 50 | 97.00 | 104.40 +/- 0.00 51 | 96.00 | 98.50 +/- 0.00 52 | 89.00 | 95.12 +/- 0.00 53 | 79.00 | 104.19 +/- 0.00 54 | 20.00 | 52.61 +/- 0.00 55 | 18.00 | 92.69 +/- 0.00 56 | 111.00 | 102.52 +/- 0.00 57 | 90.00 | 95.24 +/- 0.00 58 | 114.00 | 103.36 +/- 0.00 59 | 111.00 | 104.64 +/- 0.00 60 | 103.00 | 92.06 +/- 0.00 61 | 119.00 | 104.69 +/- 0.00 62 | 82.00 | 104.06 +/- 0.00 63 | 59.00 | 81.92 +/- 0.00 64 | 82.00 | 83.35 +/- 0.00 65 | 115.00 | 86.21 +/- 0.00 66 | 106.00 | 84.54 +/- 0.00 67 | 50.00 | 58.88 +/- 0.00 68 | 19.00 | 52.46 +/- 0.00 69 | 94.00 | 94.87 +/- 0.00 70 | 63.00 | 104.60 +/- 0.00 71 | 97.00 | 101.86 +/- 0.00 72 | 145.00 | 104.70 +/- 0.00 73 | 83.00 | 104.68 +/- 0.00 74 | 128.00 | 104.67 +/- 0.00 75 | 10.00 | 63.46 +/- 0.00 76 | 95.00 | 98.81 +/- 0.00 77 | 21.00 | 88.98 +/- 0.00 78 | 72.00 | 92.94 +/- 0.00 79 | 115.00 | 102.29 +/- 0.00 80 | 91.00 | 103.87 +/- 0.00 81 | 54.00 | 57.06 +/- 0.00 82 | 66.00 | 104.51 +/- 0.00 83 | 8.00 | 52.37 +/- 0.00 84 | 92.00 | 101.69 +/- 0.00 85 | 47.00 | 52.91 +/- 0.00 86 | 137.00 | 104.50 +/- 0.00 87 | 7.00 | 52.71 +/- 0.00 88 | 8.00 | 52.37 +/- 0.00 89 | 118.00 | 104.40 +/- 0.00 90 | 85.00 | 52.63 +/- 0.00 91 | 107.00 | 104.72 +/- 0.00 92 | 109.00 | 101.56 +/- 0.00 93 | 121.00 | 104.69 +/- 0.00 94 | 14.00 | 52.37 +/- 0.00 95 | 113.00 | 104.72 +/- 0.00 96 | 38.00 | 53.63 +/- 0.00 97 | 100.00 | 98.04 +/- 0.00 98 | 55.00 | 61.99 +/- 0.00 99 | 136.00 | 102.09 +/- 0.00 100 | 117.00 | 104.71 +/- 0.00 101 | 28.00 | 77.29 +/- 0.00 102 | 118.00 | 104.11 +/- 0.00 103 | 97.00 | 94.69 +/- 0.00 104 | 137.00 | 104.21 +/- 0.00 105 | 50.00 | 53.06 +/- 0.00 106 | 16.00 | 52.53 +/- 0.00 107 | 21.00 | 53.28 +/- 0.00 108 | 112.00 | 104.72 +/- 0.00 109 | 34.00 | 76.05 +/- 0.00 110 | 95.00 | 100.41 +/- 0.00 111 | 131.00 | 104.71 +/- 0.00 112 | 10.00 | 52.36 +/- 0.00 113 | 107.00 | 104.64 +/- 0.00 114 | 8.00 | 52.37 +/- 0.00 115 | 29.00 | 56.46 +/- 0.00 116 | 116.00 | 104.72 +/- 0.00 117 | 15.00 | 52.84 +/- 0.00 118 | 59.00 | 80.64 +/- 0.00 119 | 98.00 | 103.49 +/- 0.00 120 | 128.00 | 104.72 +/- 0.00 121 | 20.00 | 53.56 +/- 0.00 122 | 114.00 | 104.71 +/- 0.00 123 | 28.00 | 60.92 +/- 0.00 124 | 48.00 | 67.12 +/- 0.00 125 | 87.00 | 104.67 +/- 0.00 126 | eval mean loss: 386.42 127 | eval rmse: 27.80 128 | eval mae: 22.73 129 | eval score: 4548.01 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 21.56 134 | eval time: 0:00:01.501291 135 | **** end time: 2019-09-27 16:42:10.084262 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_2/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_2', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_2/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 31578 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-09-27 16:43:21.491024 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:43:21.495773 25 | ground truth | pred +/- std: 26 | 77.00 | 104.68 +/- 0.00 27 | 57.00 | 98.99 +/- 0.00 28 | 124.00 | 98.22 +/- 0.00 29 | 90.00 | 84.98 +/- 0.00 30 | 93.00 | 101.62 +/- 0.00 31 | 37.00 | 65.91 +/- 0.00 32 | 28.00 | 52.90 +/- 0.00 33 | 91.00 | 89.37 +/- 0.00 34 | 113.00 | 104.46 +/- 0.00 35 | 58.00 | 68.40 +/- 0.00 36 | 84.00 | 104.72 +/- 0.00 37 | 20.00 | 52.52 +/- 0.00 38 | 142.00 | 104.74 +/- 0.00 39 | 126.00 | 96.37 +/- 0.00 40 | 11.00 | 52.38 +/- 0.00 41 | 114.00 | 62.18 +/- 0.00 42 | 69.00 | 64.73 +/- 0.00 43 | 26.00 | 52.90 +/- 0.00 44 | 137.00 | 104.74 +/- 0.00 45 | 28.00 | 52.64 +/- 0.00 46 | 21.00 | 53.61 +/- 0.00 47 | 9.00 | 53.94 +/- 0.00 48 | 50.00 | 88.82 +/- 0.00 49 | 135.00 | 103.76 +/- 0.00 50 | 97.00 | 104.41 +/- 0.00 51 | 96.00 | 97.99 +/- 0.00 52 | 89.00 | 95.27 +/- 0.00 53 | 79.00 | 104.20 +/- 0.00 54 | 20.00 | 52.63 +/- 0.00 55 | 18.00 | 92.82 +/- 0.00 56 | 111.00 | 102.39 +/- 0.00 57 | 90.00 | 94.88 +/- 0.00 58 | 114.00 | 103.41 +/- 0.00 59 | 111.00 | 104.68 +/- 0.00 60 | 103.00 | 91.97 +/- 0.00 61 | 119.00 | 104.72 +/- 0.00 62 | 82.00 | 104.05 +/- 0.00 63 | 59.00 | 81.68 +/- 0.00 64 | 82.00 | 83.30 +/- 0.00 65 | 115.00 | 86.66 +/- 0.00 66 | 106.00 | 84.64 +/- 0.00 67 | 50.00 | 58.97 +/- 0.00 68 | 19.00 | 52.47 +/- 0.00 69 | 94.00 | 94.86 +/- 0.00 70 | 63.00 | 104.64 +/- 0.00 71 | 97.00 | 102.07 +/- 0.00 72 | 145.00 | 104.73 +/- 0.00 73 | 83.00 | 104.71 +/- 0.00 74 | 128.00 | 104.70 +/- 0.00 75 | 10.00 | 63.40 +/- 0.00 76 | 95.00 | 98.75 +/- 0.00 77 | 21.00 | 89.07 +/- 0.00 78 | 72.00 | 93.10 +/- 0.00 79 | 115.00 | 102.46 +/- 0.00 80 | 91.00 | 103.88 +/- 0.00 81 | 54.00 | 56.99 +/- 0.00 82 | 66.00 | 104.54 +/- 0.00 83 | 8.00 | 52.38 +/- 0.00 84 | 92.00 | 101.26 +/- 0.00 85 | 47.00 | 52.95 +/- 0.00 86 | 137.00 | 104.52 +/- 0.00 87 | 7.00 | 52.73 +/- 0.00 88 | 8.00 | 52.38 +/- 0.00 89 | 118.00 | 104.43 +/- 0.00 90 | 85.00 | 52.67 +/- 0.00 91 | 107.00 | 104.74 +/- 0.00 92 | 109.00 | 101.69 +/- 0.00 93 | 121.00 | 104.72 +/- 0.00 94 | 14.00 | 52.39 +/- 0.00 95 | 113.00 | 104.74 +/- 0.00 96 | 38.00 | 53.67 +/- 0.00 97 | 100.00 | 97.94 +/- 0.00 98 | 55.00 | 61.98 +/- 0.00 99 | 136.00 | 101.88 +/- 0.00 100 | 117.00 | 104.74 +/- 0.00 101 | 28.00 | 77.01 +/- 0.00 102 | 118.00 | 104.07 +/- 0.00 103 | 97.00 | 94.62 +/- 0.00 104 | 137.00 | 104.28 +/- 0.00 105 | 50.00 | 53.08 +/- 0.00 106 | 16.00 | 52.55 +/- 0.00 107 | 21.00 | 53.29 +/- 0.00 108 | 112.00 | 104.74 +/- 0.00 109 | 34.00 | 75.62 +/- 0.00 110 | 95.00 | 100.51 +/- 0.00 111 | 131.00 | 104.73 +/- 0.00 112 | 10.00 | 52.38 +/- 0.00 113 | 107.00 | 104.67 +/- 0.00 114 | 8.00 | 52.38 +/- 0.00 115 | 29.00 | 56.25 +/- 0.00 116 | 116.00 | 104.74 +/- 0.00 117 | 15.00 | 52.87 +/- 0.00 118 | 59.00 | 80.78 +/- 0.00 119 | 98.00 | 103.49 +/- 0.00 120 | 128.00 | 104.74 +/- 0.00 121 | 20.00 | 53.65 +/- 0.00 122 | 114.00 | 104.74 +/- 0.00 123 | 28.00 | 60.88 +/- 0.00 124 | 48.00 | 66.80 +/- 0.00 125 | 87.00 | 104.70 +/- 0.00 126 | eval mean loss: 385.98 127 | eval rmse: 27.78 128 | eval mae: 22.71 129 | eval score: 4571.15 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 21.57 134 | eval time: 0:00:01.466191 135 | **** end time: 2019-09-27 16:43:22.962200 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_3/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_3', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_3/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 31631 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-09-27 16:44:34.694905 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:44:34.699997 25 | ground truth | pred +/- std: 26 | 77.00 | 104.70 +/- 0.00 27 | 57.00 | 99.14 +/- 0.00 28 | 124.00 | 98.27 +/- 0.00 29 | 90.00 | 85.26 +/- 0.00 30 | 93.00 | 101.47 +/- 0.00 31 | 37.00 | 65.84 +/- 0.00 32 | 28.00 | 52.93 +/- 0.00 33 | 91.00 | 89.40 +/- 0.00 34 | 113.00 | 104.44 +/- 0.00 35 | 58.00 | 68.47 +/- 0.00 36 | 84.00 | 104.75 +/- 0.00 37 | 20.00 | 52.53 +/- 0.00 38 | 142.00 | 104.78 +/- 0.00 39 | 126.00 | 96.49 +/- 0.00 40 | 11.00 | 52.40 +/- 0.00 41 | 114.00 | 62.38 +/- 0.00 42 | 69.00 | 64.30 +/- 0.00 43 | 26.00 | 52.93 +/- 0.00 44 | 137.00 | 104.77 +/- 0.00 45 | 28.00 | 52.66 +/- 0.00 46 | 21.00 | 53.53 +/- 0.00 47 | 9.00 | 53.96 +/- 0.00 48 | 50.00 | 88.76 +/- 0.00 49 | 135.00 | 103.78 +/- 0.00 50 | 97.00 | 104.43 +/- 0.00 51 | 96.00 | 97.52 +/- 0.00 52 | 89.00 | 95.34 +/- 0.00 53 | 79.00 | 104.15 +/- 0.00 54 | 20.00 | 52.63 +/- 0.00 55 | 18.00 | 93.15 +/- 0.00 56 | 111.00 | 102.29 +/- 0.00 57 | 90.00 | 94.66 +/- 0.00 58 | 114.00 | 103.47 +/- 0.00 59 | 111.00 | 104.71 +/- 0.00 60 | 103.00 | 92.05 +/- 0.00 61 | 119.00 | 104.75 +/- 0.00 62 | 82.00 | 104.06 +/- 0.00 63 | 59.00 | 81.38 +/- 0.00 64 | 82.00 | 83.11 +/- 0.00 65 | 115.00 | 86.98 +/- 0.00 66 | 106.00 | 84.59 +/- 0.00 67 | 50.00 | 59.00 +/- 0.00 68 | 19.00 | 52.49 +/- 0.00 69 | 94.00 | 94.98 +/- 0.00 70 | 63.00 | 104.68 +/- 0.00 71 | 97.00 | 102.44 +/- 0.00 72 | 145.00 | 104.77 +/- 0.00 73 | 83.00 | 104.75 +/- 0.00 74 | 128.00 | 104.74 +/- 0.00 75 | 10.00 | 63.50 +/- 0.00 76 | 95.00 | 98.84 +/- 0.00 77 | 21.00 | 89.37 +/- 0.00 78 | 72.00 | 93.33 +/- 0.00 79 | 115.00 | 102.67 +/- 0.00 80 | 91.00 | 103.89 +/- 0.00 81 | 54.00 | 56.83 +/- 0.00 82 | 66.00 | 104.57 +/- 0.00 83 | 8.00 | 52.40 +/- 0.00 84 | 92.00 | 100.85 +/- 0.00 85 | 47.00 | 52.94 +/- 0.00 86 | 137.00 | 104.53 +/- 0.00 87 | 7.00 | 52.73 +/- 0.00 88 | 8.00 | 52.40 +/- 0.00 89 | 118.00 | 104.48 +/- 0.00 90 | 85.00 | 52.69 +/- 0.00 91 | 107.00 | 104.78 +/- 0.00 92 | 109.00 | 101.89 +/- 0.00 93 | 121.00 | 104.76 +/- 0.00 94 | 14.00 | 52.40 +/- 0.00 95 | 113.00 | 104.78 +/- 0.00 96 | 38.00 | 53.70 +/- 0.00 97 | 100.00 | 97.88 +/- 0.00 98 | 55.00 | 61.94 +/- 0.00 99 | 136.00 | 101.76 +/- 0.00 100 | 117.00 | 104.78 +/- 0.00 101 | 28.00 | 77.04 +/- 0.00 102 | 118.00 | 104.07 +/- 0.00 103 | 97.00 | 94.46 +/- 0.00 104 | 137.00 | 104.33 +/- 0.00 105 | 50.00 | 53.06 +/- 0.00 106 | 16.00 | 52.57 +/- 0.00 107 | 21.00 | 53.25 +/- 0.00 108 | 112.00 | 104.78 +/- 0.00 109 | 34.00 | 75.26 +/- 0.00 110 | 95.00 | 100.76 +/- 0.00 111 | 131.00 | 104.77 +/- 0.00 112 | 10.00 | 52.39 +/- 0.00 113 | 107.00 | 104.70 +/- 0.00 114 | 8.00 | 52.40 +/- 0.00 115 | 29.00 | 56.08 +/- 0.00 116 | 116.00 | 104.78 +/- 0.00 117 | 15.00 | 52.88 +/- 0.00 118 | 59.00 | 80.97 +/- 0.00 119 | 98.00 | 103.54 +/- 0.00 120 | 128.00 | 104.78 +/- 0.00 121 | 20.00 | 53.67 +/- 0.00 122 | 114.00 | 104.78 +/- 0.00 123 | 28.00 | 61.14 +/- 0.00 124 | 48.00 | 66.35 +/- 0.00 125 | 87.00 | 104.74 +/- 0.00 126 | eval mean loss: 386.08 127 | eval rmse: 27.79 128 | eval mae: 22.70 129 | eval score: 4660.20 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 21.58 134 | eval time: 0:00:01.467638 135 | **** end time: 2019-09-27 16:44:36.167874 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_4/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_4', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_4/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 31705 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-09-27 16:45:47.429311 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:45:47.434288 25 | ground truth | pred +/- std: 26 | 77.00 | 104.67 +/- 0.00 27 | 57.00 | 99.00 +/- 0.00 28 | 124.00 | 98.23 +/- 0.00 29 | 90.00 | 84.99 +/- 0.00 30 | 93.00 | 101.63 +/- 0.00 31 | 37.00 | 65.95 +/- 0.00 32 | 28.00 | 52.89 +/- 0.00 33 | 91.00 | 89.42 +/- 0.00 34 | 113.00 | 104.44 +/- 0.00 35 | 58.00 | 68.62 +/- 0.00 36 | 84.00 | 104.71 +/- 0.00 37 | 20.00 | 52.51 +/- 0.00 38 | 142.00 | 104.74 +/- 0.00 39 | 126.00 | 96.49 +/- 0.00 40 | 11.00 | 52.38 +/- 0.00 41 | 114.00 | 62.08 +/- 0.00 42 | 69.00 | 64.64 +/- 0.00 43 | 26.00 | 52.89 +/- 0.00 44 | 137.00 | 104.73 +/- 0.00 45 | 28.00 | 52.63 +/- 0.00 46 | 21.00 | 53.63 +/- 0.00 47 | 9.00 | 53.94 +/- 0.00 48 | 50.00 | 88.81 +/- 0.00 49 | 135.00 | 103.77 +/- 0.00 50 | 97.00 | 104.41 +/- 0.00 51 | 96.00 | 98.00 +/- 0.00 52 | 89.00 | 95.29 +/- 0.00 53 | 79.00 | 104.18 +/- 0.00 54 | 20.00 | 52.62 +/- 0.00 55 | 18.00 | 92.85 +/- 0.00 56 | 111.00 | 102.42 +/- 0.00 57 | 90.00 | 94.99 +/- 0.00 58 | 114.00 | 103.43 +/- 0.00 59 | 111.00 | 104.67 +/- 0.00 60 | 103.00 | 92.05 +/- 0.00 61 | 119.00 | 104.72 +/- 0.00 62 | 82.00 | 104.06 +/- 0.00 63 | 59.00 | 81.61 +/- 0.00 64 | 82.00 | 83.29 +/- 0.00 65 | 115.00 | 86.62 +/- 0.00 66 | 106.00 | 84.65 +/- 0.00 67 | 50.00 | 58.89 +/- 0.00 68 | 19.00 | 52.47 +/- 0.00 69 | 94.00 | 94.90 +/- 0.00 70 | 63.00 | 104.63 +/- 0.00 71 | 97.00 | 102.10 +/- 0.00 72 | 145.00 | 104.73 +/- 0.00 73 | 83.00 | 104.71 +/- 0.00 74 | 128.00 | 104.70 +/- 0.00 75 | 10.00 | 63.32 +/- 0.00 76 | 95.00 | 98.84 +/- 0.00 77 | 21.00 | 89.02 +/- 0.00 78 | 72.00 | 93.05 +/- 0.00 79 | 115.00 | 102.44 +/- 0.00 80 | 91.00 | 103.89 +/- 0.00 81 | 54.00 | 56.91 +/- 0.00 82 | 66.00 | 104.54 +/- 0.00 83 | 8.00 | 52.38 +/- 0.00 84 | 92.00 | 101.31 +/- 0.00 85 | 47.00 | 52.92 +/- 0.00 86 | 137.00 | 104.51 +/- 0.00 87 | 7.00 | 52.72 +/- 0.00 88 | 8.00 | 52.38 +/- 0.00 89 | 118.00 | 104.43 +/- 0.00 90 | 85.00 | 52.66 +/- 0.00 91 | 107.00 | 104.74 +/- 0.00 92 | 109.00 | 101.69 +/- 0.00 93 | 121.00 | 104.72 +/- 0.00 94 | 14.00 | 52.38 +/- 0.00 95 | 113.00 | 104.74 +/- 0.00 96 | 38.00 | 53.66 +/- 0.00 97 | 100.00 | 97.97 +/- 0.00 98 | 55.00 | 61.89 +/- 0.00 99 | 136.00 | 101.97 +/- 0.00 100 | 117.00 | 104.74 +/- 0.00 101 | 28.00 | 77.11 +/- 0.00 102 | 118.00 | 104.08 +/- 0.00 103 | 97.00 | 94.58 +/- 0.00 104 | 137.00 | 104.27 +/- 0.00 105 | 50.00 | 53.07 +/- 0.00 106 | 16.00 | 52.54 +/- 0.00 107 | 21.00 | 53.27 +/- 0.00 108 | 112.00 | 104.74 +/- 0.00 109 | 34.00 | 75.72 +/- 0.00 110 | 95.00 | 100.50 +/- 0.00 111 | 131.00 | 104.73 +/- 0.00 112 | 10.00 | 52.37 +/- 0.00 113 | 107.00 | 104.66 +/- 0.00 114 | 8.00 | 52.38 +/- 0.00 115 | 29.00 | 56.30 +/- 0.00 116 | 116.00 | 104.74 +/- 0.00 117 | 15.00 | 52.84 +/- 0.00 118 | 59.00 | 80.74 +/- 0.00 119 | 98.00 | 103.49 +/- 0.00 120 | 128.00 | 104.74 +/- 0.00 121 | 20.00 | 53.57 +/- 0.00 122 | 114.00 | 104.74 +/- 0.00 123 | 28.00 | 60.94 +/- 0.00 124 | 48.00 | 66.71 +/- 0.00 125 | 87.00 | 104.69 +/- 0.00 126 | eval mean loss: 385.96 127 | eval rmse: 27.78 128 | eval mae: 22.71 129 | eval score: 4572.92 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 21.58 134 | eval time: 0:00:01.465773 135 | **** end time: 2019-09-27 16:45:48.900309 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_5/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_5', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_5/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 31758 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-09-27 16:46:59.538596 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:46:59.544087 25 | ground truth | pred +/- std: 26 | 77.00 | 104.67 +/- 0.00 27 | 57.00 | 99.06 +/- 0.00 28 | 124.00 | 98.26 +/- 0.00 29 | 90.00 | 84.86 +/- 0.00 30 | 93.00 | 101.59 +/- 0.00 31 | 37.00 | 65.94 +/- 0.00 32 | 28.00 | 52.88 +/- 0.00 33 | 91.00 | 89.38 +/- 0.00 34 | 113.00 | 104.45 +/- 0.00 35 | 58.00 | 68.45 +/- 0.00 36 | 84.00 | 104.70 +/- 0.00 37 | 20.00 | 52.51 +/- 0.00 38 | 142.00 | 104.73 +/- 0.00 39 | 126.00 | 96.45 +/- 0.00 40 | 11.00 | 52.37 +/- 0.00 41 | 114.00 | 62.20 +/- 0.00 42 | 69.00 | 64.85 +/- 0.00 43 | 26.00 | 52.88 +/- 0.00 44 | 137.00 | 104.73 +/- 0.00 45 | 28.00 | 52.63 +/- 0.00 46 | 21.00 | 53.64 +/- 0.00 47 | 9.00 | 53.94 +/- 0.00 48 | 50.00 | 88.75 +/- 0.00 49 | 135.00 | 103.74 +/- 0.00 50 | 97.00 | 104.41 +/- 0.00 51 | 96.00 | 98.14 +/- 0.00 52 | 89.00 | 95.12 +/- 0.00 53 | 79.00 | 104.20 +/- 0.00 54 | 20.00 | 52.63 +/- 0.00 55 | 18.00 | 92.92 +/- 0.00 56 | 111.00 | 102.38 +/- 0.00 57 | 90.00 | 94.89 +/- 0.00 58 | 114.00 | 103.40 +/- 0.00 59 | 111.00 | 104.67 +/- 0.00 60 | 103.00 | 92.03 +/- 0.00 61 | 119.00 | 104.71 +/- 0.00 62 | 82.00 | 104.05 +/- 0.00 63 | 59.00 | 81.88 +/- 0.00 64 | 82.00 | 83.45 +/- 0.00 65 | 115.00 | 86.45 +/- 0.00 66 | 106.00 | 84.42 +/- 0.00 67 | 50.00 | 58.87 +/- 0.00 68 | 19.00 | 52.47 +/- 0.00 69 | 94.00 | 94.87 +/- 0.00 70 | 63.00 | 104.62 +/- 0.00 71 | 97.00 | 101.96 +/- 0.00 72 | 145.00 | 104.72 +/- 0.00 73 | 83.00 | 104.70 +/- 0.00 74 | 128.00 | 104.69 +/- 0.00 75 | 10.00 | 63.44 +/- 0.00 76 | 95.00 | 98.77 +/- 0.00 77 | 21.00 | 88.94 +/- 0.00 78 | 72.00 | 93.03 +/- 0.00 79 | 115.00 | 102.43 +/- 0.00 80 | 91.00 | 103.85 +/- 0.00 81 | 54.00 | 56.99 +/- 0.00 82 | 66.00 | 104.52 +/- 0.00 83 | 8.00 | 52.37 +/- 0.00 84 | 92.00 | 101.39 +/- 0.00 85 | 47.00 | 52.94 +/- 0.00 86 | 137.00 | 104.51 +/- 0.00 87 | 7.00 | 52.72 +/- 0.00 88 | 8.00 | 52.37 +/- 0.00 89 | 118.00 | 104.42 +/- 0.00 90 | 85.00 | 52.65 +/- 0.00 91 | 107.00 | 104.73 +/- 0.00 92 | 109.00 | 101.63 +/- 0.00 93 | 121.00 | 104.71 +/- 0.00 94 | 14.00 | 52.38 +/- 0.00 95 | 113.00 | 104.73 +/- 0.00 96 | 38.00 | 53.68 +/- 0.00 97 | 100.00 | 97.91 +/- 0.00 98 | 55.00 | 62.00 +/- 0.00 99 | 136.00 | 101.99 +/- 0.00 100 | 117.00 | 104.73 +/- 0.00 101 | 28.00 | 77.17 +/- 0.00 102 | 118.00 | 104.06 +/- 0.00 103 | 97.00 | 94.67 +/- 0.00 104 | 137.00 | 104.27 +/- 0.00 105 | 50.00 | 53.08 +/- 0.00 106 | 16.00 | 52.54 +/- 0.00 107 | 21.00 | 53.31 +/- 0.00 108 | 112.00 | 104.73 +/- 0.00 109 | 34.00 | 75.79 +/- 0.00 110 | 95.00 | 100.49 +/- 0.00 111 | 131.00 | 104.72 +/- 0.00 112 | 10.00 | 52.37 +/- 0.00 113 | 107.00 | 104.66 +/- 0.00 114 | 8.00 | 52.38 +/- 0.00 115 | 29.00 | 56.32 +/- 0.00 116 | 116.00 | 104.73 +/- 0.00 117 | 15.00 | 52.86 +/- 0.00 118 | 59.00 | 80.80 +/- 0.00 119 | 98.00 | 103.51 +/- 0.00 120 | 128.00 | 104.73 +/- 0.00 121 | 20.00 | 53.61 +/- 0.00 122 | 114.00 | 104.73 +/- 0.00 123 | 28.00 | 60.92 +/- 0.00 124 | 48.00 | 66.84 +/- 0.00 125 | 87.00 | 104.69 +/- 0.00 126 | eval mean loss: 386.24 127 | eval rmse: 27.79 128 | eval mae: 22.72 129 | eval score: 4582.45 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 21.56 134 | eval time: 0:00:01.480573 135 | **** end time: 2019-09-27 16:47:01.024915 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_6/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_6', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_6/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 31812 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-09-27 16:48:12.191279 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:48:12.196360 25 | ground truth | pred +/- std: 26 | 77.00 | 104.67 +/- 0.00 27 | 57.00 | 99.02 +/- 0.00 28 | 124.00 | 98.20 +/- 0.00 29 | 90.00 | 85.00 +/- 0.00 30 | 93.00 | 101.52 +/- 0.00 31 | 37.00 | 66.03 +/- 0.00 32 | 28.00 | 52.87 +/- 0.00 33 | 91.00 | 89.47 +/- 0.00 34 | 113.00 | 104.41 +/- 0.00 35 | 58.00 | 68.79 +/- 0.00 36 | 84.00 | 104.72 +/- 0.00 37 | 20.00 | 52.51 +/- 0.00 38 | 142.00 | 104.75 +/- 0.00 39 | 126.00 | 96.58 +/- 0.00 40 | 11.00 | 52.39 +/- 0.00 41 | 114.00 | 62.18 +/- 0.00 42 | 69.00 | 64.50 +/- 0.00 43 | 26.00 | 52.87 +/- 0.00 44 | 137.00 | 104.75 +/- 0.00 45 | 28.00 | 52.63 +/- 0.00 46 | 21.00 | 53.55 +/- 0.00 47 | 9.00 | 53.98 +/- 0.00 48 | 50.00 | 88.80 +/- 0.00 49 | 135.00 | 103.71 +/- 0.00 50 | 97.00 | 104.40 +/- 0.00 51 | 96.00 | 98.10 +/- 0.00 52 | 89.00 | 95.13 +/- 0.00 53 | 79.00 | 104.14 +/- 0.00 54 | 20.00 | 52.61 +/- 0.00 55 | 18.00 | 92.83 +/- 0.00 56 | 111.00 | 102.43 +/- 0.00 57 | 90.00 | 95.17 +/- 0.00 58 | 114.00 | 103.40 +/- 0.00 59 | 111.00 | 104.67 +/- 0.00 60 | 103.00 | 92.12 +/- 0.00 61 | 119.00 | 104.73 +/- 0.00 62 | 82.00 | 104.05 +/- 0.00 63 | 59.00 | 81.67 +/- 0.00 64 | 82.00 | 83.27 +/- 0.00 65 | 115.00 | 86.52 +/- 0.00 66 | 106.00 | 84.46 +/- 0.00 67 | 50.00 | 58.83 +/- 0.00 68 | 19.00 | 52.47 +/- 0.00 69 | 94.00 | 95.05 +/- 0.00 70 | 63.00 | 104.63 +/- 0.00 71 | 97.00 | 102.05 +/- 0.00 72 | 145.00 | 104.74 +/- 0.00 73 | 83.00 | 104.72 +/- 0.00 74 | 128.00 | 104.71 +/- 0.00 75 | 10.00 | 63.35 +/- 0.00 76 | 95.00 | 98.90 +/- 0.00 77 | 21.00 | 89.13 +/- 0.00 78 | 72.00 | 92.94 +/- 0.00 79 | 115.00 | 102.30 +/- 0.00 80 | 91.00 | 103.90 +/- 0.00 81 | 54.00 | 56.89 +/- 0.00 82 | 66.00 | 104.54 +/- 0.00 83 | 8.00 | 52.39 +/- 0.00 84 | 92.00 | 101.39 +/- 0.00 85 | 47.00 | 52.91 +/- 0.00 86 | 137.00 | 104.50 +/- 0.00 87 | 7.00 | 52.72 +/- 0.00 88 | 8.00 | 52.39 +/- 0.00 89 | 118.00 | 104.43 +/- 0.00 90 | 85.00 | 52.65 +/- 0.00 91 | 107.00 | 104.76 +/- 0.00 92 | 109.00 | 101.73 +/- 0.00 93 | 121.00 | 104.73 +/- 0.00 94 | 14.00 | 52.39 +/- 0.00 95 | 113.00 | 104.76 +/- 0.00 96 | 38.00 | 53.64 +/- 0.00 97 | 100.00 | 98.01 +/- 0.00 98 | 55.00 | 61.88 +/- 0.00 99 | 136.00 | 102.03 +/- 0.00 100 | 117.00 | 104.75 +/- 0.00 101 | 28.00 | 77.26 +/- 0.00 102 | 118.00 | 104.08 +/- 0.00 103 | 97.00 | 94.58 +/- 0.00 104 | 137.00 | 104.26 +/- 0.00 105 | 50.00 | 53.05 +/- 0.00 106 | 16.00 | 52.55 +/- 0.00 107 | 21.00 | 53.25 +/- 0.00 108 | 112.00 | 104.76 +/- 0.00 109 | 34.00 | 75.79 +/- 0.00 110 | 95.00 | 100.62 +/- 0.00 111 | 131.00 | 104.74 +/- 0.00 112 | 10.00 | 52.38 +/- 0.00 113 | 107.00 | 104.66 +/- 0.00 114 | 8.00 | 52.39 +/- 0.00 115 | 29.00 | 56.23 +/- 0.00 116 | 116.00 | 104.76 +/- 0.00 117 | 15.00 | 52.83 +/- 0.00 118 | 59.00 | 80.82 +/- 0.00 119 | 98.00 | 103.49 +/- 0.00 120 | 128.00 | 104.76 +/- 0.00 121 | 20.00 | 53.56 +/- 0.00 122 | 114.00 | 104.76 +/- 0.00 123 | 28.00 | 61.04 +/- 0.00 124 | 48.00 | 66.61 +/- 0.00 125 | 87.00 | 104.70 +/- 0.00 126 | eval mean loss: 386.15 127 | eval rmse: 27.79 128 | eval mae: 22.72 129 | eval score: 4583.26 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 21.58 134 | eval time: 0:00:01.454718 135 | **** end time: 2019-09-27 16:48:13.651334 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_7/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_7', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_7/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 31867 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-09-27 16:49:24.093496 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:49:24.098427 25 | ground truth | pred +/- std: 26 | 77.00 | 104.66 +/- 0.00 27 | 57.00 | 98.97 +/- 0.00 28 | 124.00 | 98.17 +/- 0.00 29 | 90.00 | 84.96 +/- 0.00 30 | 93.00 | 101.57 +/- 0.00 31 | 37.00 | 65.97 +/- 0.00 32 | 28.00 | 52.88 +/- 0.00 33 | 91.00 | 89.47 +/- 0.00 34 | 113.00 | 104.43 +/- 0.00 35 | 58.00 | 68.43 +/- 0.00 36 | 84.00 | 104.70 +/- 0.00 37 | 20.00 | 52.51 +/- 0.00 38 | 142.00 | 104.72 +/- 0.00 39 | 126.00 | 96.36 +/- 0.00 40 | 11.00 | 52.37 +/- 0.00 41 | 114.00 | 62.26 +/- 0.00 42 | 69.00 | 64.77 +/- 0.00 43 | 26.00 | 52.89 +/- 0.00 44 | 137.00 | 104.72 +/- 0.00 45 | 28.00 | 52.62 +/- 0.00 46 | 21.00 | 53.62 +/- 0.00 47 | 9.00 | 53.94 +/- 0.00 48 | 50.00 | 89.03 +/- 0.00 49 | 135.00 | 103.75 +/- 0.00 50 | 97.00 | 104.42 +/- 0.00 51 | 96.00 | 98.15 +/- 0.00 52 | 89.00 | 95.27 +/- 0.00 53 | 79.00 | 104.18 +/- 0.00 54 | 20.00 | 52.62 +/- 0.00 55 | 18.00 | 92.84 +/- 0.00 56 | 111.00 | 102.38 +/- 0.00 57 | 90.00 | 94.95 +/- 0.00 58 | 114.00 | 103.45 +/- 0.00 59 | 111.00 | 104.66 +/- 0.00 60 | 103.00 | 92.00 +/- 0.00 61 | 119.00 | 104.70 +/- 0.00 62 | 82.00 | 104.03 +/- 0.00 63 | 59.00 | 81.78 +/- 0.00 64 | 82.00 | 83.26 +/- 0.00 65 | 115.00 | 86.57 +/- 0.00 66 | 106.00 | 84.76 +/- 0.00 67 | 50.00 | 58.79 +/- 0.00 68 | 19.00 | 52.47 +/- 0.00 69 | 94.00 | 94.95 +/- 0.00 70 | 63.00 | 104.62 +/- 0.00 71 | 97.00 | 102.16 +/- 0.00 72 | 145.00 | 104.71 +/- 0.00 73 | 83.00 | 104.70 +/- 0.00 74 | 128.00 | 104.69 +/- 0.00 75 | 10.00 | 63.43 +/- 0.00 76 | 95.00 | 98.84 +/- 0.00 77 | 21.00 | 89.11 +/- 0.00 78 | 72.00 | 92.98 +/- 0.00 79 | 115.00 | 102.49 +/- 0.00 80 | 91.00 | 103.88 +/- 0.00 81 | 54.00 | 56.95 +/- 0.00 82 | 66.00 | 104.53 +/- 0.00 83 | 8.00 | 52.37 +/- 0.00 84 | 92.00 | 101.41 +/- 0.00 85 | 47.00 | 52.94 +/- 0.00 86 | 137.00 | 104.50 +/- 0.00 87 | 7.00 | 52.72 +/- 0.00 88 | 8.00 | 52.37 +/- 0.00 89 | 118.00 | 104.41 +/- 0.00 90 | 85.00 | 52.65 +/- 0.00 91 | 107.00 | 104.73 +/- 0.00 92 | 109.00 | 101.76 +/- 0.00 93 | 121.00 | 104.70 +/- 0.00 94 | 14.00 | 52.38 +/- 0.00 95 | 113.00 | 104.73 +/- 0.00 96 | 38.00 | 53.68 +/- 0.00 97 | 100.00 | 98.02 +/- 0.00 98 | 55.00 | 62.01 +/- 0.00 99 | 136.00 | 101.95 +/- 0.00 100 | 117.00 | 104.73 +/- 0.00 101 | 28.00 | 77.04 +/- 0.00 102 | 118.00 | 104.10 +/- 0.00 103 | 97.00 | 94.77 +/- 0.00 104 | 137.00 | 104.29 +/- 0.00 105 | 50.00 | 53.08 +/- 0.00 106 | 16.00 | 52.54 +/- 0.00 107 | 21.00 | 53.30 +/- 0.00 108 | 112.00 | 104.73 +/- 0.00 109 | 34.00 | 75.71 +/- 0.00 110 | 95.00 | 100.47 +/- 0.00 111 | 131.00 | 104.72 +/- 0.00 112 | 10.00 | 52.37 +/- 0.00 113 | 107.00 | 104.65 +/- 0.00 114 | 8.00 | 52.38 +/- 0.00 115 | 29.00 | 56.28 +/- 0.00 116 | 116.00 | 104.73 +/- 0.00 117 | 15.00 | 52.86 +/- 0.00 118 | 59.00 | 80.71 +/- 0.00 119 | 98.00 | 103.52 +/- 0.00 120 | 128.00 | 104.73 +/- 0.00 121 | 20.00 | 53.63 +/- 0.00 122 | 114.00 | 104.73 +/- 0.00 123 | 28.00 | 60.91 +/- 0.00 124 | 48.00 | 66.80 +/- 0.00 125 | 87.00 | 104.68 +/- 0.00 126 | eval mean loss: 386.09 127 | eval rmse: 27.79 128 | eval mae: 22.72 129 | eval score: 4580.14 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 21.57 134 | eval time: 0:00:01.447879 135 | **** end time: 2019-09-27 16:49:25.546598 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_8/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_8', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_8/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 31921 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-09-27 16:50:36.194151 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:50:36.199189 25 | ground truth | pred +/- std: 26 | 77.00 | 104.66 +/- 0.00 27 | 57.00 | 99.01 +/- 0.00 28 | 124.00 | 98.13 +/- 0.00 29 | 90.00 | 85.08 +/- 0.00 30 | 93.00 | 101.57 +/- 0.00 31 | 37.00 | 65.91 +/- 0.00 32 | 28.00 | 52.89 +/- 0.00 33 | 91.00 | 89.49 +/- 0.00 34 | 113.00 | 104.43 +/- 0.00 35 | 58.00 | 68.61 +/- 0.00 36 | 84.00 | 104.71 +/- 0.00 37 | 20.00 | 52.51 +/- 0.00 38 | 142.00 | 104.74 +/- 0.00 39 | 126.00 | 96.41 +/- 0.00 40 | 11.00 | 52.38 +/- 0.00 41 | 114.00 | 62.19 +/- 0.00 42 | 69.00 | 64.76 +/- 0.00 43 | 26.00 | 52.91 +/- 0.00 44 | 137.00 | 104.74 +/- 0.00 45 | 28.00 | 52.63 +/- 0.00 46 | 21.00 | 53.60 +/- 0.00 47 | 9.00 | 53.93 +/- 0.00 48 | 50.00 | 89.00 +/- 0.00 49 | 135.00 | 103.69 +/- 0.00 50 | 97.00 | 104.41 +/- 0.00 51 | 96.00 | 98.01 +/- 0.00 52 | 89.00 | 95.16 +/- 0.00 53 | 79.00 | 104.16 +/- 0.00 54 | 20.00 | 52.62 +/- 0.00 55 | 18.00 | 92.81 +/- 0.00 56 | 111.00 | 102.41 +/- 0.00 57 | 90.00 | 94.96 +/- 0.00 58 | 114.00 | 103.40 +/- 0.00 59 | 111.00 | 104.66 +/- 0.00 60 | 103.00 | 92.12 +/- 0.00 61 | 119.00 | 104.72 +/- 0.00 62 | 82.00 | 104.01 +/- 0.00 63 | 59.00 | 81.70 +/- 0.00 64 | 82.00 | 83.25 +/- 0.00 65 | 115.00 | 86.72 +/- 0.00 66 | 106.00 | 84.62 +/- 0.00 67 | 50.00 | 58.92 +/- 0.00 68 | 19.00 | 52.47 +/- 0.00 69 | 94.00 | 95.02 +/- 0.00 70 | 63.00 | 104.63 +/- 0.00 71 | 97.00 | 102.08 +/- 0.00 72 | 145.00 | 104.73 +/- 0.00 73 | 83.00 | 104.71 +/- 0.00 74 | 128.00 | 104.70 +/- 0.00 75 | 10.00 | 63.52 +/- 0.00 76 | 95.00 | 98.88 +/- 0.00 77 | 21.00 | 89.16 +/- 0.00 78 | 72.00 | 93.05 +/- 0.00 79 | 115.00 | 102.37 +/- 0.00 80 | 91.00 | 103.89 +/- 0.00 81 | 54.00 | 56.99 +/- 0.00 82 | 66.00 | 104.53 +/- 0.00 83 | 8.00 | 52.38 +/- 0.00 84 | 92.00 | 101.35 +/- 0.00 85 | 47.00 | 52.93 +/- 0.00 86 | 137.00 | 104.51 +/- 0.00 87 | 7.00 | 52.74 +/- 0.00 88 | 8.00 | 52.38 +/- 0.00 89 | 118.00 | 104.42 +/- 0.00 90 | 85.00 | 52.66 +/- 0.00 91 | 107.00 | 104.75 +/- 0.00 92 | 109.00 | 101.81 +/- 0.00 93 | 121.00 | 104.72 +/- 0.00 94 | 14.00 | 52.39 +/- 0.00 95 | 113.00 | 104.75 +/- 0.00 96 | 38.00 | 53.69 +/- 0.00 97 | 100.00 | 97.99 +/- 0.00 98 | 55.00 | 62.02 +/- 0.00 99 | 136.00 | 101.94 +/- 0.00 100 | 117.00 | 104.74 +/- 0.00 101 | 28.00 | 77.11 +/- 0.00 102 | 118.00 | 104.08 +/- 0.00 103 | 97.00 | 94.64 +/- 0.00 104 | 137.00 | 104.28 +/- 0.00 105 | 50.00 | 53.07 +/- 0.00 106 | 16.00 | 52.55 +/- 0.00 107 | 21.00 | 53.28 +/- 0.00 108 | 112.00 | 104.74 +/- 0.00 109 | 34.00 | 75.77 +/- 0.00 110 | 95.00 | 100.51 +/- 0.00 111 | 131.00 | 104.73 +/- 0.00 112 | 10.00 | 52.38 +/- 0.00 113 | 107.00 | 104.65 +/- 0.00 114 | 8.00 | 52.39 +/- 0.00 115 | 29.00 | 56.31 +/- 0.00 116 | 116.00 | 104.75 +/- 0.00 117 | 15.00 | 52.86 +/- 0.00 118 | 59.00 | 80.86 +/- 0.00 119 | 98.00 | 103.48 +/- 0.00 120 | 128.00 | 104.75 +/- 0.00 121 | 20.00 | 53.60 +/- 0.00 122 | 114.00 | 104.74 +/- 0.00 123 | 28.00 | 60.96 +/- 0.00 124 | 48.00 | 66.79 +/- 0.00 125 | 87.00 | 104.69 +/- 0.00 126 | eval mean loss: 386.29 127 | eval rmse: 27.80 128 | eval mae: 22.72 129 | eval score: 4583.71 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 21.56 134 | eval time: 0:00:01.452782 135 | **** end time: 2019-09-27 16:50:37.652267 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_9/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_9', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD001/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_9/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 31974 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-09-27 16:51:48.564319 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:51:48.569304 25 | ground truth | pred +/- std: 26 | 77.00 | 104.64 +/- 0.00 27 | 57.00 | 98.86 +/- 0.00 28 | 124.00 | 98.09 +/- 0.00 29 | 90.00 | 85.10 +/- 0.00 30 | 93.00 | 101.66 +/- 0.00 31 | 37.00 | 66.06 +/- 0.00 32 | 28.00 | 52.85 +/- 0.00 33 | 91.00 | 89.39 +/- 0.00 34 | 113.00 | 104.43 +/- 0.00 35 | 58.00 | 68.52 +/- 0.00 36 | 84.00 | 104.68 +/- 0.00 37 | 20.00 | 52.49 +/- 0.00 38 | 142.00 | 104.71 +/- 0.00 39 | 126.00 | 96.47 +/- 0.00 40 | 11.00 | 52.36 +/- 0.00 41 | 114.00 | 62.07 +/- 0.00 42 | 69.00 | 64.94 +/- 0.00 43 | 26.00 | 52.85 +/- 0.00 44 | 137.00 | 104.71 +/- 0.00 45 | 28.00 | 52.60 +/- 0.00 46 | 21.00 | 53.64 +/- 0.00 47 | 9.00 | 53.98 +/- 0.00 48 | 50.00 | 89.20 +/- 0.00 49 | 135.00 | 103.69 +/- 0.00 50 | 97.00 | 104.40 +/- 0.00 51 | 96.00 | 98.50 +/- 0.00 52 | 89.00 | 95.23 +/- 0.00 53 | 79.00 | 104.18 +/- 0.00 54 | 20.00 | 52.60 +/- 0.00 55 | 18.00 | 92.42 +/- 0.00 56 | 111.00 | 102.57 +/- 0.00 57 | 90.00 | 95.34 +/- 0.00 58 | 114.00 | 103.43 +/- 0.00 59 | 111.00 | 104.63 +/- 0.00 60 | 103.00 | 92.14 +/- 0.00 61 | 119.00 | 104.69 +/- 0.00 62 | 82.00 | 104.01 +/- 0.00 63 | 59.00 | 81.74 +/- 0.00 64 | 82.00 | 83.39 +/- 0.00 65 | 115.00 | 86.27 +/- 0.00 66 | 106.00 | 84.54 +/- 0.00 67 | 50.00 | 58.65 +/- 0.00 68 | 19.00 | 52.46 +/- 0.00 69 | 94.00 | 95.00 +/- 0.00 70 | 63.00 | 104.59 +/- 0.00 71 | 97.00 | 101.74 +/- 0.00 72 | 145.00 | 104.70 +/- 0.00 73 | 83.00 | 104.67 +/- 0.00 74 | 128.00 | 104.67 +/- 0.00 75 | 10.00 | 63.22 +/- 0.00 76 | 95.00 | 98.98 +/- 0.00 77 | 21.00 | 89.04 +/- 0.00 78 | 72.00 | 92.69 +/- 0.00 79 | 115.00 | 102.16 +/- 0.00 80 | 91.00 | 103.89 +/- 0.00 81 | 54.00 | 57.08 +/- 0.00 82 | 66.00 | 104.49 +/- 0.00 83 | 8.00 | 52.36 +/- 0.00 84 | 92.00 | 101.82 +/- 0.00 85 | 47.00 | 52.93 +/- 0.00 86 | 137.00 | 104.49 +/- 0.00 87 | 7.00 | 52.71 +/- 0.00 88 | 8.00 | 52.37 +/- 0.00 89 | 118.00 | 104.38 +/- 0.00 90 | 85.00 | 52.63 +/- 0.00 91 | 107.00 | 104.72 +/- 0.00 92 | 109.00 | 101.57 +/- 0.00 93 | 121.00 | 104.68 +/- 0.00 94 | 14.00 | 52.37 +/- 0.00 95 | 113.00 | 104.71 +/- 0.00 96 | 38.00 | 53.64 +/- 0.00 97 | 100.00 | 98.07 +/- 0.00 98 | 55.00 | 61.98 +/- 0.00 99 | 136.00 | 102.12 +/- 0.00 100 | 117.00 | 104.71 +/- 0.00 101 | 28.00 | 77.18 +/- 0.00 102 | 118.00 | 104.11 +/- 0.00 103 | 97.00 | 94.64 +/- 0.00 104 | 137.00 | 104.23 +/- 0.00 105 | 50.00 | 53.07 +/- 0.00 106 | 16.00 | 52.53 +/- 0.00 107 | 21.00 | 53.28 +/- 0.00 108 | 112.00 | 104.71 +/- 0.00 109 | 34.00 | 76.05 +/- 0.00 110 | 95.00 | 100.35 +/- 0.00 111 | 131.00 | 104.70 +/- 0.00 112 | 10.00 | 52.36 +/- 0.00 113 | 107.00 | 104.62 +/- 0.00 114 | 8.00 | 52.37 +/- 0.00 115 | 29.00 | 56.38 +/- 0.00 116 | 116.00 | 104.72 +/- 0.00 117 | 15.00 | 52.82 +/- 0.00 118 | 59.00 | 80.66 +/- 0.00 119 | 98.00 | 103.44 +/- 0.00 120 | 128.00 | 104.71 +/- 0.00 121 | 20.00 | 53.50 +/- 0.00 122 | 114.00 | 104.71 +/- 0.00 123 | 28.00 | 60.69 +/- 0.00 124 | 48.00 | 67.07 +/- 0.00 125 | 87.00 | 104.66 +/- 0.00 126 | eval mean loss: 385.97 127 | eval rmse: 27.78 128 | eval mae: 22.73 129 | eval score: 4500.79 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 21.57 134 | eval time: 0:00:01.448681 135 | **** end time: 2019-09-27 16:51:50.018282 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_0/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_0', model='frequentist_dense3', model_path='log/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_0/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 32028 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-09-27 16:53:00.375205 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:53:00.378294 25 | ground truth | pred +/- std: 26 | 77.00 | 120.92 +/- 0.00 27 | 57.00 | 65.72 +/- 0.00 28 | 124.00 | 93.86 +/- 0.00 29 | 90.00 | 104.95 +/- 0.00 30 | 93.00 | 112.66 +/- 0.00 31 | 37.00 | 41.26 +/- 0.00 32 | 28.00 | 27.16 +/- 0.00 33 | 91.00 | 105.28 +/- 0.00 34 | 113.00 | 92.40 +/- 0.00 35 | 58.00 | 85.27 +/- 0.00 36 | 84.00 | 82.20 +/- 0.00 37 | 20.00 | 18.21 +/- 0.00 38 | 142.00 | 123.46 +/- 0.00 39 | 126.00 | 113.54 +/- 0.00 40 | 11.00 | 11.79 +/- 0.00 41 | 114.00 | 81.22 +/- 0.00 42 | 69.00 | 50.35 +/- 0.00 43 | 26.00 | 30.91 +/- 0.00 44 | 137.00 | 116.63 +/- 0.00 45 | 28.00 | 31.10 +/- 0.00 46 | 21.00 | 21.39 +/- 0.00 47 | 9.00 | 6.98 +/- 0.00 48 | 50.00 | 63.28 +/- 0.00 49 | 135.00 | 119.52 +/- 0.00 50 | 97.00 | 114.45 +/- 0.00 51 | 96.00 | 90.32 +/- 0.00 52 | 89.00 | 82.38 +/- 0.00 53 | 79.00 | 102.39 +/- 0.00 54 | 20.00 | 20.08 +/- 0.00 55 | 18.00 | 20.65 +/- 0.00 56 | 111.00 | 119.69 +/- 0.00 57 | 90.00 | 95.51 +/- 0.00 58 | 114.00 | 99.99 +/- 0.00 59 | 111.00 | 122.96 +/- 0.00 60 | 103.00 | 107.96 +/- 0.00 61 | 119.00 | 119.31 +/- 0.00 62 | 82.00 | 69.32 +/- 0.00 63 | 59.00 | 59.20 +/- 0.00 64 | 82.00 | 78.76 +/- 0.00 65 | 115.00 | 97.72 +/- 0.00 66 | 106.00 | 91.72 +/- 0.00 67 | 50.00 | 48.97 +/- 0.00 68 | 19.00 | 24.05 +/- 0.00 69 | 94.00 | 104.04 +/- 0.00 70 | 63.00 | 112.94 +/- 0.00 71 | 97.00 | 96.91 +/- 0.00 72 | 145.00 | 118.63 +/- 0.00 73 | 83.00 | 107.80 +/- 0.00 74 | 128.00 | 99.52 +/- 0.00 75 | 10.00 | 9.43 +/- 0.00 76 | 95.00 | 77.17 +/- 0.00 77 | 21.00 | 25.11 +/- 0.00 78 | 72.00 | 70.47 +/- 0.00 79 | 115.00 | 112.60 +/- 0.00 80 | 91.00 | 93.05 +/- 0.00 81 | 54.00 | 40.89 +/- 0.00 82 | 66.00 | 105.47 +/- 0.00 83 | 8.00 | 5.31 +/- 0.00 84 | 92.00 | 95.22 +/- 0.00 85 | 47.00 | 40.28 +/- 0.00 86 | 137.00 | 123.19 +/- 0.00 87 | 7.00 | 4.12 +/- 0.00 88 | 8.00 | 6.93 +/- 0.00 89 | 118.00 | 123.67 +/- 0.00 90 | 85.00 | 54.60 +/- 0.00 91 | 107.00 | 123.67 +/- 0.00 92 | 109.00 | 117.52 +/- 0.00 93 | 121.00 | 122.09 +/- 0.00 94 | 14.00 | 21.20 +/- 0.00 95 | 113.00 | 123.48 +/- 0.00 96 | 38.00 | 30.70 +/- 0.00 97 | 100.00 | 112.45 +/- 0.00 98 | 55.00 | 81.64 +/- 0.00 99 | 136.00 | 115.22 +/- 0.00 100 | 117.00 | 119.66 +/- 0.00 101 | 28.00 | 30.13 +/- 0.00 102 | 118.00 | 115.23 +/- 0.00 103 | 97.00 | 81.86 +/- 0.00 104 | 137.00 | 116.85 +/- 0.00 105 | 50.00 | 53.95 +/- 0.00 106 | 16.00 | 14.25 +/- 0.00 107 | 21.00 | 15.49 +/- 0.00 108 | 112.00 | 121.37 +/- 0.00 109 | 34.00 | 26.19 +/- 0.00 110 | 95.00 | 105.23 +/- 0.00 111 | 131.00 | 106.29 +/- 0.00 112 | 10.00 | 8.11 +/- 0.00 113 | 107.00 | 114.14 +/- 0.00 114 | 8.00 | 7.23 +/- 0.00 115 | 29.00 | 27.34 +/- 0.00 116 | 116.00 | 121.32 +/- 0.00 117 | 15.00 | 22.09 +/- 0.00 118 | 59.00 | 62.76 +/- 0.00 119 | 98.00 | 123.01 +/- 0.00 120 | 128.00 | 122.12 +/- 0.00 121 | 20.00 | 22.18 +/- 0.00 122 | 114.00 | 98.75 +/- 0.00 123 | 28.00 | 21.88 +/- 0.00 124 | 48.00 | 52.52 +/- 0.00 125 | 87.00 | 83.22 +/- 0.00 126 | eval mean loss: 111.90 127 | eval rmse: 14.96 128 | eval mae: 10.81 129 | eval score: 492.02 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 40.44 134 | eval time: 0:00:01.515938 135 | **** end time: 2019-09-27 16:53:01.894489 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_1/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_1', model='frequentist_dense3', model_path='log/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_1/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 32082 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-09-27 16:54:12.446572 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:54:12.449726 25 | ground truth | pred +/- std: 26 | 77.00 | 121.69 +/- 0.00 27 | 57.00 | 67.98 +/- 0.00 28 | 124.00 | 93.71 +/- 0.00 29 | 90.00 | 104.87 +/- 0.00 30 | 93.00 | 117.05 +/- 0.00 31 | 37.00 | 43.23 +/- 0.00 32 | 28.00 | 21.70 +/- 0.00 33 | 91.00 | 100.50 +/- 0.00 34 | 113.00 | 95.37 +/- 0.00 35 | 58.00 | 81.99 +/- 0.00 36 | 84.00 | 83.10 +/- 0.00 37 | 20.00 | 22.31 +/- 0.00 38 | 142.00 | 123.32 +/- 0.00 39 | 126.00 | 112.64 +/- 0.00 40 | 11.00 | 11.12 +/- 0.00 41 | 114.00 | 86.76 +/- 0.00 42 | 69.00 | 40.53 +/- 0.00 43 | 26.00 | 30.10 +/- 0.00 44 | 137.00 | 120.06 +/- 0.00 45 | 28.00 | 34.89 +/- 0.00 46 | 21.00 | 18.43 +/- 0.00 47 | 9.00 | 7.35 +/- 0.00 48 | 50.00 | 55.91 +/- 0.00 49 | 135.00 | 119.85 +/- 0.00 50 | 97.00 | 115.76 +/- 0.00 51 | 96.00 | 95.20 +/- 0.00 52 | 89.00 | 100.26 +/- 0.00 53 | 79.00 | 89.24 +/- 0.00 54 | 20.00 | 24.14 +/- 0.00 55 | 18.00 | 21.62 +/- 0.00 56 | 111.00 | 121.50 +/- 0.00 57 | 90.00 | 98.73 +/- 0.00 58 | 114.00 | 110.22 +/- 0.00 59 | 111.00 | 123.17 +/- 0.00 60 | 103.00 | 100.34 +/- 0.00 61 | 119.00 | 117.52 +/- 0.00 62 | 82.00 | 67.17 +/- 0.00 63 | 59.00 | 54.71 +/- 0.00 64 | 82.00 | 84.27 +/- 0.00 65 | 115.00 | 108.08 +/- 0.00 66 | 106.00 | 100.79 +/- 0.00 67 | 50.00 | 48.28 +/- 0.00 68 | 19.00 | 21.62 +/- 0.00 69 | 94.00 | 90.79 +/- 0.00 70 | 63.00 | 105.54 +/- 0.00 71 | 97.00 | 104.63 +/- 0.00 72 | 145.00 | 118.95 +/- 0.00 73 | 83.00 | 101.62 +/- 0.00 74 | 128.00 | 105.55 +/- 0.00 75 | 10.00 | 12.07 +/- 0.00 76 | 95.00 | 71.52 +/- 0.00 77 | 21.00 | 24.47 +/- 0.00 78 | 72.00 | 87.14 +/- 0.00 79 | 115.00 | 117.24 +/- 0.00 80 | 91.00 | 94.37 +/- 0.00 81 | 54.00 | 46.56 +/- 0.00 82 | 66.00 | 101.43 +/- 0.00 83 | 8.00 | 4.12 +/- 0.00 84 | 92.00 | 88.55 +/- 0.00 85 | 47.00 | 39.03 +/- 0.00 86 | 137.00 | 122.85 +/- 0.00 87 | 7.00 | 3.69 +/- 0.00 88 | 8.00 | 7.60 +/- 0.00 89 | 118.00 | 123.28 +/- 0.00 90 | 85.00 | 56.09 +/- 0.00 91 | 107.00 | 123.35 +/- 0.00 92 | 109.00 | 115.98 +/- 0.00 93 | 121.00 | 121.89 +/- 0.00 94 | 14.00 | 19.39 +/- 0.00 95 | 113.00 | 123.16 +/- 0.00 96 | 38.00 | 26.11 +/- 0.00 97 | 100.00 | 114.42 +/- 0.00 98 | 55.00 | 77.26 +/- 0.00 99 | 136.00 | 117.37 +/- 0.00 100 | 117.00 | 121.07 +/- 0.00 101 | 28.00 | 27.60 +/- 0.00 102 | 118.00 | 111.77 +/- 0.00 103 | 97.00 | 72.08 +/- 0.00 104 | 137.00 | 112.53 +/- 0.00 105 | 50.00 | 54.99 +/- 0.00 106 | 16.00 | 13.57 +/- 0.00 107 | 21.00 | 16.76 +/- 0.00 108 | 112.00 | 121.06 +/- 0.00 109 | 34.00 | 25.92 +/- 0.00 110 | 95.00 | 101.31 +/- 0.00 111 | 131.00 | 106.43 +/- 0.00 112 | 10.00 | 9.19 +/- 0.00 113 | 107.00 | 117.35 +/- 0.00 114 | 8.00 | 5.55 +/- 0.00 115 | 29.00 | 35.27 +/- 0.00 116 | 116.00 | 122.38 +/- 0.00 117 | 15.00 | 23.14 +/- 0.00 118 | 59.00 | 67.88 +/- 0.00 119 | 98.00 | 123.21 +/- 0.00 120 | 128.00 | 122.90 +/- 0.00 121 | 20.00 | 21.38 +/- 0.00 122 | 114.00 | 107.50 +/- 0.00 123 | 28.00 | 19.87 +/- 0.00 124 | 48.00 | 62.88 +/- 0.00 125 | 87.00 | 92.99 +/- 0.00 126 | eval mean loss: 103.62 127 | eval rmse: 14.40 128 | eval mae: 10.77 129 | eval score: 391.24 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 40.72 134 | eval time: 0:00:01.561613 135 | **** end time: 2019-09-27 16:54:14.011592 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_2/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_2', model='frequentist_dense3', model_path='log/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_2/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 32132 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-09-27 16:55:24.248068 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:55:24.251180 25 | ground truth | pred +/- std: 26 | 77.00 | 122.27 +/- 0.00 27 | 57.00 | 62.09 +/- 0.00 28 | 124.00 | 91.48 +/- 0.00 29 | 90.00 | 108.50 +/- 0.00 30 | 93.00 | 114.16 +/- 0.00 31 | 37.00 | 41.45 +/- 0.00 32 | 28.00 | 28.57 +/- 0.00 33 | 91.00 | 106.53 +/- 0.00 34 | 113.00 | 96.47 +/- 0.00 35 | 58.00 | 79.24 +/- 0.00 36 | 84.00 | 77.91 +/- 0.00 37 | 20.00 | 18.19 +/- 0.00 38 | 142.00 | 123.82 +/- 0.00 39 | 126.00 | 113.05 +/- 0.00 40 | 11.00 | 11.02 +/- 0.00 41 | 114.00 | 82.65 +/- 0.00 42 | 69.00 | 48.48 +/- 0.00 43 | 26.00 | 25.28 +/- 0.00 44 | 137.00 | 115.94 +/- 0.00 45 | 28.00 | 30.89 +/- 0.00 46 | 21.00 | 20.02 +/- 0.00 47 | 9.00 | 8.62 +/- 0.00 48 | 50.00 | 61.78 +/- 0.00 49 | 135.00 | 122.41 +/- 0.00 50 | 97.00 | 112.52 +/- 0.00 51 | 96.00 | 91.51 +/- 0.00 52 | 89.00 | 86.75 +/- 0.00 53 | 79.00 | 93.60 +/- 0.00 54 | 20.00 | 18.45 +/- 0.00 55 | 18.00 | 16.34 +/- 0.00 56 | 111.00 | 121.07 +/- 0.00 57 | 90.00 | 95.26 +/- 0.00 58 | 114.00 | 107.18 +/- 0.00 59 | 111.00 | 123.77 +/- 0.00 60 | 103.00 | 105.21 +/- 0.00 61 | 119.00 | 117.80 +/- 0.00 62 | 82.00 | 62.72 +/- 0.00 63 | 59.00 | 59.18 +/- 0.00 64 | 82.00 | 83.48 +/- 0.00 65 | 115.00 | 107.56 +/- 0.00 66 | 106.00 | 107.73 +/- 0.00 67 | 50.00 | 47.63 +/- 0.00 68 | 19.00 | 22.43 +/- 0.00 69 | 94.00 | 90.83 +/- 0.00 70 | 63.00 | 108.78 +/- 0.00 71 | 97.00 | 101.63 +/- 0.00 72 | 145.00 | 117.82 +/- 0.00 73 | 83.00 | 102.03 +/- 0.00 74 | 128.00 | 102.98 +/- 0.00 75 | 10.00 | 9.03 +/- 0.00 76 | 95.00 | 73.32 +/- 0.00 77 | 21.00 | 19.97 +/- 0.00 78 | 72.00 | 86.18 +/- 0.00 79 | 115.00 | 117.85 +/- 0.00 80 | 91.00 | 98.77 +/- 0.00 81 | 54.00 | 43.91 +/- 0.00 82 | 66.00 | 105.02 +/- 0.00 83 | 8.00 | 6.42 +/- 0.00 84 | 92.00 | 91.76 +/- 0.00 85 | 47.00 | 38.44 +/- 0.00 86 | 137.00 | 123.05 +/- 0.00 87 | 7.00 | 5.47 +/- 0.00 88 | 8.00 | 11.02 +/- 0.00 89 | 118.00 | 124.56 +/- 0.00 90 | 85.00 | 53.43 +/- 0.00 91 | 107.00 | 124.26 +/- 0.00 92 | 109.00 | 117.01 +/- 0.00 93 | 121.00 | 120.93 +/- 0.00 94 | 14.00 | 16.79 +/- 0.00 95 | 113.00 | 123.76 +/- 0.00 96 | 38.00 | 29.71 +/- 0.00 97 | 100.00 | 109.12 +/- 0.00 98 | 55.00 | 68.91 +/- 0.00 99 | 136.00 | 113.35 +/- 0.00 100 | 117.00 | 118.25 +/- 0.00 101 | 28.00 | 29.29 +/- 0.00 102 | 118.00 | 110.48 +/- 0.00 103 | 97.00 | 70.29 +/- 0.00 104 | 137.00 | 116.05 +/- 0.00 105 | 50.00 | 52.30 +/- 0.00 106 | 16.00 | 16.30 +/- 0.00 107 | 21.00 | 14.44 +/- 0.00 108 | 112.00 | 121.09 +/- 0.00 109 | 34.00 | 23.96 +/- 0.00 110 | 95.00 | 102.73 +/- 0.00 111 | 131.00 | 104.34 +/- 0.00 112 | 10.00 | 8.53 +/- 0.00 113 | 107.00 | 112.98 +/- 0.00 114 | 8.00 | 8.24 +/- 0.00 115 | 29.00 | 31.87 +/- 0.00 116 | 116.00 | 122.30 +/- 0.00 117 | 15.00 | 22.06 +/- 0.00 118 | 59.00 | 65.90 +/- 0.00 119 | 98.00 | 123.93 +/- 0.00 120 | 128.00 | 122.12 +/- 0.00 121 | 20.00 | 25.40 +/- 0.00 122 | 114.00 | 100.81 +/- 0.00 123 | 28.00 | 19.87 +/- 0.00 124 | 48.00 | 61.41 +/- 0.00 125 | 87.00 | 89.37 +/- 0.00 126 | eval mean loss: 106.62 127 | eval rmse: 14.60 128 | eval mae: 10.44 129 | eval score: 431.79 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 40.67 134 | eval time: 0:00:01.530156 135 | **** end time: 2019-09-27 16:55:25.781601 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_3/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_3', model='frequentist_dense3', model_path='log/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_3/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 32185 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-09-27 16:56:36.638661 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:56:36.641862 25 | ground truth | pred +/- std: 26 | 77.00 | 122.48 +/- 0.00 27 | 57.00 | 66.49 +/- 0.00 28 | 124.00 | 87.74 +/- 0.00 29 | 90.00 | 107.81 +/- 0.00 30 | 93.00 | 116.12 +/- 0.00 31 | 37.00 | 41.96 +/- 0.00 32 | 28.00 | 27.69 +/- 0.00 33 | 91.00 | 106.09 +/- 0.00 34 | 113.00 | 98.87 +/- 0.00 35 | 58.00 | 82.12 +/- 0.00 36 | 84.00 | 75.72 +/- 0.00 37 | 20.00 | 17.98 +/- 0.00 38 | 142.00 | 123.51 +/- 0.00 39 | 126.00 | 113.19 +/- 0.00 40 | 11.00 | 11.53 +/- 0.00 41 | 114.00 | 81.45 +/- 0.00 42 | 69.00 | 49.38 +/- 0.00 43 | 26.00 | 30.23 +/- 0.00 44 | 137.00 | 118.70 +/- 0.00 45 | 28.00 | 30.73 +/- 0.00 46 | 21.00 | 21.31 +/- 0.00 47 | 9.00 | 6.66 +/- 0.00 48 | 50.00 | 54.60 +/- 0.00 49 | 135.00 | 119.81 +/- 0.00 50 | 97.00 | 109.84 +/- 0.00 51 | 96.00 | 90.05 +/- 0.00 52 | 89.00 | 78.32 +/- 0.00 53 | 79.00 | 91.69 +/- 0.00 54 | 20.00 | 23.75 +/- 0.00 55 | 18.00 | 21.56 +/- 0.00 56 | 111.00 | 121.44 +/- 0.00 57 | 90.00 | 96.81 +/- 0.00 58 | 114.00 | 106.70 +/- 0.00 59 | 111.00 | 121.97 +/- 0.00 60 | 103.00 | 108.64 +/- 0.00 61 | 119.00 | 117.66 +/- 0.00 62 | 82.00 | 64.86 +/- 0.00 63 | 59.00 | 58.68 +/- 0.00 64 | 82.00 | 85.09 +/- 0.00 65 | 115.00 | 104.33 +/- 0.00 66 | 106.00 | 96.34 +/- 0.00 67 | 50.00 | 48.56 +/- 0.00 68 | 19.00 | 23.26 +/- 0.00 69 | 94.00 | 97.17 +/- 0.00 70 | 63.00 | 109.54 +/- 0.00 71 | 97.00 | 103.26 +/- 0.00 72 | 145.00 | 118.75 +/- 0.00 73 | 83.00 | 100.18 +/- 0.00 74 | 128.00 | 97.88 +/- 0.00 75 | 10.00 | 10.83 +/- 0.00 76 | 95.00 | 80.12 +/- 0.00 77 | 21.00 | 25.22 +/- 0.00 78 | 72.00 | 84.80 +/- 0.00 79 | 115.00 | 99.77 +/- 0.00 80 | 91.00 | 101.60 +/- 0.00 81 | 54.00 | 44.69 +/- 0.00 82 | 66.00 | 98.24 +/- 0.00 83 | 8.00 | 5.21 +/- 0.00 84 | 92.00 | 93.77 +/- 0.00 85 | 47.00 | 40.04 +/- 0.00 86 | 137.00 | 122.50 +/- 0.00 87 | 7.00 | 3.58 +/- 0.00 88 | 8.00 | 7.02 +/- 0.00 89 | 118.00 | 123.55 +/- 0.00 90 | 85.00 | 56.52 +/- 0.00 91 | 107.00 | 123.57 +/- 0.00 92 | 109.00 | 116.84 +/- 0.00 93 | 121.00 | 122.22 +/- 0.00 94 | 14.00 | 17.34 +/- 0.00 95 | 113.00 | 123.49 +/- 0.00 96 | 38.00 | 24.74 +/- 0.00 97 | 100.00 | 109.17 +/- 0.00 98 | 55.00 | 72.38 +/- 0.00 99 | 136.00 | 112.24 +/- 0.00 100 | 117.00 | 120.34 +/- 0.00 101 | 28.00 | 28.71 +/- 0.00 102 | 118.00 | 109.99 +/- 0.00 103 | 97.00 | 62.85 +/- 0.00 104 | 137.00 | 112.00 +/- 0.00 105 | 50.00 | 50.79 +/- 0.00 106 | 16.00 | 13.50 +/- 0.00 107 | 21.00 | 14.25 +/- 0.00 108 | 112.00 | 121.39 +/- 0.00 109 | 34.00 | 26.58 +/- 0.00 110 | 95.00 | 99.79 +/- 0.00 111 | 131.00 | 105.93 +/- 0.00 112 | 10.00 | 7.93 +/- 0.00 113 | 107.00 | 99.38 +/- 0.00 114 | 8.00 | 6.02 +/- 0.00 115 | 29.00 | 29.85 +/- 0.00 116 | 116.00 | 122.16 +/- 0.00 117 | 15.00 | 20.59 +/- 0.00 118 | 59.00 | 67.15 +/- 0.00 119 | 98.00 | 122.71 +/- 0.00 120 | 128.00 | 123.25 +/- 0.00 121 | 20.00 | 23.87 +/- 0.00 122 | 114.00 | 98.74 +/- 0.00 123 | 28.00 | 20.96 +/- 0.00 124 | 48.00 | 59.01 +/- 0.00 125 | 87.00 | 88.41 +/- 0.00 126 | eval mean loss: 110.28 127 | eval rmse: 14.85 128 | eval mae: 10.97 129 | eval score: 432.91 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 40.20 134 | eval time: 0:00:01.571201 135 | **** end time: 2019-09-27 16:56:38.213309 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_4/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_4', model='frequentist_dense3', model_path='log/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_4/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 32237 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-09-27 16:57:49.138154 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:57:49.141458 25 | ground truth | pred +/- std: 26 | 77.00 | 121.80 +/- 0.00 27 | 57.00 | 62.63 +/- 0.00 28 | 124.00 | 89.02 +/- 0.00 29 | 90.00 | 106.70 +/- 0.00 30 | 93.00 | 112.80 +/- 0.00 31 | 37.00 | 39.41 +/- 0.00 32 | 28.00 | 24.82 +/- 0.00 33 | 91.00 | 101.77 +/- 0.00 34 | 113.00 | 95.06 +/- 0.00 35 | 58.00 | 77.44 +/- 0.00 36 | 84.00 | 87.88 +/- 0.00 37 | 20.00 | 19.78 +/- 0.00 38 | 142.00 | 124.37 +/- 0.00 39 | 126.00 | 114.10 +/- 0.00 40 | 11.00 | 11.09 +/- 0.00 41 | 114.00 | 84.64 +/- 0.00 42 | 69.00 | 44.31 +/- 0.00 43 | 26.00 | 24.52 +/- 0.00 44 | 137.00 | 115.94 +/- 0.00 45 | 28.00 | 29.29 +/- 0.00 46 | 21.00 | 19.13 +/- 0.00 47 | 9.00 | 6.84 +/- 0.00 48 | 50.00 | 60.46 +/- 0.00 49 | 135.00 | 120.11 +/- 0.00 50 | 97.00 | 115.93 +/- 0.00 51 | 96.00 | 91.03 +/- 0.00 52 | 89.00 | 92.83 +/- 0.00 53 | 79.00 | 94.72 +/- 0.00 54 | 20.00 | 19.56 +/- 0.00 55 | 18.00 | 23.01 +/- 0.00 56 | 111.00 | 116.95 +/- 0.00 57 | 90.00 | 93.87 +/- 0.00 58 | 114.00 | 108.96 +/- 0.00 59 | 111.00 | 123.72 +/- 0.00 60 | 103.00 | 98.53 +/- 0.00 61 | 119.00 | 118.77 +/- 0.00 62 | 82.00 | 63.36 +/- 0.00 63 | 59.00 | 58.51 +/- 0.00 64 | 82.00 | 83.67 +/- 0.00 65 | 115.00 | 99.48 +/- 0.00 66 | 106.00 | 105.87 +/- 0.00 67 | 50.00 | 41.47 +/- 0.00 68 | 19.00 | 21.46 +/- 0.00 69 | 94.00 | 96.08 +/- 0.00 70 | 63.00 | 110.14 +/- 0.00 71 | 97.00 | 97.03 +/- 0.00 72 | 145.00 | 117.85 +/- 0.00 73 | 83.00 | 107.00 +/- 0.00 74 | 128.00 | 99.57 +/- 0.00 75 | 10.00 | 11.52 +/- 0.00 76 | 95.00 | 77.96 +/- 0.00 77 | 21.00 | 23.04 +/- 0.00 78 | 72.00 | 77.10 +/- 0.00 79 | 115.00 | 112.65 +/- 0.00 80 | 91.00 | 101.03 +/- 0.00 81 | 54.00 | 48.63 +/- 0.00 82 | 66.00 | 101.52 +/- 0.00 83 | 8.00 | 4.65 +/- 0.00 84 | 92.00 | 99.15 +/- 0.00 85 | 47.00 | 40.68 +/- 0.00 86 | 137.00 | 122.75 +/- 0.00 87 | 7.00 | 4.22 +/- 0.00 88 | 8.00 | 7.13 +/- 0.00 89 | 118.00 | 124.29 +/- 0.00 90 | 85.00 | 51.83 +/- 0.00 91 | 107.00 | 123.82 +/- 0.00 92 | 109.00 | 112.21 +/- 0.00 93 | 121.00 | 121.77 +/- 0.00 94 | 14.00 | 19.11 +/- 0.00 95 | 113.00 | 123.90 +/- 0.00 96 | 38.00 | 23.32 +/- 0.00 97 | 100.00 | 112.63 +/- 0.00 98 | 55.00 | 73.37 +/- 0.00 99 | 136.00 | 116.31 +/- 0.00 100 | 117.00 | 118.30 +/- 0.00 101 | 28.00 | 29.13 +/- 0.00 102 | 118.00 | 107.88 +/- 0.00 103 | 97.00 | 68.52 +/- 0.00 104 | 137.00 | 118.43 +/- 0.00 105 | 50.00 | 50.89 +/- 0.00 106 | 16.00 | 15.02 +/- 0.00 107 | 21.00 | 18.22 +/- 0.00 108 | 112.00 | 121.42 +/- 0.00 109 | 34.00 | 28.97 +/- 0.00 110 | 95.00 | 98.14 +/- 0.00 111 | 131.00 | 104.46 +/- 0.00 112 | 10.00 | 8.48 +/- 0.00 113 | 107.00 | 113.53 +/- 0.00 114 | 8.00 | 6.26 +/- 0.00 115 | 29.00 | 23.65 +/- 0.00 116 | 116.00 | 121.90 +/- 0.00 117 | 15.00 | 16.95 +/- 0.00 118 | 59.00 | 69.29 +/- 0.00 119 | 98.00 | 123.71 +/- 0.00 120 | 128.00 | 122.82 +/- 0.00 121 | 20.00 | 23.86 +/- 0.00 122 | 114.00 | 103.71 +/- 0.00 123 | 28.00 | 20.60 +/- 0.00 124 | 48.00 | 51.32 +/- 0.00 125 | 87.00 | 90.77 +/- 0.00 126 | eval mean loss: 106.79 127 | eval rmse: 14.61 128 | eval mae: 10.31 129 | eval score: 430.41 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 40.81 134 | eval time: 0:00:01.584882 135 | **** end time: 2019-09-27 16:57:50.726593 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_5/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_5', model='frequentist_dense3', model_path='log/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_5/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 32291 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-09-27 16:59:01.657259 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 16:59:01.660458 25 | ground truth | pred +/- std: 26 | 77.00 | 122.48 +/- 0.00 27 | 57.00 | 69.85 +/- 0.00 28 | 124.00 | 89.70 +/- 0.00 29 | 90.00 | 107.92 +/- 0.00 30 | 93.00 | 112.21 +/- 0.00 31 | 37.00 | 43.45 +/- 0.00 32 | 28.00 | 26.44 +/- 0.00 33 | 91.00 | 100.07 +/- 0.00 34 | 113.00 | 97.31 +/- 0.00 35 | 58.00 | 77.77 +/- 0.00 36 | 84.00 | 79.96 +/- 0.00 37 | 20.00 | 15.58 +/- 0.00 38 | 142.00 | 123.83 +/- 0.00 39 | 126.00 | 113.67 +/- 0.00 40 | 11.00 | 10.80 +/- 0.00 41 | 114.00 | 84.34 +/- 0.00 42 | 69.00 | 49.97 +/- 0.00 43 | 26.00 | 32.89 +/- 0.00 44 | 137.00 | 118.08 +/- 0.00 45 | 28.00 | 35.30 +/- 0.00 46 | 21.00 | 21.03 +/- 0.00 47 | 9.00 | 8.88 +/- 0.00 48 | 50.00 | 57.77 +/- 0.00 49 | 135.00 | 121.91 +/- 0.00 50 | 97.00 | 113.56 +/- 0.00 51 | 96.00 | 91.66 +/- 0.00 52 | 89.00 | 79.91 +/- 0.00 53 | 79.00 | 101.30 +/- 0.00 54 | 20.00 | 21.41 +/- 0.00 55 | 18.00 | 22.16 +/- 0.00 56 | 111.00 | 121.59 +/- 0.00 57 | 90.00 | 94.74 +/- 0.00 58 | 114.00 | 108.87 +/- 0.00 59 | 111.00 | 123.18 +/- 0.00 60 | 103.00 | 80.31 +/- 0.00 61 | 119.00 | 120.40 +/- 0.00 62 | 82.00 | 65.41 +/- 0.00 63 | 59.00 | 63.26 +/- 0.00 64 | 82.00 | 84.62 +/- 0.00 65 | 115.00 | 108.74 +/- 0.00 66 | 106.00 | 92.26 +/- 0.00 67 | 50.00 | 47.77 +/- 0.00 68 | 19.00 | 25.55 +/- 0.00 69 | 94.00 | 103.36 +/- 0.00 70 | 63.00 | 111.83 +/- 0.00 71 | 97.00 | 94.48 +/- 0.00 72 | 145.00 | 116.10 +/- 0.00 73 | 83.00 | 104.20 +/- 0.00 74 | 128.00 | 99.06 +/- 0.00 75 | 10.00 | 11.56 +/- 0.00 76 | 95.00 | 77.63 +/- 0.00 77 | 21.00 | 26.24 +/- 0.00 78 | 72.00 | 63.84 +/- 0.00 79 | 115.00 | 119.42 +/- 0.00 80 | 91.00 | 98.61 +/- 0.00 81 | 54.00 | 39.63 +/- 0.00 82 | 66.00 | 95.05 +/- 0.00 83 | 8.00 | 5.46 +/- 0.00 84 | 92.00 | 99.42 +/- 0.00 85 | 47.00 | 41.96 +/- 0.00 86 | 137.00 | 123.22 +/- 0.00 87 | 7.00 | 3.91 +/- 0.00 88 | 8.00 | 7.82 +/- 0.00 89 | 118.00 | 123.90 +/- 0.00 90 | 85.00 | 52.51 +/- 0.00 91 | 107.00 | 123.93 +/- 0.00 92 | 109.00 | 119.87 +/- 0.00 93 | 121.00 | 121.12 +/- 0.00 94 | 14.00 | 18.80 +/- 0.00 95 | 113.00 | 123.71 +/- 0.00 96 | 38.00 | 29.92 +/- 0.00 97 | 100.00 | 110.17 +/- 0.00 98 | 55.00 | 87.65 +/- 0.00 99 | 136.00 | 113.91 +/- 0.00 100 | 117.00 | 119.27 +/- 0.00 101 | 28.00 | 28.26 +/- 0.00 102 | 118.00 | 112.17 +/- 0.00 103 | 97.00 | 69.82 +/- 0.00 104 | 137.00 | 114.69 +/- 0.00 105 | 50.00 | 53.94 +/- 0.00 106 | 16.00 | 14.55 +/- 0.00 107 | 21.00 | 17.70 +/- 0.00 108 | 112.00 | 123.50 +/- 0.00 109 | 34.00 | 27.79 +/- 0.00 110 | 95.00 | 98.67 +/- 0.00 111 | 131.00 | 111.84 +/- 0.00 112 | 10.00 | 10.06 +/- 0.00 113 | 107.00 | 119.26 +/- 0.00 114 | 8.00 | 8.44 +/- 0.00 115 | 29.00 | 31.49 +/- 0.00 116 | 116.00 | 121.75 +/- 0.00 117 | 15.00 | 25.19 +/- 0.00 118 | 59.00 | 71.37 +/- 0.00 119 | 98.00 | 123.41 +/- 0.00 120 | 128.00 | 122.98 +/- 0.00 121 | 20.00 | 22.36 +/- 0.00 122 | 114.00 | 97.65 +/- 0.00 123 | 28.00 | 21.50 +/- 0.00 124 | 48.00 | 57.99 +/- 0.00 125 | 87.00 | 85.47 +/- 0.00 126 | eval mean loss: 113.84 127 | eval rmse: 15.09 128 | eval mae: 11.23 129 | eval score: 473.07 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 40.24 134 | eval time: 0:00:01.527882 135 | **** end time: 2019-09-27 16:59:03.188596 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_6/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_6', model='frequentist_dense3', model_path='log/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_6/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 32344 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-09-27 17:00:13.511656 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 17:00:13.514661 25 | ground truth | pred +/- std: 26 | 77.00 | 121.84 +/- 0.00 27 | 57.00 | 59.97 +/- 0.00 28 | 124.00 | 87.28 +/- 0.00 29 | 90.00 | 111.92 +/- 0.00 30 | 93.00 | 114.20 +/- 0.00 31 | 37.00 | 41.53 +/- 0.00 32 | 28.00 | 20.69 +/- 0.00 33 | 91.00 | 93.55 +/- 0.00 34 | 113.00 | 97.85 +/- 0.00 35 | 58.00 | 70.40 +/- 0.00 36 | 84.00 | 79.51 +/- 0.00 37 | 20.00 | 18.06 +/- 0.00 38 | 142.00 | 123.92 +/- 0.00 39 | 126.00 | 111.09 +/- 0.00 40 | 11.00 | 12.68 +/- 0.00 41 | 114.00 | 79.87 +/- 0.00 42 | 69.00 | 45.86 +/- 0.00 43 | 26.00 | 25.62 +/- 0.00 44 | 137.00 | 120.02 +/- 0.00 45 | 28.00 | 34.92 +/- 0.00 46 | 21.00 | 21.59 +/- 0.00 47 | 9.00 | 9.00 +/- 0.00 48 | 50.00 | 60.19 +/- 0.00 49 | 135.00 | 122.96 +/- 0.00 50 | 97.00 | 109.63 +/- 0.00 51 | 96.00 | 77.43 +/- 0.00 52 | 89.00 | 85.29 +/- 0.00 53 | 79.00 | 94.82 +/- 0.00 54 | 20.00 | 20.79 +/- 0.00 55 | 18.00 | 24.50 +/- 0.00 56 | 111.00 | 116.04 +/- 0.00 57 | 90.00 | 91.40 +/- 0.00 58 | 114.00 | 115.31 +/- 0.00 59 | 111.00 | 123.75 +/- 0.00 60 | 103.00 | 92.04 +/- 0.00 61 | 119.00 | 115.49 +/- 0.00 62 | 82.00 | 57.65 +/- 0.00 63 | 59.00 | 61.23 +/- 0.00 64 | 82.00 | 89.18 +/- 0.00 65 | 115.00 | 104.29 +/- 0.00 66 | 106.00 | 93.72 +/- 0.00 67 | 50.00 | 48.82 +/- 0.00 68 | 19.00 | 24.04 +/- 0.00 69 | 94.00 | 102.46 +/- 0.00 70 | 63.00 | 113.53 +/- 0.00 71 | 97.00 | 94.86 +/- 0.00 72 | 145.00 | 117.36 +/- 0.00 73 | 83.00 | 99.75 +/- 0.00 74 | 128.00 | 101.85 +/- 0.00 75 | 10.00 | 11.59 +/- 0.00 76 | 95.00 | 74.31 +/- 0.00 77 | 21.00 | 26.06 +/- 0.00 78 | 72.00 | 91.65 +/- 0.00 79 | 115.00 | 119.00 +/- 0.00 80 | 91.00 | 102.30 +/- 0.00 81 | 54.00 | 45.40 +/- 0.00 82 | 66.00 | 97.91 +/- 0.00 83 | 8.00 | 3.83 +/- 0.00 84 | 92.00 | 101.72 +/- 0.00 85 | 47.00 | 35.85 +/- 0.00 86 | 137.00 | 123.56 +/- 0.00 87 | 7.00 | 3.95 +/- 0.00 88 | 8.00 | 8.09 +/- 0.00 89 | 118.00 | 123.49 +/- 0.00 90 | 85.00 | 51.01 +/- 0.00 91 | 107.00 | 123.67 +/- 0.00 92 | 109.00 | 119.82 +/- 0.00 93 | 121.00 | 120.30 +/- 0.00 94 | 14.00 | 20.64 +/- 0.00 95 | 113.00 | 123.54 +/- 0.00 96 | 38.00 | 28.01 +/- 0.00 97 | 100.00 | 110.11 +/- 0.00 98 | 55.00 | 73.06 +/- 0.00 99 | 136.00 | 111.80 +/- 0.00 100 | 117.00 | 116.81 +/- 0.00 101 | 28.00 | 32.71 +/- 0.00 102 | 118.00 | 107.39 +/- 0.00 103 | 97.00 | 75.53 +/- 0.00 104 | 137.00 | 117.36 +/- 0.00 105 | 50.00 | 61.12 +/- 0.00 106 | 16.00 | 14.13 +/- 0.00 107 | 21.00 | 14.79 +/- 0.00 108 | 112.00 | 121.66 +/- 0.00 109 | 34.00 | 22.74 +/- 0.00 110 | 95.00 | 96.42 +/- 0.00 111 | 131.00 | 107.30 +/- 0.00 112 | 10.00 | 10.42 +/- 0.00 113 | 107.00 | 117.56 +/- 0.00 114 | 8.00 | 7.53 +/- 0.00 115 | 29.00 | 34.29 +/- 0.00 116 | 116.00 | 121.14 +/- 0.00 117 | 15.00 | 23.18 +/- 0.00 118 | 59.00 | 78.52 +/- 0.00 119 | 98.00 | 123.63 +/- 0.00 120 | 128.00 | 123.23 +/- 0.00 121 | 20.00 | 21.95 +/- 0.00 122 | 114.00 | 98.49 +/- 0.00 123 | 28.00 | 23.01 +/- 0.00 124 | 48.00 | 54.07 +/- 0.00 125 | 87.00 | 82.22 +/- 0.00 126 | eval mean loss: 114.83 127 | eval rmse: 15.15 128 | eval mae: 11.29 129 | eval score: 484.99 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 40.25 134 | eval time: 0:00:01.528948 135 | **** end time: 2019-09-27 17:00:15.043853 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_7/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_7', model='frequentist_dense3', model_path='log/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_7/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 32384 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-09-27 17:01:25.593291 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 17:01:25.596486 25 | ground truth | pred +/- std: 26 | 77.00 | 122.53 +/- 0.00 27 | 57.00 | 61.83 +/- 0.00 28 | 124.00 | 92.33 +/- 0.00 29 | 90.00 | 103.74 +/- 0.00 30 | 93.00 | 112.95 +/- 0.00 31 | 37.00 | 41.20 +/- 0.00 32 | 28.00 | 25.11 +/- 0.00 33 | 91.00 | 105.55 +/- 0.00 34 | 113.00 | 100.34 +/- 0.00 35 | 58.00 | 83.42 +/- 0.00 36 | 84.00 | 88.07 +/- 0.00 37 | 20.00 | 18.29 +/- 0.00 38 | 142.00 | 123.96 +/- 0.00 39 | 126.00 | 111.18 +/- 0.00 40 | 11.00 | 9.07 +/- 0.00 41 | 114.00 | 87.49 +/- 0.00 42 | 69.00 | 45.21 +/- 0.00 43 | 26.00 | 30.33 +/- 0.00 44 | 137.00 | 116.80 +/- 0.00 45 | 28.00 | 28.68 +/- 0.00 46 | 21.00 | 19.33 +/- 0.00 47 | 9.00 | 10.19 +/- 0.00 48 | 50.00 | 65.40 +/- 0.00 49 | 135.00 | 119.25 +/- 0.00 50 | 97.00 | 117.37 +/- 0.00 51 | 96.00 | 87.83 +/- 0.00 52 | 89.00 | 78.59 +/- 0.00 53 | 79.00 | 97.63 +/- 0.00 54 | 20.00 | 21.57 +/- 0.00 55 | 18.00 | 29.30 +/- 0.00 56 | 111.00 | 117.70 +/- 0.00 57 | 90.00 | 96.29 +/- 0.00 58 | 114.00 | 108.99 +/- 0.00 59 | 111.00 | 123.19 +/- 0.00 60 | 103.00 | 105.95 +/- 0.00 61 | 119.00 | 118.49 +/- 0.00 62 | 82.00 | 58.92 +/- 0.00 63 | 59.00 | 55.45 +/- 0.00 64 | 82.00 | 79.56 +/- 0.00 65 | 115.00 | 103.40 +/- 0.00 66 | 106.00 | 107.09 +/- 0.00 67 | 50.00 | 52.17 +/- 0.00 68 | 19.00 | 19.60 +/- 0.00 69 | 94.00 | 95.58 +/- 0.00 70 | 63.00 | 107.06 +/- 0.00 71 | 97.00 | 98.29 +/- 0.00 72 | 145.00 | 117.49 +/- 0.00 73 | 83.00 | 110.57 +/- 0.00 74 | 128.00 | 93.26 +/- 0.00 75 | 10.00 | 11.30 +/- 0.00 76 | 95.00 | 82.72 +/- 0.00 77 | 21.00 | 32.64 +/- 0.00 78 | 72.00 | 82.08 +/- 0.00 79 | 115.00 | 116.98 +/- 0.00 80 | 91.00 | 104.68 +/- 0.00 81 | 54.00 | 42.40 +/- 0.00 82 | 66.00 | 105.85 +/- 0.00 83 | 8.00 | 4.58 +/- 0.00 84 | 92.00 | 94.71 +/- 0.00 85 | 47.00 | 41.61 +/- 0.00 86 | 137.00 | 123.05 +/- 0.00 87 | 7.00 | 4.33 +/- 0.00 88 | 8.00 | 6.65 +/- 0.00 89 | 118.00 | 123.82 +/- 0.00 90 | 85.00 | 53.74 +/- 0.00 91 | 107.00 | 124.03 +/- 0.00 92 | 109.00 | 113.94 +/- 0.00 93 | 121.00 | 120.39 +/- 0.00 94 | 14.00 | 18.26 +/- 0.00 95 | 113.00 | 123.40 +/- 0.00 96 | 38.00 | 29.20 +/- 0.00 97 | 100.00 | 110.67 +/- 0.00 98 | 55.00 | 81.47 +/- 0.00 99 | 136.00 | 112.96 +/- 0.00 100 | 117.00 | 117.86 +/- 0.00 101 | 28.00 | 25.69 +/- 0.00 102 | 118.00 | 110.74 +/- 0.00 103 | 97.00 | 67.20 +/- 0.00 104 | 137.00 | 116.57 +/- 0.00 105 | 50.00 | 53.99 +/- 0.00 106 | 16.00 | 16.57 +/- 0.00 107 | 21.00 | 15.20 +/- 0.00 108 | 112.00 | 121.11 +/- 0.00 109 | 34.00 | 29.82 +/- 0.00 110 | 95.00 | 103.05 +/- 0.00 111 | 131.00 | 102.13 +/- 0.00 112 | 10.00 | 7.04 +/- 0.00 113 | 107.00 | 113.66 +/- 0.00 114 | 8.00 | 5.08 +/- 0.00 115 | 29.00 | 30.53 +/- 0.00 116 | 116.00 | 120.34 +/- 0.00 117 | 15.00 | 21.54 +/- 0.00 118 | 59.00 | 63.45 +/- 0.00 119 | 98.00 | 123.49 +/- 0.00 120 | 128.00 | 123.24 +/- 0.00 121 | 20.00 | 21.84 +/- 0.00 122 | 114.00 | 94.82 +/- 0.00 123 | 28.00 | 22.63 +/- 0.00 124 | 48.00 | 58.93 +/- 0.00 125 | 87.00 | 86.63 +/- 0.00 126 | eval mean loss: 116.80 127 | eval rmse: 15.28 128 | eval mae: 11.05 129 | eval score: 459.56 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 40.41 134 | eval time: 0:00:01.558885 135 | **** end time: 2019-09-27 17:01:27.155628 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_8/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_8', model='frequentist_dense3', model_path='log/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_8/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 32435 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-09-27 17:02:37.338344 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 17:02:37.341361 25 | ground truth | pred +/- std: 26 | 77.00 | 121.09 +/- 0.00 27 | 57.00 | 66.17 +/- 0.00 28 | 124.00 | 98.41 +/- 0.00 29 | 90.00 | 106.37 +/- 0.00 30 | 93.00 | 113.36 +/- 0.00 31 | 37.00 | 41.58 +/- 0.00 32 | 28.00 | 28.58 +/- 0.00 33 | 91.00 | 105.56 +/- 0.00 34 | 113.00 | 99.39 +/- 0.00 35 | 58.00 | 83.01 +/- 0.00 36 | 84.00 | 80.86 +/- 0.00 37 | 20.00 | 24.74 +/- 0.00 38 | 142.00 | 124.16 +/- 0.00 39 | 126.00 | 105.63 +/- 0.00 40 | 11.00 | 10.83 +/- 0.00 41 | 114.00 | 86.57 +/- 0.00 42 | 69.00 | 49.68 +/- 0.00 43 | 26.00 | 28.24 +/- 0.00 44 | 137.00 | 117.07 +/- 0.00 45 | 28.00 | 32.12 +/- 0.00 46 | 21.00 | 19.65 +/- 0.00 47 | 9.00 | 8.70 +/- 0.00 48 | 50.00 | 55.96 +/- 0.00 49 | 135.00 | 122.68 +/- 0.00 50 | 97.00 | 113.19 +/- 0.00 51 | 96.00 | 82.81 +/- 0.00 52 | 89.00 | 92.78 +/- 0.00 53 | 79.00 | 95.94 +/- 0.00 54 | 20.00 | 14.75 +/- 0.00 55 | 18.00 | 27.40 +/- 0.00 56 | 111.00 | 111.13 +/- 0.00 57 | 90.00 | 100.44 +/- 0.00 58 | 114.00 | 111.83 +/- 0.00 59 | 111.00 | 124.32 +/- 0.00 60 | 103.00 | 100.91 +/- 0.00 61 | 119.00 | 116.35 +/- 0.00 62 | 82.00 | 61.44 +/- 0.00 63 | 59.00 | 55.10 +/- 0.00 64 | 82.00 | 80.53 +/- 0.00 65 | 115.00 | 106.80 +/- 0.00 66 | 106.00 | 104.25 +/- 0.00 67 | 50.00 | 44.85 +/- 0.00 68 | 19.00 | 26.80 +/- 0.00 69 | 94.00 | 89.42 +/- 0.00 70 | 63.00 | 106.93 +/- 0.00 71 | 97.00 | 92.85 +/- 0.00 72 | 145.00 | 110.96 +/- 0.00 73 | 83.00 | 100.52 +/- 0.00 74 | 128.00 | 96.90 +/- 0.00 75 | 10.00 | 8.21 +/- 0.00 76 | 95.00 | 71.87 +/- 0.00 77 | 21.00 | 23.64 +/- 0.00 78 | 72.00 | 98.86 +/- 0.00 79 | 115.00 | 116.30 +/- 0.00 80 | 91.00 | 102.11 +/- 0.00 81 | 54.00 | 53.02 +/- 0.00 82 | 66.00 | 98.32 +/- 0.00 83 | 8.00 | 6.35 +/- 0.00 84 | 92.00 | 102.81 +/- 0.00 85 | 47.00 | 37.59 +/- 0.00 86 | 137.00 | 123.28 +/- 0.00 87 | 7.00 | 4.18 +/- 0.00 88 | 8.00 | 7.29 +/- 0.00 89 | 118.00 | 124.29 +/- 0.00 90 | 85.00 | 47.68 +/- 0.00 91 | 107.00 | 124.26 +/- 0.00 92 | 109.00 | 119.45 +/- 0.00 93 | 121.00 | 120.88 +/- 0.00 94 | 14.00 | 20.50 +/- 0.00 95 | 113.00 | 123.94 +/- 0.00 96 | 38.00 | 28.35 +/- 0.00 97 | 100.00 | 108.99 +/- 0.00 98 | 55.00 | 76.60 +/- 0.00 99 | 136.00 | 115.57 +/- 0.00 100 | 117.00 | 119.38 +/- 0.00 101 | 28.00 | 32.15 +/- 0.00 102 | 118.00 | 110.11 +/- 0.00 103 | 97.00 | 74.73 +/- 0.00 104 | 137.00 | 114.96 +/- 0.00 105 | 50.00 | 58.27 +/- 0.00 106 | 16.00 | 15.54 +/- 0.00 107 | 21.00 | 15.95 +/- 0.00 108 | 112.00 | 121.73 +/- 0.00 109 | 34.00 | 25.95 +/- 0.00 110 | 95.00 | 101.21 +/- 0.00 111 | 131.00 | 103.01 +/- 0.00 112 | 10.00 | 6.69 +/- 0.00 113 | 107.00 | 121.78 +/- 0.00 114 | 8.00 | 6.40 +/- 0.00 115 | 29.00 | 24.19 +/- 0.00 116 | 116.00 | 122.78 +/- 0.00 117 | 15.00 | 20.77 +/- 0.00 118 | 59.00 | 67.73 +/- 0.00 119 | 98.00 | 124.05 +/- 0.00 120 | 128.00 | 123.33 +/- 0.00 121 | 20.00 | 23.70 +/- 0.00 122 | 114.00 | 103.55 +/- 0.00 123 | 28.00 | 22.07 +/- 0.00 124 | 48.00 | 51.16 +/- 0.00 125 | 87.00 | 84.51 +/- 0.00 126 | eval mean loss: 111.96 127 | eval rmse: 14.96 128 | eval mae: 11.08 129 | eval score: 413.45 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 40.47 134 | eval time: 0:00:01.517240 135 | **** end time: 2019-09-27 17:02:38.858838 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_9/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD001', dump_dir='dump/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_9', model='frequentist_dense3', model_path='log/CMAPSS/FD001/min-max/frequentist_dense3/frequentist_dense3_9/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 32490 3 | use_cuda: True 4 | Dataset: CMAPSS/FD001 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-09-27 17:03:48.725211 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-09-27 17:03:48.728446 25 | ground truth | pred +/- std: 26 | 77.00 | 121.02 +/- 0.00 27 | 57.00 | 69.17 +/- 0.00 28 | 124.00 | 93.57 +/- 0.00 29 | 90.00 | 102.10 +/- 0.00 30 | 93.00 | 114.77 +/- 0.00 31 | 37.00 | 42.46 +/- 0.00 32 | 28.00 | 26.72 +/- 0.00 33 | 91.00 | 104.11 +/- 0.00 34 | 113.00 | 93.58 +/- 0.00 35 | 58.00 | 78.67 +/- 0.00 36 | 84.00 | 75.32 +/- 0.00 37 | 20.00 | 19.77 +/- 0.00 38 | 142.00 | 123.28 +/- 0.00 39 | 126.00 | 112.95 +/- 0.00 40 | 11.00 | 11.68 +/- 0.00 41 | 114.00 | 80.41 +/- 0.00 42 | 69.00 | 53.70 +/- 0.00 43 | 26.00 | 33.00 +/- 0.00 44 | 137.00 | 113.00 +/- 0.00 45 | 28.00 | 30.08 +/- 0.00 46 | 21.00 | 22.70 +/- 0.00 47 | 9.00 | 7.60 +/- 0.00 48 | 50.00 | 61.18 +/- 0.00 49 | 135.00 | 122.64 +/- 0.00 50 | 97.00 | 114.18 +/- 0.00 51 | 96.00 | 91.11 +/- 0.00 52 | 89.00 | 94.26 +/- 0.00 53 | 79.00 | 98.03 +/- 0.00 54 | 20.00 | 22.56 +/- 0.00 55 | 18.00 | 13.70 +/- 0.00 56 | 111.00 | 121.80 +/- 0.00 57 | 90.00 | 99.80 +/- 0.00 58 | 114.00 | 106.36 +/- 0.00 59 | 111.00 | 122.96 +/- 0.00 60 | 103.00 | 93.20 +/- 0.00 61 | 119.00 | 119.13 +/- 0.00 62 | 82.00 | 66.20 +/- 0.00 63 | 59.00 | 63.32 +/- 0.00 64 | 82.00 | 86.31 +/- 0.00 65 | 115.00 | 106.03 +/- 0.00 66 | 106.00 | 99.32 +/- 0.00 67 | 50.00 | 52.30 +/- 0.00 68 | 19.00 | 20.59 +/- 0.00 69 | 94.00 | 94.15 +/- 0.00 70 | 63.00 | 108.33 +/- 0.00 71 | 97.00 | 96.95 +/- 0.00 72 | 145.00 | 118.17 +/- 0.00 73 | 83.00 | 107.47 +/- 0.00 74 | 128.00 | 109.48 +/- 0.00 75 | 10.00 | 6.28 +/- 0.00 76 | 95.00 | 75.60 +/- 0.00 77 | 21.00 | 19.19 +/- 0.00 78 | 72.00 | 77.45 +/- 0.00 79 | 115.00 | 119.19 +/- 0.00 80 | 91.00 | 91.40 +/- 0.00 81 | 54.00 | 41.66 +/- 0.00 82 | 66.00 | 103.13 +/- 0.00 83 | 8.00 | 6.15 +/- 0.00 84 | 92.00 | 98.68 +/- 0.00 85 | 47.00 | 49.83 +/- 0.00 86 | 137.00 | 122.68 +/- 0.00 87 | 7.00 | 4.36 +/- 0.00 88 | 8.00 | 8.63 +/- 0.00 89 | 118.00 | 123.33 +/- 0.00 90 | 85.00 | 55.24 +/- 0.00 91 | 107.00 | 123.45 +/- 0.00 92 | 109.00 | 115.90 +/- 0.00 93 | 121.00 | 122.35 +/- 0.00 94 | 14.00 | 16.98 +/- 0.00 95 | 113.00 | 123.21 +/- 0.00 96 | 38.00 | 31.37 +/- 0.00 97 | 100.00 | 108.79 +/- 0.00 98 | 55.00 | 81.91 +/- 0.00 99 | 136.00 | 113.26 +/- 0.00 100 | 117.00 | 111.98 +/- 0.00 101 | 28.00 | 29.03 +/- 0.00 102 | 118.00 | 113.85 +/- 0.00 103 | 97.00 | 73.07 +/- 0.00 104 | 137.00 | 109.43 +/- 0.00 105 | 50.00 | 57.92 +/- 0.00 106 | 16.00 | 13.83 +/- 0.00 107 | 21.00 | 15.02 +/- 0.00 108 | 112.00 | 122.72 +/- 0.00 109 | 34.00 | 23.21 +/- 0.00 110 | 95.00 | 103.62 +/- 0.00 111 | 131.00 | 112.17 +/- 0.00 112 | 10.00 | 9.10 +/- 0.00 113 | 107.00 | 121.76 +/- 0.00 114 | 8.00 | 7.82 +/- 0.00 115 | 29.00 | 33.93 +/- 0.00 116 | 116.00 | 122.51 +/- 0.00 117 | 15.00 | 25.47 +/- 0.00 118 | 59.00 | 68.28 +/- 0.00 119 | 98.00 | 123.04 +/- 0.00 120 | 128.00 | 123.19 +/- 0.00 121 | 20.00 | 26.36 +/- 0.00 122 | 114.00 | 94.09 +/- 0.00 123 | 28.00 | 19.78 +/- 0.00 124 | 48.00 | 52.46 +/- 0.00 125 | 87.00 | 91.46 +/- 0.00 126 | eval mean loss: 108.27 127 | eval rmse: 14.72 128 | eval mae: 10.91 129 | eval score: 423.63 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.56 133 | pred std: 40.38 134 | eval time: 0:00:01.534443 135 | **** end time: 2019-09-27 17:03:50.263156 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_0/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_0', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_0/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8097 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-10-01 02:04:18.732334 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:04:18.737187 25 | ground truth | pred +/- std: 26 | 67.00 | 110.96 +/- 0.00 27 | 115.00 | 112.09 +/- 0.00 28 | 93.00 | 80.13 +/- 0.00 29 | 123.00 | 112.00 +/- 0.00 30 | 8.00 | 56.12 +/- 0.00 31 | 86.00 | 72.96 +/- 0.00 32 | 128.00 | 112.20 +/- 0.00 33 | 40.00 | 84.11 +/- 0.00 34 | 71.00 | 83.70 +/- 0.00 35 | 58.00 | 64.17 +/- 0.00 36 | 128.00 | 98.61 +/- 0.00 37 | 65.00 | 89.77 +/- 0.00 38 | 51.00 | 82.06 +/- 0.00 39 | 27.00 | 78.85 +/- 0.00 40 | 124.00 | 111.97 +/- 0.00 41 | 120.00 | 111.89 +/- 0.00 42 | 137.00 | 110.14 +/- 0.00 43 | 99.00 | 103.68 +/- 0.00 44 | 20.00 | 56.14 +/- 0.00 45 | 11.00 | 56.30 +/- 0.00 46 | 45.00 | 65.05 +/- 0.00 47 | 115.00 | 96.49 +/- 0.00 48 | 115.00 | 82.70 +/- 0.00 49 | 89.00 | 88.41 +/- 0.00 50 | 63.00 | 98.45 +/- 0.00 51 | 44.00 | 61.74 +/- 0.00 52 | 66.00 | 98.64 +/- 0.00 53 | 81.00 | 93.80 +/- 0.00 54 | 144.00 | 112.18 +/- 0.00 55 | 137.00 | 112.20 +/- 0.00 56 | 88.00 | 82.85 +/- 0.00 57 | 100.00 | 111.90 +/- 0.00 58 | 69.00 | 74.91 +/- 0.00 59 | 145.00 | 98.09 +/- 0.00 60 | 92.00 | 93.65 +/- 0.00 61 | 78.00 | 104.73 +/- 0.00 62 | 18.00 | 56.36 +/- 0.00 63 | 56.00 | 112.21 +/- 0.00 64 | 129.00 | 112.21 +/- 0.00 65 | 115.00 | 101.34 +/- 0.00 66 | 117.00 | 110.82 +/- 0.00 67 | 120.00 | 103.40 +/- 0.00 68 | 41.00 | 56.71 +/- 0.00 69 | 133.00 | 112.23 +/- 0.00 70 | 41.00 | 104.98 +/- 0.00 71 | 6.00 | 56.14 +/- 0.00 72 | 7.00 | 56.30 +/- 0.00 73 | 18.00 | 57.96 +/- 0.00 74 | 51.00 | 61.09 +/- 0.00 75 | 55.00 | 64.72 +/- 0.00 76 | 71.00 | 75.86 +/- 0.00 77 | 101.00 | 112.22 +/- 0.00 78 | 58.00 | 102.39 +/- 0.00 79 | 31.00 | 57.06 +/- 0.00 80 | 79.00 | 92.69 +/- 0.00 81 | 9.00 | 56.12 +/- 0.00 82 | 17.00 | 56.13 +/- 0.00 83 | 111.00 | 112.06 +/- 0.00 84 | 49.00 | 58.97 +/- 0.00 85 | 56.00 | 104.64 +/- 0.00 86 | 104.00 | 108.19 +/- 0.00 87 | 113.00 | 59.81 +/- 0.00 88 | 135.00 | 112.22 +/- 0.00 89 | 117.00 | 112.20 +/- 0.00 90 | 87.00 | 110.57 +/- 0.00 91 | 15.00 | 60.85 +/- 0.00 92 | 25.00 | 56.15 +/- 0.00 93 | 55.00 | 59.84 +/- 0.00 94 | 131.00 | 112.16 +/- 0.00 95 | 55.00 | 59.48 +/- 0.00 96 | 45.00 | 88.81 +/- 0.00 97 | 11.00 | 69.87 +/- 0.00 98 | 103.00 | 112.22 +/- 0.00 99 | 77.00 | 67.89 +/- 0.00 100 | 55.00 | 80.75 +/- 0.00 101 | 10.00 | 56.12 +/- 0.00 102 | 56.00 | 75.60 +/- 0.00 103 | 127.00 | 111.08 +/- 0.00 104 | 14.00 | 56.13 +/- 0.00 105 | 68.00 | 79.56 +/- 0.00 106 | 88.00 | 105.35 +/- 0.00 107 | 87.00 | 110.10 +/- 0.00 108 | 8.00 | 56.12 +/- 0.00 109 | 22.00 | 66.38 +/- 0.00 110 | 55.00 | 70.88 +/- 0.00 111 | 87.00 | 102.95 +/- 0.00 112 | 85.00 | 111.88 +/- 0.00 113 | 78.00 | 61.71 +/- 0.00 114 | 108.00 | 112.06 +/- 0.00 115 | 144.00 | 112.08 +/- 0.00 116 | 119.00 | 112.11 +/- 0.00 117 | 99.00 | 110.39 +/- 0.00 118 | 145.00 | 111.82 +/- 0.00 119 | 87.00 | 75.71 +/- 0.00 120 | 28.00 | 56.49 +/- 0.00 121 | 136.00 | 110.26 +/- 0.00 122 | 27.00 | 56.17 +/- 0.00 123 | 132.00 | 112.07 +/- 0.00 124 | 28.00 | 57.34 +/- 0.00 125 | 35.00 | 56.18 +/- 0.00 126 | eval mean loss: 417.03 127 | eval rmse: 28.88 128 | eval mae: 24.16 129 | eval score: 3643.56 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 22.34 134 | eval time: 0:00:01.344616 135 | **** end time: 2019-10-01 02:04:20.082008 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_1/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_1', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_1/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8117 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-10-01 02:04:27.328881 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:04:27.334168 25 | ground truth | pred +/- std: 26 | 67.00 | 110.94 +/- 0.00 27 | 115.00 | 112.07 +/- 0.00 28 | 93.00 | 80.67 +/- 0.00 29 | 123.00 | 111.98 +/- 0.00 30 | 8.00 | 56.12 +/- 0.00 31 | 86.00 | 72.80 +/- 0.00 32 | 128.00 | 112.19 +/- 0.00 33 | 40.00 | 83.71 +/- 0.00 34 | 71.00 | 83.60 +/- 0.00 35 | 58.00 | 64.37 +/- 0.00 36 | 128.00 | 98.66 +/- 0.00 37 | 65.00 | 90.47 +/- 0.00 38 | 51.00 | 81.92 +/- 0.00 39 | 27.00 | 78.53 +/- 0.00 40 | 124.00 | 111.95 +/- 0.00 41 | 120.00 | 111.86 +/- 0.00 42 | 137.00 | 110.32 +/- 0.00 43 | 99.00 | 103.55 +/- 0.00 44 | 20.00 | 56.13 +/- 0.00 45 | 11.00 | 56.29 +/- 0.00 46 | 45.00 | 64.73 +/- 0.00 47 | 115.00 | 96.96 +/- 0.00 48 | 115.00 | 83.30 +/- 0.00 49 | 89.00 | 88.74 +/- 0.00 50 | 63.00 | 98.32 +/- 0.00 51 | 44.00 | 61.67 +/- 0.00 52 | 66.00 | 97.94 +/- 0.00 53 | 81.00 | 93.91 +/- 0.00 54 | 144.00 | 112.17 +/- 0.00 55 | 137.00 | 112.19 +/- 0.00 56 | 88.00 | 82.56 +/- 0.00 57 | 100.00 | 111.87 +/- 0.00 58 | 69.00 | 75.29 +/- 0.00 59 | 145.00 | 98.34 +/- 0.00 60 | 92.00 | 94.53 +/- 0.00 61 | 78.00 | 105.01 +/- 0.00 62 | 18.00 | 56.34 +/- 0.00 63 | 56.00 | 112.19 +/- 0.00 64 | 129.00 | 112.20 +/- 0.00 65 | 115.00 | 101.39 +/- 0.00 66 | 117.00 | 110.83 +/- 0.00 67 | 120.00 | 103.69 +/- 0.00 68 | 41.00 | 56.66 +/- 0.00 69 | 133.00 | 112.22 +/- 0.00 70 | 41.00 | 104.77 +/- 0.00 71 | 6.00 | 56.14 +/- 0.00 72 | 7.00 | 56.28 +/- 0.00 73 | 18.00 | 57.93 +/- 0.00 74 | 51.00 | 60.81 +/- 0.00 75 | 55.00 | 64.69 +/- 0.00 76 | 71.00 | 76.14 +/- 0.00 77 | 101.00 | 112.21 +/- 0.00 78 | 58.00 | 102.02 +/- 0.00 79 | 31.00 | 57.02 +/- 0.00 80 | 79.00 | 92.63 +/- 0.00 81 | 9.00 | 56.11 +/- 0.00 82 | 17.00 | 56.12 +/- 0.00 83 | 111.00 | 112.04 +/- 0.00 84 | 49.00 | 58.86 +/- 0.00 85 | 56.00 | 104.60 +/- 0.00 86 | 104.00 | 108.20 +/- 0.00 87 | 113.00 | 59.83 +/- 0.00 88 | 135.00 | 112.20 +/- 0.00 89 | 117.00 | 112.18 +/- 0.00 90 | 87.00 | 110.59 +/- 0.00 91 | 15.00 | 60.69 +/- 0.00 92 | 25.00 | 56.14 +/- 0.00 93 | 55.00 | 59.86 +/- 0.00 94 | 131.00 | 112.15 +/- 0.00 95 | 55.00 | 59.37 +/- 0.00 96 | 45.00 | 88.82 +/- 0.00 97 | 11.00 | 69.82 +/- 0.00 98 | 103.00 | 112.21 +/- 0.00 99 | 77.00 | 68.11 +/- 0.00 100 | 55.00 | 81.16 +/- 0.00 101 | 10.00 | 56.11 +/- 0.00 102 | 56.00 | 75.66 +/- 0.00 103 | 127.00 | 111.06 +/- 0.00 104 | 14.00 | 56.12 +/- 0.00 105 | 68.00 | 78.69 +/- 0.00 106 | 88.00 | 105.53 +/- 0.00 107 | 87.00 | 110.15 +/- 0.00 108 | 8.00 | 56.12 +/- 0.00 109 | 22.00 | 66.13 +/- 0.00 110 | 55.00 | 71.03 +/- 0.00 111 | 87.00 | 103.13 +/- 0.00 112 | 85.00 | 111.85 +/- 0.00 113 | 78.00 | 61.63 +/- 0.00 114 | 108.00 | 112.04 +/- 0.00 115 | 144.00 | 112.08 +/- 0.00 116 | 119.00 | 112.10 +/- 0.00 117 | 99.00 | 110.34 +/- 0.00 118 | 145.00 | 111.79 +/- 0.00 119 | 87.00 | 75.33 +/- 0.00 120 | 28.00 | 56.48 +/- 0.00 121 | 136.00 | 110.32 +/- 0.00 122 | 27.00 | 56.17 +/- 0.00 123 | 132.00 | 112.06 +/- 0.00 124 | 28.00 | 57.50 +/- 0.00 125 | 35.00 | 56.18 +/- 0.00 126 | eval mean loss: 415.59 127 | eval rmse: 28.83 128 | eval mae: 24.13 129 | eval score: 3609.54 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 22.36 134 | eval time: 0:00:01.386014 135 | **** end time: 2019-10-01 02:04:28.720392 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_2/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_2', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_2/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8151 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-10-01 02:04:35.910186 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:04:35.914845 25 | ground truth | pred +/- std: 26 | 67.00 | 110.95 +/- 0.00 27 | 115.00 | 112.07 +/- 0.00 28 | 93.00 | 80.49 +/- 0.00 29 | 123.00 | 111.98 +/- 0.00 30 | 8.00 | 56.11 +/- 0.00 31 | 86.00 | 72.80 +/- 0.00 32 | 128.00 | 112.18 +/- 0.00 33 | 40.00 | 83.87 +/- 0.00 34 | 71.00 | 83.46 +/- 0.00 35 | 58.00 | 64.32 +/- 0.00 36 | 128.00 | 98.59 +/- 0.00 37 | 65.00 | 90.24 +/- 0.00 38 | 51.00 | 81.87 +/- 0.00 39 | 27.00 | 78.55 +/- 0.00 40 | 124.00 | 111.95 +/- 0.00 41 | 120.00 | 111.86 +/- 0.00 42 | 137.00 | 110.28 +/- 0.00 43 | 99.00 | 103.59 +/- 0.00 44 | 20.00 | 56.12 +/- 0.00 45 | 11.00 | 56.29 +/- 0.00 46 | 45.00 | 64.84 +/- 0.00 47 | 115.00 | 96.93 +/- 0.00 48 | 115.00 | 83.14 +/- 0.00 49 | 89.00 | 88.87 +/- 0.00 50 | 63.00 | 98.23 +/- 0.00 51 | 44.00 | 61.72 +/- 0.00 52 | 66.00 | 98.08 +/- 0.00 53 | 81.00 | 93.82 +/- 0.00 54 | 144.00 | 112.17 +/- 0.00 55 | 137.00 | 112.19 +/- 0.00 56 | 88.00 | 82.70 +/- 0.00 57 | 100.00 | 111.87 +/- 0.00 58 | 69.00 | 75.17 +/- 0.00 59 | 145.00 | 98.31 +/- 0.00 60 | 92.00 | 94.43 +/- 0.00 61 | 78.00 | 104.83 +/- 0.00 62 | 18.00 | 56.35 +/- 0.00 63 | 56.00 | 112.19 +/- 0.00 64 | 129.00 | 112.20 +/- 0.00 65 | 115.00 | 101.31 +/- 0.00 66 | 117.00 | 110.81 +/- 0.00 67 | 120.00 | 103.63 +/- 0.00 68 | 41.00 | 56.67 +/- 0.00 69 | 133.00 | 112.21 +/- 0.00 70 | 41.00 | 104.96 +/- 0.00 71 | 6.00 | 56.13 +/- 0.00 72 | 7.00 | 56.27 +/- 0.00 73 | 18.00 | 57.96 +/- 0.00 74 | 51.00 | 60.89 +/- 0.00 75 | 55.00 | 64.67 +/- 0.00 76 | 71.00 | 76.11 +/- 0.00 77 | 101.00 | 112.20 +/- 0.00 78 | 58.00 | 102.11 +/- 0.00 79 | 31.00 | 57.06 +/- 0.00 80 | 79.00 | 92.68 +/- 0.00 81 | 9.00 | 56.11 +/- 0.00 82 | 17.00 | 56.12 +/- 0.00 83 | 111.00 | 112.03 +/- 0.00 84 | 49.00 | 58.92 +/- 0.00 85 | 56.00 | 104.61 +/- 0.00 86 | 104.00 | 108.19 +/- 0.00 87 | 113.00 | 59.81 +/- 0.00 88 | 135.00 | 112.20 +/- 0.00 89 | 117.00 | 112.18 +/- 0.00 90 | 87.00 | 110.59 +/- 0.00 91 | 15.00 | 60.71 +/- 0.00 92 | 25.00 | 56.14 +/- 0.00 93 | 55.00 | 59.89 +/- 0.00 94 | 131.00 | 112.14 +/- 0.00 95 | 55.00 | 59.42 +/- 0.00 96 | 45.00 | 88.95 +/- 0.00 97 | 11.00 | 69.90 +/- 0.00 98 | 103.00 | 112.20 +/- 0.00 99 | 77.00 | 68.05 +/- 0.00 100 | 55.00 | 80.97 +/- 0.00 101 | 10.00 | 56.11 +/- 0.00 102 | 56.00 | 75.40 +/- 0.00 103 | 127.00 | 111.09 +/- 0.00 104 | 14.00 | 56.11 +/- 0.00 105 | 68.00 | 78.95 +/- 0.00 106 | 88.00 | 105.43 +/- 0.00 107 | 87.00 | 110.12 +/- 0.00 108 | 8.00 | 56.11 +/- 0.00 109 | 22.00 | 66.25 +/- 0.00 110 | 55.00 | 70.84 +/- 0.00 111 | 87.00 | 103.00 +/- 0.00 112 | 85.00 | 111.85 +/- 0.00 113 | 78.00 | 61.64 +/- 0.00 114 | 108.00 | 112.04 +/- 0.00 115 | 144.00 | 112.07 +/- 0.00 116 | 119.00 | 112.10 +/- 0.00 117 | 99.00 | 110.34 +/- 0.00 118 | 145.00 | 111.78 +/- 0.00 119 | 87.00 | 75.36 +/- 0.00 120 | 28.00 | 56.48 +/- 0.00 121 | 136.00 | 110.35 +/- 0.00 122 | 27.00 | 56.16 +/- 0.00 123 | 132.00 | 112.06 +/- 0.00 124 | 28.00 | 57.50 +/- 0.00 125 | 35.00 | 56.17 +/- 0.00 126 | eval mean loss: 415.91 127 | eval rmse: 28.84 128 | eval mae: 24.13 129 | eval score: 3627.23 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 22.35 134 | eval time: 0:00:01.374150 135 | **** end time: 2019-10-01 02:04:37.289175 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_3/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_3', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_3/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8172 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-10-01 02:04:44.533246 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:04:44.538132 25 | ground truth | pred +/- std: 26 | 67.00 | 111.01 +/- 0.00 27 | 115.00 | 112.07 +/- 0.00 28 | 93.00 | 80.25 +/- 0.00 29 | 123.00 | 111.98 +/- 0.00 30 | 8.00 | 56.11 +/- 0.00 31 | 86.00 | 72.82 +/- 0.00 32 | 128.00 | 112.18 +/- 0.00 33 | 40.00 | 84.07 +/- 0.00 34 | 71.00 | 83.70 +/- 0.00 35 | 58.00 | 64.39 +/- 0.00 36 | 128.00 | 98.69 +/- 0.00 37 | 65.00 | 90.13 +/- 0.00 38 | 51.00 | 82.19 +/- 0.00 39 | 27.00 | 78.58 +/- 0.00 40 | 124.00 | 111.92 +/- 0.00 41 | 120.00 | 111.89 +/- 0.00 42 | 137.00 | 110.17 +/- 0.00 43 | 99.00 | 103.59 +/- 0.00 44 | 20.00 | 56.13 +/- 0.00 45 | 11.00 | 56.29 +/- 0.00 46 | 45.00 | 64.87 +/- 0.00 47 | 115.00 | 96.57 +/- 0.00 48 | 115.00 | 83.20 +/- 0.00 49 | 89.00 | 88.76 +/- 0.00 50 | 63.00 | 98.28 +/- 0.00 51 | 44.00 | 61.58 +/- 0.00 52 | 66.00 | 98.31 +/- 0.00 53 | 81.00 | 93.87 +/- 0.00 54 | 144.00 | 112.17 +/- 0.00 55 | 137.00 | 112.19 +/- 0.00 56 | 88.00 | 82.74 +/- 0.00 57 | 100.00 | 111.87 +/- 0.00 58 | 69.00 | 75.21 +/- 0.00 59 | 145.00 | 98.27 +/- 0.00 60 | 92.00 | 94.09 +/- 0.00 61 | 78.00 | 104.67 +/- 0.00 62 | 18.00 | 56.34 +/- 0.00 63 | 56.00 | 112.19 +/- 0.00 64 | 129.00 | 112.20 +/- 0.00 65 | 115.00 | 101.31 +/- 0.00 66 | 117.00 | 110.92 +/- 0.00 67 | 120.00 | 103.45 +/- 0.00 68 | 41.00 | 56.68 +/- 0.00 69 | 133.00 | 112.22 +/- 0.00 70 | 41.00 | 105.05 +/- 0.00 71 | 6.00 | 56.13 +/- 0.00 72 | 7.00 | 56.28 +/- 0.00 73 | 18.00 | 57.96 +/- 0.00 74 | 51.00 | 60.87 +/- 0.00 75 | 55.00 | 64.79 +/- 0.00 76 | 71.00 | 76.18 +/- 0.00 77 | 101.00 | 112.20 +/- 0.00 78 | 58.00 | 102.35 +/- 0.00 79 | 31.00 | 57.05 +/- 0.00 80 | 79.00 | 92.50 +/- 0.00 81 | 9.00 | 56.11 +/- 0.00 82 | 17.00 | 56.12 +/- 0.00 83 | 111.00 | 112.04 +/- 0.00 84 | 49.00 | 58.91 +/- 0.00 85 | 56.00 | 104.66 +/- 0.00 86 | 104.00 | 108.23 +/- 0.00 87 | 113.00 | 59.65 +/- 0.00 88 | 135.00 | 112.20 +/- 0.00 89 | 117.00 | 112.19 +/- 0.00 90 | 87.00 | 110.57 +/- 0.00 91 | 15.00 | 60.59 +/- 0.00 92 | 25.00 | 56.14 +/- 0.00 93 | 55.00 | 59.90 +/- 0.00 94 | 131.00 | 112.14 +/- 0.00 95 | 55.00 | 59.45 +/- 0.00 96 | 45.00 | 88.87 +/- 0.00 97 | 11.00 | 69.81 +/- 0.00 98 | 103.00 | 112.20 +/- 0.00 99 | 77.00 | 68.01 +/- 0.00 100 | 55.00 | 80.99 +/- 0.00 101 | 10.00 | 56.11 +/- 0.00 102 | 56.00 | 75.29 +/- 0.00 103 | 127.00 | 111.10 +/- 0.00 104 | 14.00 | 56.12 +/- 0.00 105 | 68.00 | 79.34 +/- 0.00 106 | 88.00 | 105.57 +/- 0.00 107 | 87.00 | 110.13 +/- 0.00 108 | 8.00 | 56.11 +/- 0.00 109 | 22.00 | 66.24 +/- 0.00 110 | 55.00 | 70.82 +/- 0.00 111 | 87.00 | 102.99 +/- 0.00 112 | 85.00 | 111.83 +/- 0.00 113 | 78.00 | 61.74 +/- 0.00 114 | 108.00 | 112.04 +/- 0.00 115 | 144.00 | 112.07 +/- 0.00 116 | 119.00 | 112.11 +/- 0.00 117 | 99.00 | 110.37 +/- 0.00 118 | 145.00 | 111.78 +/- 0.00 119 | 87.00 | 75.76 +/- 0.00 120 | 28.00 | 56.48 +/- 0.00 121 | 136.00 | 110.38 +/- 0.00 122 | 27.00 | 56.16 +/- 0.00 123 | 132.00 | 112.06 +/- 0.00 124 | 28.00 | 57.37 +/- 0.00 125 | 35.00 | 56.17 +/- 0.00 126 | eval mean loss: 416.34 127 | eval rmse: 28.86 128 | eval mae: 24.14 129 | eval score: 3634.61 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 22.35 134 | eval time: 0:00:01.353604 135 | **** end time: 2019-10-01 02:04:45.891936 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_4/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_4', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_4/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8193 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-10-01 02:04:53.130575 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:04:53.135538 25 | ground truth | pred +/- std: 26 | 67.00 | 111.05 +/- 0.00 27 | 115.00 | 112.09 +/- 0.00 28 | 93.00 | 80.26 +/- 0.00 29 | 123.00 | 112.00 +/- 0.00 30 | 8.00 | 56.12 +/- 0.00 31 | 86.00 | 72.83 +/- 0.00 32 | 128.00 | 112.19 +/- 0.00 33 | 40.00 | 83.88 +/- 0.00 34 | 71.00 | 83.54 +/- 0.00 35 | 58.00 | 64.43 +/- 0.00 36 | 128.00 | 98.58 +/- 0.00 37 | 65.00 | 90.41 +/- 0.00 38 | 51.00 | 82.07 +/- 0.00 39 | 27.00 | 78.67 +/- 0.00 40 | 124.00 | 111.96 +/- 0.00 41 | 120.00 | 111.87 +/- 0.00 42 | 137.00 | 110.19 +/- 0.00 43 | 99.00 | 103.43 +/- 0.00 44 | 20.00 | 56.13 +/- 0.00 45 | 11.00 | 56.29 +/- 0.00 46 | 45.00 | 64.66 +/- 0.00 47 | 115.00 | 96.58 +/- 0.00 48 | 115.00 | 83.08 +/- 0.00 49 | 89.00 | 88.31 +/- 0.00 50 | 63.00 | 98.28 +/- 0.00 51 | 44.00 | 61.62 +/- 0.00 52 | 66.00 | 98.14 +/- 0.00 53 | 81.00 | 93.83 +/- 0.00 54 | 144.00 | 112.18 +/- 0.00 55 | 137.00 | 112.19 +/- 0.00 56 | 88.00 | 82.68 +/- 0.00 57 | 100.00 | 111.90 +/- 0.00 58 | 69.00 | 75.34 +/- 0.00 59 | 145.00 | 98.21 +/- 0.00 60 | 92.00 | 94.24 +/- 0.00 61 | 78.00 | 104.62 +/- 0.00 62 | 18.00 | 56.35 +/- 0.00 63 | 56.00 | 112.19 +/- 0.00 64 | 129.00 | 112.20 +/- 0.00 65 | 115.00 | 101.30 +/- 0.00 66 | 117.00 | 110.98 +/- 0.00 67 | 120.00 | 103.45 +/- 0.00 68 | 41.00 | 56.65 +/- 0.00 69 | 133.00 | 112.22 +/- 0.00 70 | 41.00 | 104.72 +/- 0.00 71 | 6.00 | 56.14 +/- 0.00 72 | 7.00 | 56.29 +/- 0.00 73 | 18.00 | 58.01 +/- 0.00 74 | 51.00 | 60.85 +/- 0.00 75 | 55.00 | 64.80 +/- 0.00 76 | 71.00 | 76.22 +/- 0.00 77 | 101.00 | 112.21 +/- 0.00 78 | 58.00 | 102.27 +/- 0.00 79 | 31.00 | 57.05 +/- 0.00 80 | 79.00 | 92.14 +/- 0.00 81 | 9.00 | 56.11 +/- 0.00 82 | 17.00 | 56.12 +/- 0.00 83 | 111.00 | 112.07 +/- 0.00 84 | 49.00 | 58.89 +/- 0.00 85 | 56.00 | 104.58 +/- 0.00 86 | 104.00 | 108.22 +/- 0.00 87 | 113.00 | 59.66 +/- 0.00 88 | 135.00 | 112.20 +/- 0.00 89 | 117.00 | 112.20 +/- 0.00 90 | 87.00 | 110.68 +/- 0.00 91 | 15.00 | 60.78 +/- 0.00 92 | 25.00 | 56.14 +/- 0.00 93 | 55.00 | 59.85 +/- 0.00 94 | 131.00 | 112.16 +/- 0.00 95 | 55.00 | 59.45 +/- 0.00 96 | 45.00 | 88.94 +/- 0.00 97 | 11.00 | 70.10 +/- 0.00 98 | 103.00 | 112.20 +/- 0.00 99 | 77.00 | 68.16 +/- 0.00 100 | 55.00 | 81.26 +/- 0.00 101 | 10.00 | 56.12 +/- 0.00 102 | 56.00 | 75.65 +/- 0.00 103 | 127.00 | 111.13 +/- 0.00 104 | 14.00 | 56.12 +/- 0.00 105 | 68.00 | 78.98 +/- 0.00 106 | 88.00 | 105.81 +/- 0.00 107 | 87.00 | 110.08 +/- 0.00 108 | 8.00 | 56.12 +/- 0.00 109 | 22.00 | 66.43 +/- 0.00 110 | 55.00 | 70.93 +/- 0.00 111 | 87.00 | 102.93 +/- 0.00 112 | 85.00 | 111.87 +/- 0.00 113 | 78.00 | 61.52 +/- 0.00 114 | 108.00 | 112.06 +/- 0.00 115 | 144.00 | 112.08 +/- 0.00 116 | 119.00 | 112.14 +/- 0.00 117 | 99.00 | 110.39 +/- 0.00 118 | 145.00 | 111.80 +/- 0.00 119 | 87.00 | 75.53 +/- 0.00 120 | 28.00 | 56.47 +/- 0.00 121 | 136.00 | 110.33 +/- 0.00 122 | 27.00 | 56.17 +/- 0.00 123 | 132.00 | 112.08 +/- 0.00 124 | 28.00 | 57.47 +/- 0.00 125 | 35.00 | 56.18 +/- 0.00 126 | eval mean loss: 416.64 127 | eval rmse: 28.87 128 | eval mae: 24.15 129 | eval score: 3629.90 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 22.35 134 | eval time: 0:00:01.347393 135 | **** end time: 2019-10-01 02:04:54.483172 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_5/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_5', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_5/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8214 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-10-01 02:05:01.690090 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:05:01.695185 25 | ground truth | pred +/- std: 26 | 67.00 | 110.95 +/- 0.00 27 | 115.00 | 112.08 +/- 0.00 28 | 93.00 | 80.48 +/- 0.00 29 | 123.00 | 111.99 +/- 0.00 30 | 8.00 | 56.12 +/- 0.00 31 | 86.00 | 72.92 +/- 0.00 32 | 128.00 | 112.19 +/- 0.00 33 | 40.00 | 83.80 +/- 0.00 34 | 71.00 | 83.47 +/- 0.00 35 | 58.00 | 64.30 +/- 0.00 36 | 128.00 | 98.68 +/- 0.00 37 | 65.00 | 90.09 +/- 0.00 38 | 51.00 | 81.86 +/- 0.00 39 | 27.00 | 78.77 +/- 0.00 40 | 124.00 | 111.98 +/- 0.00 41 | 120.00 | 111.86 +/- 0.00 42 | 137.00 | 110.28 +/- 0.00 43 | 99.00 | 103.48 +/- 0.00 44 | 20.00 | 56.13 +/- 0.00 45 | 11.00 | 56.30 +/- 0.00 46 | 45.00 | 64.85 +/- 0.00 47 | 115.00 | 96.81 +/- 0.00 48 | 115.00 | 82.98 +/- 0.00 49 | 89.00 | 88.57 +/- 0.00 50 | 63.00 | 98.42 +/- 0.00 51 | 44.00 | 61.78 +/- 0.00 52 | 66.00 | 98.26 +/- 0.00 53 | 81.00 | 93.81 +/- 0.00 54 | 144.00 | 112.17 +/- 0.00 55 | 137.00 | 112.19 +/- 0.00 56 | 88.00 | 82.67 +/- 0.00 57 | 100.00 | 111.89 +/- 0.00 58 | 69.00 | 75.13 +/- 0.00 59 | 145.00 | 98.26 +/- 0.00 60 | 92.00 | 94.37 +/- 0.00 61 | 78.00 | 104.96 +/- 0.00 62 | 18.00 | 56.35 +/- 0.00 63 | 56.00 | 112.20 +/- 0.00 64 | 129.00 | 112.20 +/- 0.00 65 | 115.00 | 101.41 +/- 0.00 66 | 117.00 | 110.77 +/- 0.00 67 | 120.00 | 103.62 +/- 0.00 68 | 41.00 | 56.68 +/- 0.00 69 | 133.00 | 112.22 +/- 0.00 70 | 41.00 | 104.79 +/- 0.00 71 | 6.00 | 56.14 +/- 0.00 72 | 7.00 | 56.29 +/- 0.00 73 | 18.00 | 57.93 +/- 0.00 74 | 51.00 | 60.92 +/- 0.00 75 | 55.00 | 64.66 +/- 0.00 76 | 71.00 | 75.76 +/- 0.00 77 | 101.00 | 112.21 +/- 0.00 78 | 58.00 | 102.09 +/- 0.00 79 | 31.00 | 57.03 +/- 0.00 80 | 79.00 | 92.66 +/- 0.00 81 | 9.00 | 56.12 +/- 0.00 82 | 17.00 | 56.13 +/- 0.00 83 | 111.00 | 112.05 +/- 0.00 84 | 49.00 | 58.93 +/- 0.00 85 | 56.00 | 104.64 +/- 0.00 86 | 104.00 | 108.23 +/- 0.00 87 | 113.00 | 59.82 +/- 0.00 88 | 135.00 | 112.21 +/- 0.00 89 | 117.00 | 112.18 +/- 0.00 90 | 87.00 | 110.61 +/- 0.00 91 | 15.00 | 60.86 +/- 0.00 92 | 25.00 | 56.15 +/- 0.00 93 | 55.00 | 59.87 +/- 0.00 94 | 131.00 | 112.15 +/- 0.00 95 | 55.00 | 59.46 +/- 0.00 96 | 45.00 | 88.96 +/- 0.00 97 | 11.00 | 69.85 +/- 0.00 98 | 103.00 | 112.21 +/- 0.00 99 | 77.00 | 68.10 +/- 0.00 100 | 55.00 | 81.17 +/- 0.00 101 | 10.00 | 56.12 +/- 0.00 102 | 56.00 | 75.74 +/- 0.00 103 | 127.00 | 111.07 +/- 0.00 104 | 14.00 | 56.12 +/- 0.00 105 | 68.00 | 79.06 +/- 0.00 106 | 88.00 | 105.49 +/- 0.00 107 | 87.00 | 110.04 +/- 0.00 108 | 8.00 | 56.12 +/- 0.00 109 | 22.00 | 66.23 +/- 0.00 110 | 55.00 | 71.15 +/- 0.00 111 | 87.00 | 103.02 +/- 0.00 112 | 85.00 | 111.87 +/- 0.00 113 | 78.00 | 61.59 +/- 0.00 114 | 108.00 | 112.05 +/- 0.00 115 | 144.00 | 112.07 +/- 0.00 116 | 119.00 | 112.10 +/- 0.00 117 | 99.00 | 110.28 +/- 0.00 118 | 145.00 | 111.81 +/- 0.00 119 | 87.00 | 75.21 +/- 0.00 120 | 28.00 | 56.49 +/- 0.00 121 | 136.00 | 110.23 +/- 0.00 122 | 27.00 | 56.17 +/- 0.00 123 | 132.00 | 112.07 +/- 0.00 124 | 28.00 | 57.54 +/- 0.00 125 | 35.00 | 56.18 +/- 0.00 126 | eval mean loss: 416.32 127 | eval rmse: 28.86 128 | eval mae: 24.14 129 | eval score: 3623.29 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 22.35 134 | eval time: 0:00:01.373914 135 | **** end time: 2019-10-01 02:05:03.069283 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_6/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_6', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_6/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8236 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-10-01 02:05:10.257061 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:05:10.262020 25 | ground truth | pred +/- std: 26 | 67.00 | 111.06 +/- 0.00 27 | 115.00 | 112.12 +/- 0.00 28 | 93.00 | 79.63 +/- 0.00 29 | 123.00 | 112.06 +/- 0.00 30 | 8.00 | 56.13 +/- 0.00 31 | 86.00 | 72.80 +/- 0.00 32 | 128.00 | 112.21 +/- 0.00 33 | 40.00 | 84.55 +/- 0.00 34 | 71.00 | 83.84 +/- 0.00 35 | 58.00 | 64.28 +/- 0.00 36 | 128.00 | 98.88 +/- 0.00 37 | 65.00 | 89.94 +/- 0.00 38 | 51.00 | 82.46 +/- 0.00 39 | 27.00 | 78.78 +/- 0.00 40 | 124.00 | 112.02 +/- 0.00 41 | 120.00 | 111.96 +/- 0.00 42 | 137.00 | 109.98 +/- 0.00 43 | 99.00 | 103.95 +/- 0.00 44 | 20.00 | 56.14 +/- 0.00 45 | 11.00 | 56.30 +/- 0.00 46 | 45.00 | 65.11 +/- 0.00 47 | 115.00 | 96.21 +/- 0.00 48 | 115.00 | 83.22 +/- 0.00 49 | 89.00 | 87.84 +/- 0.00 50 | 63.00 | 98.33 +/- 0.00 51 | 44.00 | 61.43 +/- 0.00 52 | 66.00 | 98.77 +/- 0.00 53 | 81.00 | 93.74 +/- 0.00 54 | 144.00 | 112.20 +/- 0.00 55 | 137.00 | 112.22 +/- 0.00 56 | 88.00 | 82.97 +/- 0.00 57 | 100.00 | 111.96 +/- 0.00 58 | 69.00 | 74.75 +/- 0.00 59 | 145.00 | 98.19 +/- 0.00 60 | 92.00 | 92.67 +/- 0.00 61 | 78.00 | 104.28 +/- 0.00 62 | 18.00 | 56.37 +/- 0.00 63 | 56.00 | 112.22 +/- 0.00 64 | 129.00 | 112.23 +/- 0.00 65 | 115.00 | 101.27 +/- 0.00 66 | 117.00 | 110.87 +/- 0.00 67 | 120.00 | 103.31 +/- 0.00 68 | 41.00 | 56.70 +/- 0.00 69 | 133.00 | 112.24 +/- 0.00 70 | 41.00 | 105.31 +/- 0.00 71 | 6.00 | 56.15 +/- 0.00 72 | 7.00 | 56.29 +/- 0.00 73 | 18.00 | 57.92 +/- 0.00 74 | 51.00 | 61.17 +/- 0.00 75 | 55.00 | 64.89 +/- 0.00 76 | 71.00 | 75.95 +/- 0.00 77 | 101.00 | 112.23 +/- 0.00 78 | 58.00 | 102.90 +/- 0.00 79 | 31.00 | 57.08 +/- 0.00 80 | 79.00 | 92.25 +/- 0.00 81 | 9.00 | 56.13 +/- 0.00 82 | 17.00 | 56.14 +/- 0.00 83 | 111.00 | 112.10 +/- 0.00 84 | 49.00 | 58.90 +/- 0.00 85 | 56.00 | 104.53 +/- 0.00 86 | 104.00 | 108.20 +/- 0.00 87 | 113.00 | 59.73 +/- 0.00 88 | 135.00 | 112.23 +/- 0.00 89 | 117.00 | 112.21 +/- 0.00 90 | 87.00 | 110.58 +/- 0.00 91 | 15.00 | 60.75 +/- 0.00 92 | 25.00 | 56.16 +/- 0.00 93 | 55.00 | 59.79 +/- 0.00 94 | 131.00 | 112.19 +/- 0.00 95 | 55.00 | 59.56 +/- 0.00 96 | 45.00 | 88.52 +/- 0.00 97 | 11.00 | 69.73 +/- 0.00 98 | 103.00 | 112.23 +/- 0.00 99 | 77.00 | 67.86 +/- 0.00 100 | 55.00 | 80.49 +/- 0.00 101 | 10.00 | 56.13 +/- 0.00 102 | 56.00 | 75.45 +/- 0.00 103 | 127.00 | 111.23 +/- 0.00 104 | 14.00 | 56.13 +/- 0.00 105 | 68.00 | 80.01 +/- 0.00 106 | 88.00 | 105.15 +/- 0.00 107 | 87.00 | 110.34 +/- 0.00 108 | 8.00 | 56.13 +/- 0.00 109 | 22.00 | 66.43 +/- 0.00 110 | 55.00 | 70.74 +/- 0.00 111 | 87.00 | 103.01 +/- 0.00 112 | 85.00 | 111.93 +/- 0.00 113 | 78.00 | 61.83 +/- 0.00 114 | 108.00 | 112.11 +/- 0.00 115 | 144.00 | 112.10 +/- 0.00 116 | 119.00 | 112.14 +/- 0.00 117 | 99.00 | 110.65 +/- 0.00 118 | 145.00 | 111.91 +/- 0.00 119 | 87.00 | 76.41 +/- 0.00 120 | 28.00 | 56.48 +/- 0.00 121 | 136.00 | 110.41 +/- 0.00 122 | 27.00 | 56.17 +/- 0.00 123 | 132.00 | 112.12 +/- 0.00 124 | 28.00 | 57.02 +/- 0.00 125 | 35.00 | 56.19 +/- 0.00 126 | eval mean loss: 417.03 127 | eval rmse: 28.88 128 | eval mae: 24.15 129 | eval score: 3661.84 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 22.36 134 | eval time: 0:00:01.398892 135 | **** end time: 2019-10-01 02:05:11.661097 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_7/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_7', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_7/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8256 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-10-01 02:05:18.831568 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:05:18.836709 25 | ground truth | pred +/- std: 26 | 67.00 | 110.92 +/- 0.00 27 | 115.00 | 112.06 +/- 0.00 28 | 93.00 | 80.41 +/- 0.00 29 | 123.00 | 111.97 +/- 0.00 30 | 8.00 | 56.11 +/- 0.00 31 | 86.00 | 72.85 +/- 0.00 32 | 128.00 | 112.18 +/- 0.00 33 | 40.00 | 84.00 +/- 0.00 34 | 71.00 | 83.57 +/- 0.00 35 | 58.00 | 64.24 +/- 0.00 36 | 128.00 | 98.60 +/- 0.00 37 | 65.00 | 89.98 +/- 0.00 38 | 51.00 | 81.95 +/- 0.00 39 | 27.00 | 78.54 +/- 0.00 40 | 124.00 | 111.95 +/- 0.00 41 | 120.00 | 111.86 +/- 0.00 42 | 137.00 | 110.25 +/- 0.00 43 | 99.00 | 103.56 +/- 0.00 44 | 20.00 | 56.13 +/- 0.00 45 | 11.00 | 56.30 +/- 0.00 46 | 45.00 | 64.89 +/- 0.00 47 | 115.00 | 96.68 +/- 0.00 48 | 115.00 | 82.82 +/- 0.00 49 | 89.00 | 88.79 +/- 0.00 50 | 63.00 | 98.44 +/- 0.00 51 | 44.00 | 61.72 +/- 0.00 52 | 66.00 | 98.38 +/- 0.00 53 | 81.00 | 93.92 +/- 0.00 54 | 144.00 | 112.17 +/- 0.00 55 | 137.00 | 112.18 +/- 0.00 56 | 88.00 | 82.71 +/- 0.00 57 | 100.00 | 111.87 +/- 0.00 58 | 69.00 | 75.32 +/- 0.00 59 | 145.00 | 98.27 +/- 0.00 60 | 92.00 | 94.27 +/- 0.00 61 | 78.00 | 104.88 +/- 0.00 62 | 18.00 | 56.35 +/- 0.00 63 | 56.00 | 112.19 +/- 0.00 64 | 129.00 | 112.19 +/- 0.00 65 | 115.00 | 101.42 +/- 0.00 66 | 117.00 | 110.80 +/- 0.00 67 | 120.00 | 103.52 +/- 0.00 68 | 41.00 | 56.69 +/- 0.00 69 | 133.00 | 112.22 +/- 0.00 70 | 41.00 | 104.93 +/- 0.00 71 | 6.00 | 56.14 +/- 0.00 72 | 7.00 | 56.28 +/- 0.00 73 | 18.00 | 57.95 +/- 0.00 74 | 51.00 | 60.87 +/- 0.00 75 | 55.00 | 64.68 +/- 0.00 76 | 71.00 | 75.89 +/- 0.00 77 | 101.00 | 112.20 +/- 0.00 78 | 58.00 | 102.27 +/- 0.00 79 | 31.00 | 57.04 +/- 0.00 80 | 79.00 | 92.65 +/- 0.00 81 | 9.00 | 56.11 +/- 0.00 82 | 17.00 | 56.12 +/- 0.00 83 | 111.00 | 112.03 +/- 0.00 84 | 49.00 | 58.92 +/- 0.00 85 | 56.00 | 104.70 +/- 0.00 86 | 104.00 | 108.28 +/- 0.00 87 | 113.00 | 59.73 +/- 0.00 88 | 135.00 | 112.20 +/- 0.00 89 | 117.00 | 112.18 +/- 0.00 90 | 87.00 | 110.57 +/- 0.00 91 | 15.00 | 60.77 +/- 0.00 92 | 25.00 | 56.14 +/- 0.00 93 | 55.00 | 59.84 +/- 0.00 94 | 131.00 | 112.14 +/- 0.00 95 | 55.00 | 59.47 +/- 0.00 96 | 45.00 | 88.98 +/- 0.00 97 | 11.00 | 69.85 +/- 0.00 98 | 103.00 | 112.20 +/- 0.00 99 | 77.00 | 68.08 +/- 0.00 100 | 55.00 | 81.09 +/- 0.00 101 | 10.00 | 56.11 +/- 0.00 102 | 56.00 | 75.53 +/- 0.00 103 | 127.00 | 111.05 +/- 0.00 104 | 14.00 | 56.12 +/- 0.00 105 | 68.00 | 79.22 +/- 0.00 106 | 88.00 | 105.45 +/- 0.00 107 | 87.00 | 110.06 +/- 0.00 108 | 8.00 | 56.11 +/- 0.00 109 | 22.00 | 66.29 +/- 0.00 110 | 55.00 | 70.89 +/- 0.00 111 | 87.00 | 103.04 +/- 0.00 112 | 85.00 | 111.84 +/- 0.00 113 | 78.00 | 61.65 +/- 0.00 114 | 108.00 | 112.03 +/- 0.00 115 | 144.00 | 112.06 +/- 0.00 116 | 119.00 | 112.10 +/- 0.00 117 | 99.00 | 110.27 +/- 0.00 118 | 145.00 | 111.77 +/- 0.00 119 | 87.00 | 75.44 +/- 0.00 120 | 28.00 | 56.48 +/- 0.00 121 | 136.00 | 110.24 +/- 0.00 122 | 27.00 | 56.17 +/- 0.00 123 | 132.00 | 112.06 +/- 0.00 124 | 28.00 | 57.51 +/- 0.00 125 | 35.00 | 56.17 +/- 0.00 126 | eval mean loss: 416.59 127 | eval rmse: 28.86 128 | eval mae: 24.15 129 | eval score: 3631.10 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 22.35 134 | eval time: 0:00:01.370087 135 | **** end time: 2019-10-01 02:05:20.206984 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_8/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_8', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_8/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8277 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-10-01 02:05:27.403897 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:05:27.408995 25 | ground truth | pred +/- std: 26 | 67.00 | 111.02 +/- 0.00 27 | 115.00 | 112.11 +/- 0.00 28 | 93.00 | 80.08 +/- 0.00 29 | 123.00 | 112.04 +/- 0.00 30 | 8.00 | 56.12 +/- 0.00 31 | 86.00 | 72.74 +/- 0.00 32 | 128.00 | 112.20 +/- 0.00 33 | 40.00 | 84.22 +/- 0.00 34 | 71.00 | 83.79 +/- 0.00 35 | 58.00 | 64.31 +/- 0.00 36 | 128.00 | 98.82 +/- 0.00 37 | 65.00 | 90.07 +/- 0.00 38 | 51.00 | 82.17 +/- 0.00 39 | 27.00 | 78.51 +/- 0.00 40 | 124.00 | 112.02 +/- 0.00 41 | 120.00 | 111.91 +/- 0.00 42 | 137.00 | 110.24 +/- 0.00 43 | 99.00 | 103.70 +/- 0.00 44 | 20.00 | 56.13 +/- 0.00 45 | 11.00 | 56.30 +/- 0.00 46 | 45.00 | 64.91 +/- 0.00 47 | 115.00 | 96.68 +/- 0.00 48 | 115.00 | 83.26 +/- 0.00 49 | 89.00 | 88.29 +/- 0.00 50 | 63.00 | 98.33 +/- 0.00 51 | 44.00 | 61.63 +/- 0.00 52 | 66.00 | 98.31 +/- 0.00 53 | 81.00 | 93.98 +/- 0.00 54 | 144.00 | 112.18 +/- 0.00 55 | 137.00 | 112.20 +/- 0.00 56 | 88.00 | 82.63 +/- 0.00 57 | 100.00 | 111.96 +/- 0.00 58 | 69.00 | 75.13 +/- 0.00 59 | 145.00 | 98.37 +/- 0.00 60 | 92.00 | 93.58 +/- 0.00 61 | 78.00 | 104.71 +/- 0.00 62 | 18.00 | 56.35 +/- 0.00 63 | 56.00 | 112.21 +/- 0.00 64 | 129.00 | 112.21 +/- 0.00 65 | 115.00 | 101.24 +/- 0.00 66 | 117.00 | 110.86 +/- 0.00 67 | 120.00 | 103.61 +/- 0.00 68 | 41.00 | 56.68 +/- 0.00 69 | 133.00 | 112.23 +/- 0.00 70 | 41.00 | 104.93 +/- 0.00 71 | 6.00 | 56.14 +/- 0.00 72 | 7.00 | 56.29 +/- 0.00 73 | 18.00 | 57.91 +/- 0.00 74 | 51.00 | 60.94 +/- 0.00 75 | 55.00 | 64.76 +/- 0.00 76 | 71.00 | 75.99 +/- 0.00 77 | 101.00 | 112.22 +/- 0.00 78 | 58.00 | 102.51 +/- 0.00 79 | 31.00 | 57.04 +/- 0.00 80 | 79.00 | 92.16 +/- 0.00 81 | 9.00 | 56.12 +/- 0.00 82 | 17.00 | 56.13 +/- 0.00 83 | 111.00 | 112.08 +/- 0.00 84 | 49.00 | 58.88 +/- 0.00 85 | 56.00 | 104.50 +/- 0.00 86 | 104.00 | 108.25 +/- 0.00 87 | 113.00 | 59.78 +/- 0.00 88 | 135.00 | 112.21 +/- 0.00 89 | 117.00 | 112.20 +/- 0.00 90 | 87.00 | 110.62 +/- 0.00 91 | 15.00 | 60.70 +/- 0.00 92 | 25.00 | 56.15 +/- 0.00 93 | 55.00 | 59.85 +/- 0.00 94 | 131.00 | 112.17 +/- 0.00 95 | 55.00 | 59.48 +/- 0.00 96 | 45.00 | 88.56 +/- 0.00 97 | 11.00 | 69.73 +/- 0.00 98 | 103.00 | 112.21 +/- 0.00 99 | 77.00 | 68.07 +/- 0.00 100 | 55.00 | 80.95 +/- 0.00 101 | 10.00 | 56.12 +/- 0.00 102 | 56.00 | 75.41 +/- 0.00 103 | 127.00 | 111.18 +/- 0.00 104 | 14.00 | 56.13 +/- 0.00 105 | 68.00 | 79.34 +/- 0.00 106 | 88.00 | 105.24 +/- 0.00 107 | 87.00 | 110.22 +/- 0.00 108 | 8.00 | 56.12 +/- 0.00 109 | 22.00 | 66.23 +/- 0.00 110 | 55.00 | 70.99 +/- 0.00 111 | 87.00 | 103.15 +/- 0.00 112 | 85.00 | 111.91 +/- 0.00 113 | 78.00 | 61.75 +/- 0.00 114 | 108.00 | 112.09 +/- 0.00 115 | 144.00 | 112.08 +/- 0.00 116 | 119.00 | 112.13 +/- 0.00 117 | 99.00 | 110.38 +/- 0.00 118 | 145.00 | 111.89 +/- 0.00 119 | 87.00 | 75.78 +/- 0.00 120 | 28.00 | 56.49 +/- 0.00 121 | 136.00 | 110.32 +/- 0.00 122 | 27.00 | 56.17 +/- 0.00 123 | 132.00 | 112.10 +/- 0.00 124 | 28.00 | 57.22 +/- 0.00 125 | 35.00 | 56.18 +/- 0.00 126 | eval mean loss: 415.95 127 | eval rmse: 28.84 128 | eval mae: 24.13 129 | eval score: 3623.35 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 22.36 134 | eval time: 0:00:01.365690 135 | **** end time: 2019-10-01 02:05:28.774920 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_9/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_9', model='frequentist_conv2_pool2', model_path='log/CMAPSS/FD003/min-max/frequentist_conv2_pool2/frequentist_conv2_pool2_9/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8299 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistConv2Pool2... 6 | Done. 7 | **** start time: 2019-10-01 02:05:35.997354 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Conv2d-1 [-1, 8, 26, 1] 560 12 | Sigmoid-2 [-1, 8, 26, 1] 0 13 | AvgPool2d-3 [-1, 8, 13, 1] 0 14 | Conv2d-4 [-1, 14, 12, 1] 224 15 | Sigmoid-5 [-1, 14, 12, 1] 0 16 | AvgPool2d-6 [-1, 14, 6, 1] 0 17 | Flatten-7 [-1, 84] 0 18 | Linear-8 [-1, 1] 84 19 | ================================================================ 20 | Total params: 868 21 | Trainable params: 868 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:05:36.002319 25 | ground truth | pred +/- std: 26 | 67.00 | 110.98 +/- 0.00 27 | 115.00 | 112.04 +/- 0.00 28 | 93.00 | 80.93 +/- 0.00 29 | 123.00 | 111.94 +/- 0.00 30 | 8.00 | 56.11 +/- 0.00 31 | 86.00 | 72.80 +/- 0.00 32 | 128.00 | 112.17 +/- 0.00 33 | 40.00 | 83.35 +/- 0.00 34 | 71.00 | 83.32 +/- 0.00 35 | 58.00 | 64.54 +/- 0.00 36 | 128.00 | 98.16 +/- 0.00 37 | 65.00 | 90.98 +/- 0.00 38 | 51.00 | 81.64 +/- 0.00 39 | 27.00 | 78.76 +/- 0.00 40 | 124.00 | 111.86 +/- 0.00 41 | 120.00 | 111.82 +/- 0.00 42 | 137.00 | 110.23 +/- 0.00 43 | 99.00 | 103.20 +/- 0.00 44 | 20.00 | 56.12 +/- 0.00 45 | 11.00 | 56.28 +/- 0.00 46 | 45.00 | 64.50 +/- 0.00 47 | 115.00 | 97.09 +/- 0.00 48 | 115.00 | 82.98 +/- 0.00 49 | 89.00 | 88.77 +/- 0.00 50 | 63.00 | 98.31 +/- 0.00 51 | 44.00 | 61.67 +/- 0.00 52 | 66.00 | 97.57 +/- 0.00 53 | 81.00 | 93.54 +/- 0.00 54 | 144.00 | 112.16 +/- 0.00 55 | 137.00 | 112.17 +/- 0.00 56 | 88.00 | 82.63 +/- 0.00 57 | 100.00 | 111.80 +/- 0.00 58 | 69.00 | 75.55 +/- 0.00 59 | 145.00 | 98.26 +/- 0.00 60 | 92.00 | 95.45 +/- 0.00 61 | 78.00 | 104.99 +/- 0.00 62 | 18.00 | 56.33 +/- 0.00 63 | 56.00 | 112.17 +/- 0.00 64 | 129.00 | 112.19 +/- 0.00 65 | 115.00 | 101.22 +/- 0.00 66 | 117.00 | 110.93 +/- 0.00 67 | 120.00 | 103.75 +/- 0.00 68 | 41.00 | 56.62 +/- 0.00 69 | 133.00 | 112.21 +/- 0.00 70 | 41.00 | 104.41 +/- 0.00 71 | 6.00 | 56.13 +/- 0.00 72 | 7.00 | 56.28 +/- 0.00 73 | 18.00 | 57.98 +/- 0.00 74 | 51.00 | 60.64 +/- 0.00 75 | 55.00 | 64.69 +/- 0.00 76 | 71.00 | 76.36 +/- 0.00 77 | 101.00 | 112.19 +/- 0.00 78 | 58.00 | 101.71 +/- 0.00 79 | 31.00 | 57.00 +/- 0.00 80 | 79.00 | 92.62 +/- 0.00 81 | 9.00 | 56.11 +/- 0.00 82 | 17.00 | 56.12 +/- 0.00 83 | 111.00 | 112.01 +/- 0.00 84 | 49.00 | 58.87 +/- 0.00 85 | 56.00 | 104.84 +/- 0.00 86 | 104.00 | 108.17 +/- 0.00 87 | 113.00 | 59.67 +/- 0.00 88 | 135.00 | 112.19 +/- 0.00 89 | 117.00 | 112.17 +/- 0.00 90 | 87.00 | 110.65 +/- 0.00 91 | 15.00 | 60.75 +/- 0.00 92 | 25.00 | 56.14 +/- 0.00 93 | 55.00 | 59.84 +/- 0.00 94 | 131.00 | 112.12 +/- 0.00 95 | 55.00 | 59.32 +/- 0.00 96 | 45.00 | 89.22 +/- 0.00 97 | 11.00 | 70.37 +/- 0.00 98 | 103.00 | 112.19 +/- 0.00 99 | 77.00 | 68.30 +/- 0.00 100 | 55.00 | 81.57 +/- 0.00 101 | 10.00 | 56.11 +/- 0.00 102 | 56.00 | 75.95 +/- 0.00 103 | 127.00 | 111.00 +/- 0.00 104 | 14.00 | 56.11 +/- 0.00 105 | 68.00 | 78.11 +/- 0.00 106 | 88.00 | 106.43 +/- 0.00 107 | 87.00 | 109.89 +/- 0.00 108 | 8.00 | 56.11 +/- 0.00 109 | 22.00 | 66.18 +/- 0.00 110 | 55.00 | 71.24 +/- 0.00 111 | 87.00 | 102.85 +/- 0.00 112 | 85.00 | 111.79 +/- 0.00 113 | 78.00 | 61.29 +/- 0.00 114 | 108.00 | 111.99 +/- 0.00 115 | 144.00 | 112.07 +/- 0.00 116 | 119.00 | 112.11 +/- 0.00 117 | 99.00 | 110.19 +/- 0.00 118 | 145.00 | 111.67 +/- 0.00 119 | 87.00 | 74.62 +/- 0.00 120 | 28.00 | 56.45 +/- 0.00 121 | 136.00 | 110.31 +/- 0.00 122 | 27.00 | 56.17 +/- 0.00 123 | 132.00 | 112.03 +/- 0.00 124 | 28.00 | 57.84 +/- 0.00 125 | 35.00 | 56.17 +/- 0.00 126 | eval mean loss: 416.48 127 | eval rmse: 28.86 128 | eval mae: 24.16 129 | eval score: 3616.47 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 22.34 134 | eval time: 0:00:01.386883 135 | **** end time: 2019-10-01 02:05:37.389403 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_0/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_0', model='frequentist_dense3', model_path='log/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_0/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8333 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-10-01 02:05:44.600611 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:05:44.603507 25 | ground truth | pred +/- std: 26 | 67.00 | 53.50 +/- 0.00 27 | 115.00 | 125.43 +/- 0.00 28 | 93.00 | 91.12 +/- 0.00 29 | 123.00 | 125.59 +/- 0.00 30 | 8.00 | 8.36 +/- 0.00 31 | 86.00 | 76.12 +/- 0.00 32 | 128.00 | 113.78 +/- 0.00 33 | 40.00 | 46.57 +/- 0.00 34 | 71.00 | 101.30 +/- 0.00 35 | 58.00 | 55.51 +/- 0.00 36 | 128.00 | 115.84 +/- 0.00 37 | 65.00 | 111.07 +/- 0.00 38 | 51.00 | 53.68 +/- 0.00 39 | 27.00 | 30.43 +/- 0.00 40 | 124.00 | 120.34 +/- 0.00 41 | 120.00 | 116.84 +/- 0.00 42 | 137.00 | 119.79 +/- 0.00 43 | 99.00 | 105.09 +/- 0.00 44 | 20.00 | 20.04 +/- 0.00 45 | 11.00 | 14.87 +/- 0.00 46 | 45.00 | 50.05 +/- 0.00 47 | 115.00 | 117.43 +/- 0.00 48 | 115.00 | 103.70 +/- 0.00 49 | 89.00 | 90.13 +/- 0.00 50 | 63.00 | 81.62 +/- 0.00 51 | 44.00 | 46.11 +/- 0.00 52 | 66.00 | 79.67 +/- 0.00 53 | 81.00 | 104.19 +/- 0.00 54 | 144.00 | 124.47 +/- 0.00 55 | 137.00 | 119.97 +/- 0.00 56 | 88.00 | 93.74 +/- 0.00 57 | 100.00 | 124.13 +/- 0.00 58 | 69.00 | 89.98 +/- 0.00 59 | 145.00 | 123.57 +/- 0.00 60 | 92.00 | 116.33 +/- 0.00 61 | 78.00 | 78.84 +/- 0.00 62 | 18.00 | 16.24 +/- 0.00 63 | 56.00 | 76.35 +/- 0.00 64 | 129.00 | 125.56 +/- 0.00 65 | 115.00 | 115.83 +/- 0.00 66 | 117.00 | 123.46 +/- 0.00 67 | 120.00 | 124.67 +/- 0.00 68 | 41.00 | 40.64 +/- 0.00 69 | 133.00 | 125.63 +/- 0.00 70 | 41.00 | 46.57 +/- 0.00 71 | 6.00 | 7.95 +/- 0.00 72 | 7.00 | 8.58 +/- 0.00 73 | 18.00 | 21.06 +/- 0.00 74 | 51.00 | 59.12 +/- 0.00 75 | 55.00 | 59.55 +/- 0.00 76 | 71.00 | 51.10 +/- 0.00 77 | 101.00 | 107.07 +/- 0.00 78 | 58.00 | 44.62 +/- 0.00 79 | 31.00 | 29.68 +/- 0.00 80 | 79.00 | 88.68 +/- 0.00 81 | 9.00 | 10.95 +/- 0.00 82 | 17.00 | 17.37 +/- 0.00 83 | 111.00 | 124.43 +/- 0.00 84 | 49.00 | 46.84 +/- 0.00 85 | 56.00 | 99.38 +/- 0.00 86 | 104.00 | 108.88 +/- 0.00 87 | 113.00 | 98.59 +/- 0.00 88 | 135.00 | 113.09 +/- 0.00 89 | 117.00 | 122.93 +/- 0.00 90 | 87.00 | 108.36 +/- 0.00 91 | 15.00 | 16.44 +/- 0.00 92 | 25.00 | 26.97 +/- 0.00 93 | 55.00 | 63.77 +/- 0.00 94 | 131.00 | 122.51 +/- 0.00 95 | 55.00 | 44.12 +/- 0.00 96 | 45.00 | 54.82 +/- 0.00 97 | 11.00 | 7.67 +/- 0.00 98 | 103.00 | 66.59 +/- 0.00 99 | 77.00 | 60.64 +/- 0.00 100 | 55.00 | 71.95 +/- 0.00 101 | 10.00 | 5.33 +/- 0.00 102 | 56.00 | 55.56 +/- 0.00 103 | 127.00 | 107.82 +/- 0.00 104 | 14.00 | 12.72 +/- 0.00 105 | 68.00 | 51.67 +/- 0.00 106 | 88.00 | 116.75 +/- 0.00 107 | 87.00 | 122.65 +/- 0.00 108 | 8.00 | 5.77 +/- 0.00 109 | 22.00 | 31.00 +/- 0.00 110 | 55.00 | 50.03 +/- 0.00 111 | 87.00 | 115.35 +/- 0.00 112 | 85.00 | 119.48 +/- 0.00 113 | 78.00 | 84.09 +/- 0.00 114 | 108.00 | 121.75 +/- 0.00 115 | 144.00 | 122.95 +/- 0.00 116 | 119.00 | 123.96 +/- 0.00 117 | 99.00 | 120.56 +/- 0.00 118 | 145.00 | 122.90 +/- 0.00 119 | 87.00 | 101.71 +/- 0.00 120 | 28.00 | 25.03 +/- 0.00 121 | 136.00 | 117.72 +/- 0.00 122 | 27.00 | 29.27 +/- 0.00 123 | 132.00 | 122.10 +/- 0.00 124 | 28.00 | 22.56 +/- 0.00 125 | 35.00 | 34.07 +/- 0.00 126 | eval mean loss: 111.84 127 | eval rmse: 14.96 128 | eval mae: 10.96 129 | eval score: 472.11 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 40.69 134 | eval time: 0:00:01.469608 135 | **** end time: 2019-10-01 02:05:46.073316 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_1/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_1', model='frequentist_dense3', model_path='log/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_1/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8353 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-10-01 02:05:53.331058 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:05:53.334211 25 | ground truth | pred +/- std: 26 | 67.00 | 66.81 +/- 0.00 27 | 115.00 | 125.42 +/- 0.00 28 | 93.00 | 85.85 +/- 0.00 29 | 123.00 | 125.35 +/- 0.00 30 | 8.00 | 8.32 +/- 0.00 31 | 86.00 | 74.13 +/- 0.00 32 | 128.00 | 112.89 +/- 0.00 33 | 40.00 | 39.22 +/- 0.00 34 | 71.00 | 94.90 +/- 0.00 35 | 58.00 | 54.76 +/- 0.00 36 | 128.00 | 115.25 +/- 0.00 37 | 65.00 | 109.73 +/- 0.00 38 | 51.00 | 63.44 +/- 0.00 39 | 27.00 | 25.92 +/- 0.00 40 | 124.00 | 117.96 +/- 0.00 41 | 120.00 | 114.77 +/- 0.00 42 | 137.00 | 121.27 +/- 0.00 43 | 99.00 | 101.22 +/- 0.00 44 | 20.00 | 21.72 +/- 0.00 45 | 11.00 | 16.57 +/- 0.00 46 | 45.00 | 45.40 +/- 0.00 47 | 115.00 | 119.27 +/- 0.00 48 | 115.00 | 105.90 +/- 0.00 49 | 89.00 | 85.45 +/- 0.00 50 | 63.00 | 82.87 +/- 0.00 51 | 44.00 | 46.81 +/- 0.00 52 | 66.00 | 76.11 +/- 0.00 53 | 81.00 | 104.05 +/- 0.00 54 | 144.00 | 125.14 +/- 0.00 55 | 137.00 | 120.31 +/- 0.00 56 | 88.00 | 93.75 +/- 0.00 57 | 100.00 | 122.58 +/- 0.00 58 | 69.00 | 82.72 +/- 0.00 59 | 145.00 | 122.25 +/- 0.00 60 | 92.00 | 115.50 +/- 0.00 61 | 78.00 | 77.63 +/- 0.00 62 | 18.00 | 15.28 +/- 0.00 63 | 56.00 | 66.02 +/- 0.00 64 | 129.00 | 125.65 +/- 0.00 65 | 115.00 | 115.33 +/- 0.00 66 | 117.00 | 121.66 +/- 0.00 67 | 120.00 | 121.76 +/- 0.00 68 | 41.00 | 42.36 +/- 0.00 69 | 133.00 | 125.67 +/- 0.00 70 | 41.00 | 49.74 +/- 0.00 71 | 6.00 | 7.84 +/- 0.00 72 | 7.00 | 10.89 +/- 0.00 73 | 18.00 | 23.18 +/- 0.00 74 | 51.00 | 61.66 +/- 0.00 75 | 55.00 | 57.60 +/- 0.00 76 | 71.00 | 45.80 +/- 0.00 77 | 101.00 | 111.67 +/- 0.00 78 | 58.00 | 46.50 +/- 0.00 79 | 31.00 | 28.78 +/- 0.00 80 | 79.00 | 79.87 +/- 0.00 81 | 9.00 | 8.89 +/- 0.00 82 | 17.00 | 19.64 +/- 0.00 83 | 111.00 | 123.82 +/- 0.00 84 | 49.00 | 50.72 +/- 0.00 85 | 56.00 | 97.93 +/- 0.00 86 | 104.00 | 105.51 +/- 0.00 87 | 113.00 | 94.71 +/- 0.00 88 | 135.00 | 106.08 +/- 0.00 89 | 117.00 | 124.54 +/- 0.00 90 | 87.00 | 110.13 +/- 0.00 91 | 15.00 | 16.11 +/- 0.00 92 | 25.00 | 23.84 +/- 0.00 93 | 55.00 | 63.68 +/- 0.00 94 | 131.00 | 122.64 +/- 0.00 95 | 55.00 | 44.88 +/- 0.00 96 | 45.00 | 47.80 +/- 0.00 97 | 11.00 | 8.73 +/- 0.00 98 | 103.00 | 81.94 +/- 0.00 99 | 77.00 | 65.70 +/- 0.00 100 | 55.00 | 65.53 +/- 0.00 101 | 10.00 | 5.41 +/- 0.00 102 | 56.00 | 51.06 +/- 0.00 103 | 127.00 | 110.74 +/- 0.00 104 | 14.00 | 11.03 +/- 0.00 105 | 68.00 | 58.48 +/- 0.00 106 | 88.00 | 112.46 +/- 0.00 107 | 87.00 | 121.32 +/- 0.00 108 | 8.00 | 6.48 +/- 0.00 109 | 22.00 | 29.33 +/- 0.00 110 | 55.00 | 50.97 +/- 0.00 111 | 87.00 | 115.37 +/- 0.00 112 | 85.00 | 120.54 +/- 0.00 113 | 78.00 | 84.26 +/- 0.00 114 | 108.00 | 119.04 +/- 0.00 115 | 144.00 | 123.73 +/- 0.00 116 | 119.00 | 121.57 +/- 0.00 117 | 99.00 | 120.95 +/- 0.00 118 | 145.00 | 123.20 +/- 0.00 119 | 87.00 | 109.04 +/- 0.00 120 | 28.00 | 26.18 +/- 0.00 121 | 136.00 | 116.52 +/- 0.00 122 | 27.00 | 32.70 +/- 0.00 123 | 132.00 | 118.21 +/- 0.00 124 | 28.00 | 23.30 +/- 0.00 125 | 35.00 | 34.18 +/- 0.00 126 | eval mean loss: 101.59 127 | eval rmse: 14.25 128 | eval mae: 10.37 129 | eval score: 414.94 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 40.30 134 | eval time: 0:00:01.460912 135 | **** end time: 2019-10-01 02:05:54.795331 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_2/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_2', model='frequentist_dense3', model_path='log/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_2/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8374 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-10-01 02:06:02.103924 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:06:02.106961 25 | ground truth | pred +/- std: 26 | 67.00 | 58.35 +/- 0.00 27 | 115.00 | 125.51 +/- 0.00 28 | 93.00 | 90.39 +/- 0.00 29 | 123.00 | 125.68 +/- 0.00 30 | 8.00 | 9.20 +/- 0.00 31 | 86.00 | 75.13 +/- 0.00 32 | 128.00 | 111.36 +/- 0.00 33 | 40.00 | 44.13 +/- 0.00 34 | 71.00 | 94.42 +/- 0.00 35 | 58.00 | 53.82 +/- 0.00 36 | 128.00 | 116.77 +/- 0.00 37 | 65.00 | 107.71 +/- 0.00 38 | 51.00 | 62.39 +/- 0.00 39 | 27.00 | 26.49 +/- 0.00 40 | 124.00 | 117.31 +/- 0.00 41 | 120.00 | 117.83 +/- 0.00 42 | 137.00 | 121.27 +/- 0.00 43 | 99.00 | 101.20 +/- 0.00 44 | 20.00 | 22.83 +/- 0.00 45 | 11.00 | 15.72 +/- 0.00 46 | 45.00 | 46.16 +/- 0.00 47 | 115.00 | 118.97 +/- 0.00 48 | 115.00 | 110.26 +/- 0.00 49 | 89.00 | 86.69 +/- 0.00 50 | 63.00 | 82.93 +/- 0.00 51 | 44.00 | 45.03 +/- 0.00 52 | 66.00 | 69.78 +/- 0.00 53 | 81.00 | 103.64 +/- 0.00 54 | 144.00 | 124.74 +/- 0.00 55 | 137.00 | 114.65 +/- 0.00 56 | 88.00 | 93.74 +/- 0.00 57 | 100.00 | 122.83 +/- 0.00 58 | 69.00 | 84.49 +/- 0.00 59 | 145.00 | 123.21 +/- 0.00 60 | 92.00 | 116.84 +/- 0.00 61 | 78.00 | 78.89 +/- 0.00 62 | 18.00 | 14.32 +/- 0.00 63 | 56.00 | 63.01 +/- 0.00 64 | 129.00 | 125.82 +/- 0.00 65 | 115.00 | 114.23 +/- 0.00 66 | 117.00 | 122.36 +/- 0.00 67 | 120.00 | 122.55 +/- 0.00 68 | 41.00 | 42.07 +/- 0.00 69 | 133.00 | 125.95 +/- 0.00 70 | 41.00 | 55.21 +/- 0.00 71 | 6.00 | 6.49 +/- 0.00 72 | 7.00 | 10.07 +/- 0.00 73 | 18.00 | 23.28 +/- 0.00 74 | 51.00 | 59.66 +/- 0.00 75 | 55.00 | 58.94 +/- 0.00 76 | 71.00 | 46.03 +/- 0.00 77 | 101.00 | 110.28 +/- 0.00 78 | 58.00 | 47.59 +/- 0.00 79 | 31.00 | 28.87 +/- 0.00 80 | 79.00 | 98.53 +/- 0.00 81 | 9.00 | 8.78 +/- 0.00 82 | 17.00 | 19.59 +/- 0.00 83 | 111.00 | 124.26 +/- 0.00 84 | 49.00 | 49.59 +/- 0.00 85 | 56.00 | 94.06 +/- 0.00 86 | 104.00 | 94.66 +/- 0.00 87 | 113.00 | 98.93 +/- 0.00 88 | 135.00 | 107.37 +/- 0.00 89 | 117.00 | 122.04 +/- 0.00 90 | 87.00 | 105.36 +/- 0.00 91 | 15.00 | 14.30 +/- 0.00 92 | 25.00 | 23.82 +/- 0.00 93 | 55.00 | 62.30 +/- 0.00 94 | 131.00 | 120.30 +/- 0.00 95 | 55.00 | 43.14 +/- 0.00 96 | 45.00 | 54.29 +/- 0.00 97 | 11.00 | 9.88 +/- 0.00 98 | 103.00 | 68.50 +/- 0.00 99 | 77.00 | 63.95 +/- 0.00 100 | 55.00 | 63.55 +/- 0.00 101 | 10.00 | 7.04 +/- 0.00 102 | 56.00 | 52.59 +/- 0.00 103 | 127.00 | 109.62 +/- 0.00 104 | 14.00 | 13.47 +/- 0.00 105 | 68.00 | 53.13 +/- 0.00 106 | 88.00 | 112.20 +/- 0.00 107 | 87.00 | 120.78 +/- 0.00 108 | 8.00 | 7.41 +/- 0.00 109 | 22.00 | 30.32 +/- 0.00 110 | 55.00 | 47.92 +/- 0.00 111 | 87.00 | 112.81 +/- 0.00 112 | 85.00 | 119.91 +/- 0.00 113 | 78.00 | 86.77 +/- 0.00 114 | 108.00 | 119.70 +/- 0.00 115 | 144.00 | 122.79 +/- 0.00 116 | 119.00 | 123.01 +/- 0.00 117 | 99.00 | 119.56 +/- 0.00 118 | 145.00 | 122.91 +/- 0.00 119 | 87.00 | 108.08 +/- 0.00 120 | 28.00 | 26.17 +/- 0.00 121 | 136.00 | 118.28 +/- 0.00 122 | 27.00 | 32.60 +/- 0.00 123 | 132.00 | 122.23 +/- 0.00 124 | 28.00 | 24.13 +/- 0.00 125 | 35.00 | 33.08 +/- 0.00 126 | eval mean loss: 103.29 127 | eval rmse: 14.37 128 | eval mae: 10.62 129 | eval score: 381.80 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 40.21 134 | eval time: 0:00:01.443773 135 | **** end time: 2019-10-01 02:06:03.550984 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_3/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_3', model='frequentist_dense3', model_path='log/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_3/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8395 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-10-01 02:06:10.749330 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:06:10.752325 25 | ground truth | pred +/- std: 26 | 67.00 | 64.00 +/- 0.00 27 | 115.00 | 125.47 +/- 0.00 28 | 93.00 | 93.28 +/- 0.00 29 | 123.00 | 125.55 +/- 0.00 30 | 8.00 | 7.89 +/- 0.00 31 | 86.00 | 75.93 +/- 0.00 32 | 128.00 | 114.08 +/- 0.00 33 | 40.00 | 39.60 +/- 0.00 34 | 71.00 | 92.29 +/- 0.00 35 | 58.00 | 55.09 +/- 0.00 36 | 128.00 | 115.93 +/- 0.00 37 | 65.00 | 112.10 +/- 0.00 38 | 51.00 | 61.45 +/- 0.00 39 | 27.00 | 27.04 +/- 0.00 40 | 124.00 | 120.59 +/- 0.00 41 | 120.00 | 118.87 +/- 0.00 42 | 137.00 | 122.56 +/- 0.00 43 | 99.00 | 106.08 +/- 0.00 44 | 20.00 | 23.32 +/- 0.00 45 | 11.00 | 18.64 +/- 0.00 46 | 45.00 | 46.76 +/- 0.00 47 | 115.00 | 119.60 +/- 0.00 48 | 115.00 | 100.72 +/- 0.00 49 | 89.00 | 93.26 +/- 0.00 50 | 63.00 | 77.20 +/- 0.00 51 | 44.00 | 48.20 +/- 0.00 52 | 66.00 | 77.89 +/- 0.00 53 | 81.00 | 103.31 +/- 0.00 54 | 144.00 | 125.06 +/- 0.00 55 | 137.00 | 113.71 +/- 0.00 56 | 88.00 | 87.08 +/- 0.00 57 | 100.00 | 121.65 +/- 0.00 58 | 69.00 | 87.74 +/- 0.00 59 | 145.00 | 123.92 +/- 0.00 60 | 92.00 | 113.76 +/- 0.00 61 | 78.00 | 78.00 +/- 0.00 62 | 18.00 | 15.15 +/- 0.00 63 | 56.00 | 42.90 +/- 0.00 64 | 129.00 | 125.63 +/- 0.00 65 | 115.00 | 113.90 +/- 0.00 66 | 117.00 | 122.83 +/- 0.00 67 | 120.00 | 123.14 +/- 0.00 68 | 41.00 | 40.52 +/- 0.00 69 | 133.00 | 125.37 +/- 0.00 70 | 41.00 | 51.34 +/- 0.00 71 | 6.00 | 6.18 +/- 0.00 72 | 7.00 | 9.30 +/- 0.00 73 | 18.00 | 22.52 +/- 0.00 74 | 51.00 | 54.44 +/- 0.00 75 | 55.00 | 58.49 +/- 0.00 76 | 71.00 | 48.15 +/- 0.00 77 | 101.00 | 108.71 +/- 0.00 78 | 58.00 | 45.44 +/- 0.00 79 | 31.00 | 29.78 +/- 0.00 80 | 79.00 | 95.97 +/- 0.00 81 | 9.00 | 8.69 +/- 0.00 82 | 17.00 | 15.01 +/- 0.00 83 | 111.00 | 122.97 +/- 0.00 84 | 49.00 | 48.83 +/- 0.00 85 | 56.00 | 96.48 +/- 0.00 86 | 104.00 | 109.69 +/- 0.00 87 | 113.00 | 94.79 +/- 0.00 88 | 135.00 | 103.36 +/- 0.00 89 | 117.00 | 121.07 +/- 0.00 90 | 87.00 | 108.37 +/- 0.00 91 | 15.00 | 14.26 +/- 0.00 92 | 25.00 | 27.36 +/- 0.00 93 | 55.00 | 58.76 +/- 0.00 94 | 131.00 | 119.93 +/- 0.00 95 | 55.00 | 46.74 +/- 0.00 96 | 45.00 | 51.13 +/- 0.00 97 | 11.00 | 9.43 +/- 0.00 98 | 103.00 | 78.99 +/- 0.00 99 | 77.00 | 69.32 +/- 0.00 100 | 55.00 | 63.78 +/- 0.00 101 | 10.00 | 5.50 +/- 0.00 102 | 56.00 | 55.09 +/- 0.00 103 | 127.00 | 109.05 +/- 0.00 104 | 14.00 | 10.93 +/- 0.00 105 | 68.00 | 62.78 +/- 0.00 106 | 88.00 | 110.76 +/- 0.00 107 | 87.00 | 122.82 +/- 0.00 108 | 8.00 | 6.91 +/- 0.00 109 | 22.00 | 28.04 +/- 0.00 110 | 55.00 | 48.52 +/- 0.00 111 | 87.00 | 112.08 +/- 0.00 112 | 85.00 | 120.88 +/- 0.00 113 | 78.00 | 81.77 +/- 0.00 114 | 108.00 | 121.40 +/- 0.00 115 | 144.00 | 121.55 +/- 0.00 116 | 119.00 | 122.06 +/- 0.00 117 | 99.00 | 120.68 +/- 0.00 118 | 145.00 | 123.88 +/- 0.00 119 | 87.00 | 103.71 +/- 0.00 120 | 28.00 | 28.71 +/- 0.00 121 | 136.00 | 118.58 +/- 0.00 122 | 27.00 | 33.21 +/- 0.00 123 | 132.00 | 121.01 +/- 0.00 124 | 28.00 | 25.82 +/- 0.00 125 | 35.00 | 32.85 +/- 0.00 126 | eval mean loss: 100.30 127 | eval rmse: 14.16 128 | eval mae: 10.16 129 | eval score: 420.92 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 40.39 134 | eval time: 0:00:01.420492 135 | **** end time: 2019-10-01 02:06:12.173069 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_4/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_4', model='frequentist_dense3', model_path='log/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_4/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8416 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-10-01 02:06:19.386262 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:06:19.389157 25 | ground truth | pred +/- std: 26 | 67.00 | 65.01 +/- 0.00 27 | 115.00 | 125.44 +/- 0.00 28 | 93.00 | 91.01 +/- 0.00 29 | 123.00 | 125.68 +/- 0.00 30 | 8.00 | 8.91 +/- 0.00 31 | 86.00 | 77.11 +/- 0.00 32 | 128.00 | 112.04 +/- 0.00 33 | 40.00 | 40.67 +/- 0.00 34 | 71.00 | 91.70 +/- 0.00 35 | 58.00 | 53.59 +/- 0.00 36 | 128.00 | 115.42 +/- 0.00 37 | 65.00 | 107.79 +/- 0.00 38 | 51.00 | 59.11 +/- 0.00 39 | 27.00 | 28.38 +/- 0.00 40 | 124.00 | 118.07 +/- 0.00 41 | 120.00 | 117.30 +/- 0.00 42 | 137.00 | 121.78 +/- 0.00 43 | 99.00 | 106.68 +/- 0.00 44 | 20.00 | 25.13 +/- 0.00 45 | 11.00 | 18.09 +/- 0.00 46 | 45.00 | 47.60 +/- 0.00 47 | 115.00 | 120.41 +/- 0.00 48 | 115.00 | 105.54 +/- 0.00 49 | 89.00 | 91.58 +/- 0.00 50 | 63.00 | 78.19 +/- 0.00 51 | 44.00 | 42.34 +/- 0.00 52 | 66.00 | 75.13 +/- 0.00 53 | 81.00 | 101.58 +/- 0.00 54 | 144.00 | 124.49 +/- 0.00 55 | 137.00 | 119.70 +/- 0.00 56 | 88.00 | 90.81 +/- 0.00 57 | 100.00 | 123.50 +/- 0.00 58 | 69.00 | 85.67 +/- 0.00 59 | 145.00 | 124.12 +/- 0.00 60 | 92.00 | 112.07 +/- 0.00 61 | 78.00 | 76.79 +/- 0.00 62 | 18.00 | 14.41 +/- 0.00 63 | 56.00 | 50.33 +/- 0.00 64 | 129.00 | 125.82 +/- 0.00 65 | 115.00 | 113.81 +/- 0.00 66 | 117.00 | 121.58 +/- 0.00 67 | 120.00 | 122.85 +/- 0.00 68 | 41.00 | 39.35 +/- 0.00 69 | 133.00 | 125.88 +/- 0.00 70 | 41.00 | 52.75 +/- 0.00 71 | 6.00 | 5.76 +/- 0.00 72 | 7.00 | 7.25 +/- 0.00 73 | 18.00 | 24.75 +/- 0.00 74 | 51.00 | 60.06 +/- 0.00 75 | 55.00 | 64.24 +/- 0.00 76 | 71.00 | 43.98 +/- 0.00 77 | 101.00 | 110.90 +/- 0.00 78 | 58.00 | 41.91 +/- 0.00 79 | 31.00 | 30.07 +/- 0.00 80 | 79.00 | 100.54 +/- 0.00 81 | 9.00 | 5.82 +/- 0.00 82 | 17.00 | 18.66 +/- 0.00 83 | 111.00 | 124.03 +/- 0.00 84 | 49.00 | 53.19 +/- 0.00 85 | 56.00 | 96.03 +/- 0.00 86 | 104.00 | 114.23 +/- 0.00 87 | 113.00 | 92.99 +/- 0.00 88 | 135.00 | 103.00 +/- 0.00 89 | 117.00 | 120.83 +/- 0.00 90 | 87.00 | 107.35 +/- 0.00 91 | 15.00 | 12.95 +/- 0.00 92 | 25.00 | 26.66 +/- 0.00 93 | 55.00 | 58.16 +/- 0.00 94 | 131.00 | 119.01 +/- 0.00 95 | 55.00 | 42.74 +/- 0.00 96 | 45.00 | 54.23 +/- 0.00 97 | 11.00 | 6.53 +/- 0.00 98 | 103.00 | 77.59 +/- 0.00 99 | 77.00 | 68.41 +/- 0.00 100 | 55.00 | 71.63 +/- 0.00 101 | 10.00 | 6.06 +/- 0.00 102 | 56.00 | 53.48 +/- 0.00 103 | 127.00 | 108.40 +/- 0.00 104 | 14.00 | 11.50 +/- 0.00 105 | 68.00 | 62.85 +/- 0.00 106 | 88.00 | 114.72 +/- 0.00 107 | 87.00 | 120.79 +/- 0.00 108 | 8.00 | 8.44 +/- 0.00 109 | 22.00 | 30.32 +/- 0.00 110 | 55.00 | 50.48 +/- 0.00 111 | 87.00 | 115.18 +/- 0.00 112 | 85.00 | 119.07 +/- 0.00 113 | 78.00 | 77.16 +/- 0.00 114 | 108.00 | 119.49 +/- 0.00 115 | 144.00 | 122.61 +/- 0.00 116 | 119.00 | 123.38 +/- 0.00 117 | 99.00 | 119.30 +/- 0.00 118 | 145.00 | 123.69 +/- 0.00 119 | 87.00 | 110.32 +/- 0.00 120 | 28.00 | 28.97 +/- 0.00 121 | 136.00 | 114.20 +/- 0.00 122 | 27.00 | 34.13 +/- 0.00 123 | 132.00 | 120.61 +/- 0.00 124 | 28.00 | 25.04 +/- 0.00 125 | 35.00 | 33.13 +/- 0.00 126 | eval mean loss: 104.04 127 | eval rmse: 14.43 128 | eval mae: 10.70 129 | eval score: 391.81 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 40.36 134 | eval time: 0:00:01.407550 135 | **** end time: 2019-10-01 02:06:20.796909 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_5/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_5', model='frequentist_dense3', model_path='log/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_5/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8437 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-10-01 02:06:28.061587 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:06:28.064702 25 | ground truth | pred +/- std: 26 | 67.00 | 64.05 +/- 0.00 27 | 115.00 | 125.37 +/- 0.00 28 | 93.00 | 88.91 +/- 0.00 29 | 123.00 | 125.37 +/- 0.00 30 | 8.00 | 6.47 +/- 0.00 31 | 86.00 | 76.46 +/- 0.00 32 | 128.00 | 110.94 +/- 0.00 33 | 40.00 | 42.39 +/- 0.00 34 | 71.00 | 95.74 +/- 0.00 35 | 58.00 | 53.20 +/- 0.00 36 | 128.00 | 119.13 +/- 0.00 37 | 65.00 | 115.02 +/- 0.00 38 | 51.00 | 58.04 +/- 0.00 39 | 27.00 | 26.96 +/- 0.00 40 | 124.00 | 118.87 +/- 0.00 41 | 120.00 | 116.95 +/- 0.00 42 | 137.00 | 121.78 +/- 0.00 43 | 99.00 | 102.93 +/- 0.00 44 | 20.00 | 22.42 +/- 0.00 45 | 11.00 | 18.17 +/- 0.00 46 | 45.00 | 47.79 +/- 0.00 47 | 115.00 | 119.65 +/- 0.00 48 | 115.00 | 108.62 +/- 0.00 49 | 89.00 | 83.30 +/- 0.00 50 | 63.00 | 70.37 +/- 0.00 51 | 44.00 | 43.02 +/- 0.00 52 | 66.00 | 72.91 +/- 0.00 53 | 81.00 | 98.21 +/- 0.00 54 | 144.00 | 124.64 +/- 0.00 55 | 137.00 | 120.48 +/- 0.00 56 | 88.00 | 99.33 +/- 0.00 57 | 100.00 | 122.47 +/- 0.00 58 | 69.00 | 81.00 +/- 0.00 59 | 145.00 | 124.05 +/- 0.00 60 | 92.00 | 114.19 +/- 0.00 61 | 78.00 | 69.46 +/- 0.00 62 | 18.00 | 15.76 +/- 0.00 63 | 56.00 | 54.18 +/- 0.00 64 | 129.00 | 125.59 +/- 0.00 65 | 115.00 | 115.26 +/- 0.00 66 | 117.00 | 122.62 +/- 0.00 67 | 120.00 | 123.55 +/- 0.00 68 | 41.00 | 40.49 +/- 0.00 69 | 133.00 | 125.62 +/- 0.00 70 | 41.00 | 48.46 +/- 0.00 71 | 6.00 | 6.53 +/- 0.00 72 | 7.00 | 10.14 +/- 0.00 73 | 18.00 | 21.30 +/- 0.00 74 | 51.00 | 55.21 +/- 0.00 75 | 55.00 | 57.21 +/- 0.00 76 | 71.00 | 50.30 +/- 0.00 77 | 101.00 | 117.08 +/- 0.00 78 | 58.00 | 45.24 +/- 0.00 79 | 31.00 | 30.88 +/- 0.00 80 | 79.00 | 92.44 +/- 0.00 81 | 9.00 | 10.90 +/- 0.00 82 | 17.00 | 16.95 +/- 0.00 83 | 111.00 | 123.76 +/- 0.00 84 | 49.00 | 49.24 +/- 0.00 85 | 56.00 | 91.60 +/- 0.00 86 | 104.00 | 106.54 +/- 0.00 87 | 113.00 | 96.17 +/- 0.00 88 | 135.00 | 99.43 +/- 0.00 89 | 117.00 | 122.27 +/- 0.00 90 | 87.00 | 106.72 +/- 0.00 91 | 15.00 | 16.10 +/- 0.00 92 | 25.00 | 24.91 +/- 0.00 93 | 55.00 | 59.74 +/- 0.00 94 | 131.00 | 120.35 +/- 0.00 95 | 55.00 | 46.76 +/- 0.00 96 | 45.00 | 47.89 +/- 0.00 97 | 11.00 | 9.24 +/- 0.00 98 | 103.00 | 80.03 +/- 0.00 99 | 77.00 | 64.59 +/- 0.00 100 | 55.00 | 60.36 +/- 0.00 101 | 10.00 | 5.97 +/- 0.00 102 | 56.00 | 51.36 +/- 0.00 103 | 127.00 | 111.05 +/- 0.00 104 | 14.00 | 11.58 +/- 0.00 105 | 68.00 | 57.22 +/- 0.00 106 | 88.00 | 115.63 +/- 0.00 107 | 87.00 | 122.32 +/- 0.00 108 | 8.00 | 6.98 +/- 0.00 109 | 22.00 | 31.07 +/- 0.00 110 | 55.00 | 49.66 +/- 0.00 111 | 87.00 | 111.98 +/- 0.00 112 | 85.00 | 119.24 +/- 0.00 113 | 78.00 | 86.29 +/- 0.00 114 | 108.00 | 121.98 +/- 0.00 115 | 144.00 | 122.83 +/- 0.00 116 | 119.00 | 123.33 +/- 0.00 117 | 99.00 | 119.50 +/- 0.00 118 | 145.00 | 122.24 +/- 0.00 119 | 87.00 | 109.31 +/- 0.00 120 | 28.00 | 26.53 +/- 0.00 121 | 136.00 | 116.61 +/- 0.00 122 | 27.00 | 34.84 +/- 0.00 123 | 132.00 | 120.82 +/- 0.00 124 | 28.00 | 22.17 +/- 0.00 125 | 35.00 | 33.01 +/- 0.00 126 | eval mean loss: 98.74 127 | eval rmse: 14.05 128 | eval mae: 10.14 129 | eval score: 434.34 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 40.57 134 | eval time: 0:00:01.452353 135 | **** end time: 2019-10-01 02:06:29.517250 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_6/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_6', model='frequentist_dense3', model_path='log/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_6/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8460 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-10-01 02:06:36.740509 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:06:36.743689 25 | ground truth | pred +/- std: 26 | 67.00 | 57.24 +/- 0.00 27 | 115.00 | 125.50 +/- 0.00 28 | 93.00 | 91.47 +/- 0.00 29 | 123.00 | 125.43 +/- 0.00 30 | 8.00 | 9.01 +/- 0.00 31 | 86.00 | 75.23 +/- 0.00 32 | 128.00 | 112.29 +/- 0.00 33 | 40.00 | 39.91 +/- 0.00 34 | 71.00 | 93.67 +/- 0.00 35 | 58.00 | 53.11 +/- 0.00 36 | 128.00 | 115.09 +/- 0.00 37 | 65.00 | 113.11 +/- 0.00 38 | 51.00 | 67.88 +/- 0.00 39 | 27.00 | 23.98 +/- 0.00 40 | 124.00 | 117.63 +/- 0.00 41 | 120.00 | 114.40 +/- 0.00 42 | 137.00 | 120.95 +/- 0.00 43 | 99.00 | 102.59 +/- 0.00 44 | 20.00 | 22.88 +/- 0.00 45 | 11.00 | 17.35 +/- 0.00 46 | 45.00 | 47.00 +/- 0.00 47 | 115.00 | 120.54 +/- 0.00 48 | 115.00 | 103.34 +/- 0.00 49 | 89.00 | 84.68 +/- 0.00 50 | 63.00 | 78.73 +/- 0.00 51 | 44.00 | 39.02 +/- 0.00 52 | 66.00 | 75.69 +/- 0.00 53 | 81.00 | 103.84 +/- 0.00 54 | 144.00 | 125.14 +/- 0.00 55 | 137.00 | 118.56 +/- 0.00 56 | 88.00 | 93.25 +/- 0.00 57 | 100.00 | 122.36 +/- 0.00 58 | 69.00 | 82.28 +/- 0.00 59 | 145.00 | 123.89 +/- 0.00 60 | 92.00 | 116.60 +/- 0.00 61 | 78.00 | 76.27 +/- 0.00 62 | 18.00 | 15.34 +/- 0.00 63 | 56.00 | 78.87 +/- 0.00 64 | 129.00 | 125.71 +/- 0.00 65 | 115.00 | 115.23 +/- 0.00 66 | 117.00 | 119.95 +/- 0.00 67 | 120.00 | 122.63 +/- 0.00 68 | 41.00 | 42.42 +/- 0.00 69 | 133.00 | 125.71 +/- 0.00 70 | 41.00 | 38.47 +/- 0.00 71 | 6.00 | 7.49 +/- 0.00 72 | 7.00 | 8.84 +/- 0.00 73 | 18.00 | 22.64 +/- 0.00 74 | 51.00 | 54.06 +/- 0.00 75 | 55.00 | 53.17 +/- 0.00 76 | 71.00 | 45.61 +/- 0.00 77 | 101.00 | 118.70 +/- 0.00 78 | 58.00 | 45.60 +/- 0.00 79 | 31.00 | 28.23 +/- 0.00 80 | 79.00 | 90.29 +/- 0.00 81 | 9.00 | 10.56 +/- 0.00 82 | 17.00 | 17.96 +/- 0.00 83 | 111.00 | 125.15 +/- 0.00 84 | 49.00 | 49.82 +/- 0.00 85 | 56.00 | 95.85 +/- 0.00 86 | 104.00 | 103.26 +/- 0.00 87 | 113.00 | 95.48 +/- 0.00 88 | 135.00 | 102.63 +/- 0.00 89 | 117.00 | 123.26 +/- 0.00 90 | 87.00 | 105.87 +/- 0.00 91 | 15.00 | 17.22 +/- 0.00 92 | 25.00 | 23.19 +/- 0.00 93 | 55.00 | 64.27 +/- 0.00 94 | 131.00 | 121.88 +/- 0.00 95 | 55.00 | 52.33 +/- 0.00 96 | 45.00 | 49.28 +/- 0.00 97 | 11.00 | 11.35 +/- 0.00 98 | 103.00 | 76.67 +/- 0.00 99 | 77.00 | 62.64 +/- 0.00 100 | 55.00 | 63.72 +/- 0.00 101 | 10.00 | 5.41 +/- 0.00 102 | 56.00 | 48.53 +/- 0.00 103 | 127.00 | 108.27 +/- 0.00 104 | 14.00 | 12.50 +/- 0.00 105 | 68.00 | 54.26 +/- 0.00 106 | 88.00 | 116.18 +/- 0.00 107 | 87.00 | 122.30 +/- 0.00 108 | 8.00 | 8.15 +/- 0.00 109 | 22.00 | 28.52 +/- 0.00 110 | 55.00 | 49.43 +/- 0.00 111 | 87.00 | 116.76 +/- 0.00 112 | 85.00 | 122.63 +/- 0.00 113 | 78.00 | 88.78 +/- 0.00 114 | 108.00 | 120.40 +/- 0.00 115 | 144.00 | 122.90 +/- 0.00 116 | 119.00 | 123.22 +/- 0.00 117 | 99.00 | 121.42 +/- 0.00 118 | 145.00 | 123.61 +/- 0.00 119 | 87.00 | 107.19 +/- 0.00 120 | 28.00 | 29.03 +/- 0.00 121 | 136.00 | 119.05 +/- 0.00 122 | 27.00 | 33.32 +/- 0.00 123 | 132.00 | 119.62 +/- 0.00 124 | 28.00 | 23.54 +/- 0.00 125 | 35.00 | 31.43 +/- 0.00 126 | eval mean loss: 110.44 127 | eval rmse: 14.86 128 | eval mae: 10.90 129 | eval score: 469.85 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 40.61 134 | eval time: 0:00:01.448332 135 | **** end time: 2019-10-01 02:06:38.192233 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_7/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_7', model='frequentist_dense3', model_path='log/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_7/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8480 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-10-01 02:06:45.524055 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:06:45.527076 25 | ground truth | pred +/- std: 26 | 67.00 | 66.40 +/- 0.00 27 | 115.00 | 125.56 +/- 0.00 28 | 93.00 | 91.68 +/- 0.00 29 | 123.00 | 125.65 +/- 0.00 30 | 8.00 | 7.49 +/- 0.00 31 | 86.00 | 76.16 +/- 0.00 32 | 128.00 | 113.19 +/- 0.00 33 | 40.00 | 41.63 +/- 0.00 34 | 71.00 | 88.77 +/- 0.00 35 | 58.00 | 50.94 +/- 0.00 36 | 128.00 | 116.89 +/- 0.00 37 | 65.00 | 112.82 +/- 0.00 38 | 51.00 | 70.12 +/- 0.00 39 | 27.00 | 32.19 +/- 0.00 40 | 124.00 | 118.91 +/- 0.00 41 | 120.00 | 112.75 +/- 0.00 42 | 137.00 | 120.53 +/- 0.00 43 | 99.00 | 109.68 +/- 0.00 44 | 20.00 | 25.70 +/- 0.00 45 | 11.00 | 17.48 +/- 0.00 46 | 45.00 | 49.16 +/- 0.00 47 | 115.00 | 117.59 +/- 0.00 48 | 115.00 | 109.77 +/- 0.00 49 | 89.00 | 90.55 +/- 0.00 50 | 63.00 | 70.10 +/- 0.00 51 | 44.00 | 45.92 +/- 0.00 52 | 66.00 | 69.51 +/- 0.00 53 | 81.00 | 101.65 +/- 0.00 54 | 144.00 | 124.50 +/- 0.00 55 | 137.00 | 119.44 +/- 0.00 56 | 88.00 | 90.10 +/- 0.00 57 | 100.00 | 122.66 +/- 0.00 58 | 69.00 | 85.62 +/- 0.00 59 | 145.00 | 124.89 +/- 0.00 60 | 92.00 | 113.63 +/- 0.00 61 | 78.00 | 80.08 +/- 0.00 62 | 18.00 | 16.31 +/- 0.00 63 | 56.00 | 38.25 +/- 0.00 64 | 129.00 | 125.86 +/- 0.00 65 | 115.00 | 115.84 +/- 0.00 66 | 117.00 | 121.25 +/- 0.00 67 | 120.00 | 124.52 +/- 0.00 68 | 41.00 | 40.92 +/- 0.00 69 | 133.00 | 125.53 +/- 0.00 70 | 41.00 | 53.02 +/- 0.00 71 | 6.00 | 7.15 +/- 0.00 72 | 7.00 | 7.79 +/- 0.00 73 | 18.00 | 24.48 +/- 0.00 74 | 51.00 | 56.23 +/- 0.00 75 | 55.00 | 60.53 +/- 0.00 76 | 71.00 | 44.81 +/- 0.00 77 | 101.00 | 111.91 +/- 0.00 78 | 58.00 | 43.89 +/- 0.00 79 | 31.00 | 29.02 +/- 0.00 80 | 79.00 | 85.64 +/- 0.00 81 | 9.00 | 9.05 +/- 0.00 82 | 17.00 | 15.27 +/- 0.00 83 | 111.00 | 123.17 +/- 0.00 84 | 49.00 | 51.86 +/- 0.00 85 | 56.00 | 95.16 +/- 0.00 86 | 104.00 | 106.39 +/- 0.00 87 | 113.00 | 96.30 +/- 0.00 88 | 135.00 | 109.01 +/- 0.00 89 | 117.00 | 122.05 +/- 0.00 90 | 87.00 | 104.28 +/- 0.00 91 | 15.00 | 16.65 +/- 0.00 92 | 25.00 | 26.45 +/- 0.00 93 | 55.00 | 59.52 +/- 0.00 94 | 131.00 | 121.16 +/- 0.00 95 | 55.00 | 47.94 +/- 0.00 96 | 45.00 | 51.29 +/- 0.00 97 | 11.00 | 6.02 +/- 0.00 98 | 103.00 | 83.85 +/- 0.00 99 | 77.00 | 69.03 +/- 0.00 100 | 55.00 | 72.50 +/- 0.00 101 | 10.00 | 7.44 +/- 0.00 102 | 56.00 | 49.62 +/- 0.00 103 | 127.00 | 107.98 +/- 0.00 104 | 14.00 | 13.56 +/- 0.00 105 | 68.00 | 63.43 +/- 0.00 106 | 88.00 | 115.03 +/- 0.00 107 | 87.00 | 123.40 +/- 0.00 108 | 8.00 | 6.33 +/- 0.00 109 | 22.00 | 29.20 +/- 0.00 110 | 55.00 | 47.61 +/- 0.00 111 | 87.00 | 114.42 +/- 0.00 112 | 85.00 | 120.63 +/- 0.00 113 | 78.00 | 86.10 +/- 0.00 114 | 108.00 | 120.89 +/- 0.00 115 | 144.00 | 121.94 +/- 0.00 116 | 119.00 | 123.72 +/- 0.00 117 | 99.00 | 117.58 +/- 0.00 118 | 145.00 | 123.66 +/- 0.00 119 | 87.00 | 102.70 +/- 0.00 120 | 28.00 | 28.87 +/- 0.00 121 | 136.00 | 116.32 +/- 0.00 122 | 27.00 | 29.75 +/- 0.00 123 | 132.00 | 121.18 +/- 0.00 124 | 28.00 | 22.52 +/- 0.00 125 | 35.00 | 31.38 +/- 0.00 126 | eval mean loss: 98.42 127 | eval rmse: 14.03 128 | eval mae: 10.28 129 | eval score: 422.34 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 40.43 134 | eval time: 0:00:01.442672 135 | **** end time: 2019-10-01 02:06:46.969973 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_8/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_8', model='frequentist_dense3', model_path='log/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_8/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8514 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-10-01 02:06:54.172423 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:06:54.175438 25 | ground truth | pred +/- std: 26 | 67.00 | 59.24 +/- 0.00 27 | 115.00 | 125.39 +/- 0.00 28 | 93.00 | 90.94 +/- 0.00 29 | 123.00 | 125.65 +/- 0.00 30 | 8.00 | 8.72 +/- 0.00 31 | 86.00 | 72.86 +/- 0.00 32 | 128.00 | 110.86 +/- 0.00 33 | 40.00 | 41.61 +/- 0.00 34 | 71.00 | 88.89 +/- 0.00 35 | 58.00 | 54.37 +/- 0.00 36 | 128.00 | 116.18 +/- 0.00 37 | 65.00 | 112.71 +/- 0.00 38 | 51.00 | 61.73 +/- 0.00 39 | 27.00 | 26.42 +/- 0.00 40 | 124.00 | 118.01 +/- 0.00 41 | 120.00 | 114.93 +/- 0.00 42 | 137.00 | 122.01 +/- 0.00 43 | 99.00 | 107.99 +/- 0.00 44 | 20.00 | 26.51 +/- 0.00 45 | 11.00 | 16.28 +/- 0.00 46 | 45.00 | 44.10 +/- 0.00 47 | 115.00 | 117.21 +/- 0.00 48 | 115.00 | 105.53 +/- 0.00 49 | 89.00 | 88.97 +/- 0.00 50 | 63.00 | 73.19 +/- 0.00 51 | 44.00 | 40.41 +/- 0.00 52 | 66.00 | 69.79 +/- 0.00 53 | 81.00 | 99.61 +/- 0.00 54 | 144.00 | 124.10 +/- 0.00 55 | 137.00 | 121.93 +/- 0.00 56 | 88.00 | 96.86 +/- 0.00 57 | 100.00 | 122.72 +/- 0.00 58 | 69.00 | 79.37 +/- 0.00 59 | 145.00 | 123.82 +/- 0.00 60 | 92.00 | 114.96 +/- 0.00 61 | 78.00 | 76.39 +/- 0.00 62 | 18.00 | 13.84 +/- 0.00 63 | 56.00 | 56.13 +/- 0.00 64 | 129.00 | 125.79 +/- 0.00 65 | 115.00 | 114.66 +/- 0.00 66 | 117.00 | 120.94 +/- 0.00 67 | 120.00 | 124.44 +/- 0.00 68 | 41.00 | 41.50 +/- 0.00 69 | 133.00 | 125.85 +/- 0.00 70 | 41.00 | 54.67 +/- 0.00 71 | 6.00 | 6.04 +/- 0.00 72 | 7.00 | 8.05 +/- 0.00 73 | 18.00 | 24.17 +/- 0.00 74 | 51.00 | 53.27 +/- 0.00 75 | 55.00 | 59.45 +/- 0.00 76 | 71.00 | 56.93 +/- 0.00 77 | 101.00 | 108.00 +/- 0.00 78 | 58.00 | 47.04 +/- 0.00 79 | 31.00 | 30.25 +/- 0.00 80 | 79.00 | 93.81 +/- 0.00 81 | 9.00 | 9.24 +/- 0.00 82 | 17.00 | 18.72 +/- 0.00 83 | 111.00 | 124.08 +/- 0.00 84 | 49.00 | 49.27 +/- 0.00 85 | 56.00 | 93.79 +/- 0.00 86 | 104.00 | 110.79 +/- 0.00 87 | 113.00 | 98.02 +/- 0.00 88 | 135.00 | 100.18 +/- 0.00 89 | 117.00 | 121.54 +/- 0.00 90 | 87.00 | 104.31 +/- 0.00 91 | 15.00 | 14.13 +/- 0.00 92 | 25.00 | 24.33 +/- 0.00 93 | 55.00 | 59.75 +/- 0.00 94 | 131.00 | 119.61 +/- 0.00 95 | 55.00 | 52.31 +/- 0.00 96 | 45.00 | 56.38 +/- 0.00 97 | 11.00 | 7.88 +/- 0.00 98 | 103.00 | 82.99 +/- 0.00 99 | 77.00 | 65.03 +/- 0.00 100 | 55.00 | 69.04 +/- 0.00 101 | 10.00 | 7.20 +/- 0.00 102 | 56.00 | 50.38 +/- 0.00 103 | 127.00 | 107.80 +/- 0.00 104 | 14.00 | 14.04 +/- 0.00 105 | 68.00 | 61.31 +/- 0.00 106 | 88.00 | 117.88 +/- 0.00 107 | 87.00 | 121.59 +/- 0.00 108 | 8.00 | 7.38 +/- 0.00 109 | 22.00 | 32.86 +/- 0.00 110 | 55.00 | 52.82 +/- 0.00 111 | 87.00 | 113.93 +/- 0.00 112 | 85.00 | 118.97 +/- 0.00 113 | 78.00 | 86.11 +/- 0.00 114 | 108.00 | 120.01 +/- 0.00 115 | 144.00 | 122.97 +/- 0.00 116 | 119.00 | 123.52 +/- 0.00 117 | 99.00 | 119.72 +/- 0.00 118 | 145.00 | 123.01 +/- 0.00 119 | 87.00 | 108.88 +/- 0.00 120 | 28.00 | 26.56 +/- 0.00 121 | 136.00 | 115.39 +/- 0.00 122 | 27.00 | 33.78 +/- 0.00 123 | 132.00 | 120.95 +/- 0.00 124 | 28.00 | 21.91 +/- 0.00 125 | 35.00 | 31.78 +/- 0.00 126 | eval mean loss: 97.38 127 | eval rmse: 13.96 128 | eval mae: 10.10 129 | eval score: 409.86 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 40.19 134 | eval time: 0:00:01.428802 135 | **** end time: 2019-10-01 02:06:55.604433 **** 136 | -------------------------------------------------------------------------------- /results/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_9/log_evaluate.txt: -------------------------------------------------------------------------------- 1 | Namespace(batch_size=512, dataset='CMAPSS/FD003', dump_dir='dump/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_9', model='frequentist_dense3', model_path='log/CMAPSS/FD003/min-max/frequentist_dense3/frequentist_dense3_9/checkpoint.pth.tar', normalization='min-max', num_mc=1) 2 | pid: 8534 3 | use_cuda: True 4 | Dataset: CMAPSS/FD003 5 | Restoring FrequentistDense3... 6 | Done. 7 | **** start time: 2019-10-01 02:07:02.854510 **** 8 | ________________________________________________________________ 9 | Layer (type) Output Shape Param # 10 | ================================================================ 11 | Flatten-1 [-1, 420] 0 12 | Linear-2 [-1, 100] 42,000 13 | Sigmoid-3 [-1, 100] 0 14 | Linear-4 [-1, 100] 10,000 15 | Sigmoid-5 [-1, 100] 0 16 | Linear-6 [-1, 100] 10,000 17 | Sigmoid-7 [-1, 100] 0 18 | Linear-8 [-1, 1] 100 19 | ================================================================ 20 | Total params: 62,100 21 | Trainable params: 62,100 22 | Non-trainable params: 0 23 | ________________________________________________________________ 24 | 2019-10-01 02:07:02.857786 25 | ground truth | pred +/- std: 26 | 67.00 | 63.90 +/- 0.00 27 | 115.00 | 125.01 +/- 0.00 28 | 93.00 | 92.25 +/- 0.00 29 | 123.00 | 125.51 +/- 0.00 30 | 8.00 | 7.89 +/- 0.00 31 | 86.00 | 73.35 +/- 0.00 32 | 128.00 | 115.37 +/- 0.00 33 | 40.00 | 42.71 +/- 0.00 34 | 71.00 | 90.65 +/- 0.00 35 | 58.00 | 56.70 +/- 0.00 36 | 128.00 | 116.52 +/- 0.00 37 | 65.00 | 113.94 +/- 0.00 38 | 51.00 | 65.56 +/- 0.00 39 | 27.00 | 29.25 +/- 0.00 40 | 124.00 | 118.37 +/- 0.00 41 | 120.00 | 113.01 +/- 0.00 42 | 137.00 | 121.11 +/- 0.00 43 | 99.00 | 107.22 +/- 0.00 44 | 20.00 | 22.41 +/- 0.00 45 | 11.00 | 17.59 +/- 0.00 46 | 45.00 | 45.93 +/- 0.00 47 | 115.00 | 117.28 +/- 0.00 48 | 115.00 | 106.07 +/- 0.00 49 | 89.00 | 84.99 +/- 0.00 50 | 63.00 | 78.39 +/- 0.00 51 | 44.00 | 45.06 +/- 0.00 52 | 66.00 | 69.83 +/- 0.00 53 | 81.00 | 102.27 +/- 0.00 54 | 144.00 | 124.52 +/- 0.00 55 | 137.00 | 117.51 +/- 0.00 56 | 88.00 | 93.59 +/- 0.00 57 | 100.00 | 122.58 +/- 0.00 58 | 69.00 | 86.73 +/- 0.00 59 | 145.00 | 124.64 +/- 0.00 60 | 92.00 | 114.54 +/- 0.00 61 | 78.00 | 71.92 +/- 0.00 62 | 18.00 | 14.28 +/- 0.00 63 | 56.00 | 52.23 +/- 0.00 64 | 129.00 | 125.78 +/- 0.00 65 | 115.00 | 115.81 +/- 0.00 66 | 117.00 | 120.03 +/- 0.00 67 | 120.00 | 123.56 +/- 0.00 68 | 41.00 | 40.60 +/- 0.00 69 | 133.00 | 125.58 +/- 0.00 70 | 41.00 | 49.43 +/- 0.00 71 | 6.00 | 7.03 +/- 0.00 72 | 7.00 | 8.64 +/- 0.00 73 | 18.00 | 22.93 +/- 0.00 74 | 51.00 | 52.09 +/- 0.00 75 | 55.00 | 63.43 +/- 0.00 76 | 71.00 | 46.87 +/- 0.00 77 | 101.00 | 113.27 +/- 0.00 78 | 58.00 | 43.17 +/- 0.00 79 | 31.00 | 29.62 +/- 0.00 80 | 79.00 | 99.79 +/- 0.00 81 | 9.00 | 10.31 +/- 0.00 82 | 17.00 | 16.27 +/- 0.00 83 | 111.00 | 122.49 +/- 0.00 84 | 49.00 | 51.43 +/- 0.00 85 | 56.00 | 94.20 +/- 0.00 86 | 104.00 | 105.51 +/- 0.00 87 | 113.00 | 96.49 +/- 0.00 88 | 135.00 | 101.17 +/- 0.00 89 | 117.00 | 123.08 +/- 0.00 90 | 87.00 | 106.06 +/- 0.00 91 | 15.00 | 14.41 +/- 0.00 92 | 25.00 | 24.96 +/- 0.00 93 | 55.00 | 59.14 +/- 0.00 94 | 131.00 | 119.20 +/- 0.00 95 | 55.00 | 50.74 +/- 0.00 96 | 45.00 | 52.57 +/- 0.00 97 | 11.00 | 8.20 +/- 0.00 98 | 103.00 | 81.86 +/- 0.00 99 | 77.00 | 69.46 +/- 0.00 100 | 55.00 | 69.90 +/- 0.00 101 | 10.00 | 6.21 +/- 0.00 102 | 56.00 | 53.05 +/- 0.00 103 | 127.00 | 108.53 +/- 0.00 104 | 14.00 | 14.08 +/- 0.00 105 | 68.00 | 59.24 +/- 0.00 106 | 88.00 | 118.35 +/- 0.00 107 | 87.00 | 122.61 +/- 0.00 108 | 8.00 | 7.09 +/- 0.00 109 | 22.00 | 30.22 +/- 0.00 110 | 55.00 | 50.71 +/- 0.00 111 | 87.00 | 112.61 +/- 0.00 112 | 85.00 | 119.79 +/- 0.00 113 | 78.00 | 85.07 +/- 0.00 114 | 108.00 | 122.13 +/- 0.00 115 | 144.00 | 121.88 +/- 0.00 116 | 119.00 | 124.18 +/- 0.00 117 | 99.00 | 119.24 +/- 0.00 118 | 145.00 | 123.63 +/- 0.00 119 | 87.00 | 105.83 +/- 0.00 120 | 28.00 | 27.72 +/- 0.00 121 | 136.00 | 115.63 +/- 0.00 122 | 27.00 | 33.14 +/- 0.00 123 | 132.00 | 120.82 +/- 0.00 124 | 28.00 | 23.35 +/- 0.00 125 | 35.00 | 31.93 +/- 0.00 126 | eval mean loss: 103.22 127 | eval rmse: 14.37 128 | eval mae: 10.39 129 | eval score: 443.82 130 | epistemic: 0.00 131 | epoch: 249 132 | ground truth std: 41.40 133 | pred std: 40.31 134 | eval time: 0:00:01.452792 135 | **** end time: 2019-10-01 02:07:04.310800 **** 136 | -------------------------------------------------------------------------------- /train.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | readonly DATASET="CMAPSS/FD001" 3 | readonly MODEL="frequentist_dense3" 4 | readonly LOG_PATH="log/$DATASET/min-max/$MODEL/${MODEL}_test" 5 | readonly MAX_EPOCH=250 6 | readonly BATCH_SIZE=512 7 | readonly MAX_RUL=125 8 | readonly NUM_MC=1 9 | 10 | python3 train.py --dataset $DATASET --model $MODEL --log_dir $LOG_PATH --max_epoch $MAX_EPOCH --batch_size $BATCH_SIZE --max_rul $MAX_RUL --num_mc $NUM_MC 11 | -------------------------------------------------------------------------------- /utils/visualization.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """Visualization functions.""" 3 | 4 | import warnings 5 | warnings.filterwarnings("ignore") 6 | 7 | import numpy as np 8 | import matplotlib 9 | matplotlib.use("Agg") # use a non-interactive backend 10 | import matplotlib.pyplot as plt 11 | from matplotlib import figure 12 | from matplotlib import rc 13 | import seaborn as sns 14 | 15 | 16 | ALPHA = 0.35 17 | 18 | # set style 19 | sns.set_style("ticks") 20 | rc("font", size=12.5) 21 | rc("text", usetex=True) 22 | rc("grid", linestyle="dotted") 23 | rc("axes", unicode_minus=False) 24 | rc("axes", labelsize=15) 25 | rc("axes", titlesize=15) 26 | rc("axes", linewidth=1.2) 27 | rc("legend", fontsize=12.5) 28 | rc("legend", handlelength=1) 29 | rc("xtick", labelsize=12.5) 30 | rc("ytick", labelsize=12.5) 31 | rc("figure", figsize=(5, 4)) 32 | rc("lines", linewidth=1.2) 33 | 34 | 35 | def plot_weight_distr(names, qm_vals, qs_vals): 36 | """Plot weight distribution. 37 | 38 | Parameters 39 | ---------- 40 | names : [str] 41 | List of layer names. 42 | qm_vals : [ndarray] 43 | List of 1D array of `float` representing layer weight means. 44 | qs_vals : [ndarray] 45 | List of 1D array of `float` representing layer weight standard deviations. 46 | """ 47 | assert len(qm_vals) == len(qs_vals) 48 | 49 | fig = figure.Figure(figsize=(10, 4)) 50 | 51 | ax = fig.add_subplot(1, 2, 1) 52 | for n, qm in zip(names, qm_vals): 53 | sns.kdeplot(qm.flatten(), ax=ax, label=n, shade=True) 54 | ax.grid(True, linewidth=1.5) 55 | ax.set_xlabel("Weights mean") 56 | ax.set_ylabel("Density") 57 | ax.set_xlim([-1.5, 1.5]) 58 | ax.get_legend().remove() 59 | sns.despine(ax=ax) 60 | 61 | ax = fig.add_subplot(1, 2, 2) 62 | for n, qs in zip(names, qs_vals): 63 | sns.kdeplot(qs.flatten(), ax=ax, label=n.replace("Linear", "Dense"), shade=True) 64 | if sum(qs.flatten()) == 0: 65 | ax.axvline(x=0, linestyle="-") 66 | 67 | ax.grid(True, linewidth=1.5) 68 | ax.set_xlabel("Weights standard deviation") 69 | ax.set_ylabel("Density") 70 | ax.set_xlim([0, 1.]) 71 | ax.legend(loc="upper right", frameon=True) 72 | sns.despine(ax=ax) 73 | 74 | fig.tight_layout() 75 | 76 | return fig 77 | 78 | 79 | def plot_predictive_distr(preds, mean, std): 80 | """Plot predictive distribution. 81 | 82 | Parameters 83 | ---------- 84 | preds : ndarray 85 | 1D array of `float` representing the predicted labels. 86 | mean : float 87 | Predictive mean. 88 | std : float 89 | Predictive standard deviation. 90 | """ 91 | RED = (0.7686274509803922, 0.3058823529411765, 0.3215686274509804) 92 | 93 | fig = plt.figure() 94 | 95 | p = sns.kdeplot(preds, shade=True) 96 | x, y = p.get_lines()[0].get_data() 97 | 98 | plt.axvline(x=mean, linestyle="--", color=RED, linewidth=1, label=r"$ \mu \enspace = %.2f$" % mean) 99 | plt.fill_between(x, 0, y, where=((x > mean - std) & (x < mean + std)), color=RED, alpha=ALPHA) 100 | plt.fill(np.NaN, np.NaN, color=RED, alpha=ALPHA, label=r"$2\sigma = %.2f$" % (2 * std)) 101 | 102 | plt.grid(True, linewidth=1.5) 103 | plt.xlabel("Predicted RUL") 104 | plt.ylabel("Density") 105 | plt.legend(loc="upper left", frameon=True) 106 | sns.despine() 107 | 108 | fig.tight_layout() 109 | 110 | return fig 111 | --------------------------------------------------------------------------------