/////////////////////////////////////////// // Running convGAN-proximary-5 on folding_car_good /////////////////////////////////////////// Load 'data_input/folding_car_good' from pickle file Data loaded. -> Shuffling data ### Start exercise for synthetic point generator ====== Step 1/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 1/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 19s - loss: 0.0055 45/133 [=========>....................] - ETA: 0s - loss: 0.0416  90/133 [===================>..........] - ETA: 0s - loss: 0.0481 133/133 [==============================] - 0s 1ms/step - loss: 0.0407 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.1364 46/133 [=========>....................] - ETA: 0s - loss: 0.0461 91/133 [===================>..........] - ETA: 0s - loss: 0.0325 133/133 [==============================] - 0s 1ms/step - loss: 0.0322 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0197 47/133 [=========>....................] - ETA: 0s - loss: 0.0229 93/133 [===================>..........] - ETA: 0s - loss: 0.0306 133/133 [==============================] - 0s 1ms/step - loss: 0.0289 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0164 46/133 [=========>....................] - ETA: 0s - loss: 0.0223 90/133 [===================>..........] - ETA: 0s - loss: 0.0252 133/133 [==============================] - 0s 1ms/step - loss: 0.0264 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0048 44/133 [========>.....................] - ETA: 0s - loss: 0.0222 85/133 [==================>...........] - ETA: 0s - loss: 0.0255 126/133 [===========================>..] - ETA: 0s - loss: 0.0232 133/133 [==============================] - 0s 1ms/step - loss: 0.0244 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0217 46/133 [=========>....................] - ETA: 0s - loss: 0.0215 91/133 [===================>..........] - ETA: 0s - loss: 0.0249 133/133 [==============================] - 0s 1ms/step - loss: 0.0224 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0071 46/133 [=========>....................] - ETA: 0s - loss: 0.0181 89/133 [===================>..........] - ETA: 0s - loss: 0.0210 131/133 [============================>.] - ETA: 0s - loss: 0.0210 133/133 [==============================] - 0s 1ms/step - loss: 0.0209 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0222 38/133 [=======>......................] - ETA: 0s - loss: 0.0164 75/133 [===============>..............] - ETA: 0s - loss: 0.0191 117/133 [=========================>....] - ETA: 0s - loss: 0.0187 133/133 [==============================] - 0s 1ms/step - loss: 0.0205 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0053 45/133 [=========>....................] - ETA: 0s - loss: 0.0120 90/133 [===================>..........] - ETA: 0s - loss: 0.0166 133/133 [==============================] - 0s 1ms/step - loss: 0.0184 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0096 46/133 [=========>....................] - ETA: 0s - loss: 0.0154 92/133 [===================>..........] - ETA: 0s - loss: 0.0195 133/133 [==============================] - 0s 1ms/step - loss: 0.0172 -> test with GAN.predict GAN tn, fp: 330, 2 GAN fn, tp: 3, 11 GAN f1 score: 0.815 GAN cohens kappa score: 0.807 -> test with 'LR' LR tn, fp: 178, 154 LR fn, tp: 6, 8 LR f1 score: 0.091 LR cohens kappa score: 0.018 LR average precision score: 0.058 -> test with 'RF' RF tn, fp: 330, 2 RF fn, tp: 3, 11 RF f1 score: 0.815 RF cohens kappa score: 0.807 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 2, 12 GB f1 score: 0.857 GB cohens kappa score: 0.851 -> test with 'KNN' KNN tn, fp: 310, 22 KNN fn, tp: 0, 14 KNN f1 score: 0.560 KNN cohens kappa score: 0.533 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0045 50/133 [==========>...................] - ETA: 0s - loss: 0.0634  100/133 [=====================>........] - ETA: 0s - loss: 0.0579 133/133 [==============================] - 0s 1ms/step - loss: 0.0552 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0160 50/133 [==========>...................] - ETA: 0s - loss: 0.0513 99/133 [=====================>........] - ETA: 0s - loss: 0.0496 133/133 [==============================] - 0s 1ms/step - loss: 0.0474 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0801 50/133 [==========>...................] - ETA: 0s - loss: 0.0362 99/133 [=====================>........] - ETA: 0s - loss: 0.0356 133/133 [==============================] - 0s 1ms/step - loss: 0.0373 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0033 48/133 [=========>....................] - ETA: 0s - loss: 0.0416 97/133 [====================>.........] - ETA: 0s - loss: 0.0376 133/133 [==============================] - 0s 1ms/step - loss: 0.0356 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0039 49/133 [==========>...................] - ETA: 0s - loss: 0.0209 97/133 [====================>.........] - ETA: 0s - loss: 0.0268 133/133 [==============================] - 0s 1ms/step - loss: 0.0313 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0649 50/133 [==========>...................] - ETA: 0s - loss: 0.0219 97/133 [====================>.........] - ETA: 0s - loss: 0.0217 133/133 [==============================] - 0s 1ms/step - loss: 0.0277 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0156 50/133 [==========>...................] - ETA: 0s - loss: 0.0326 99/133 [=====================>........] - ETA: 0s - loss: 0.0265 133/133 [==============================] - 0s 1ms/step - loss: 0.0281 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0045 49/133 [==========>...................] - ETA: 0s - loss: 0.0178 98/133 [=====================>........] - ETA: 0s - loss: 0.0224 133/133 [==============================] - 0s 1ms/step - loss: 0.0252 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0015 50/133 [==========>...................] - ETA: 0s - loss: 0.0193 99/133 [=====================>........] - ETA: 0s - loss: 0.0242 133/133 [==============================] - 0s 1ms/step - loss: 0.0242 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0110 50/133 [==========>...................] - ETA: 0s - loss: 0.0301 98/133 [=====================>........] - ETA: 0s - loss: 0.0197 133/133 [==============================] - 0s 1ms/step - loss: 0.0220 -> test with GAN.predict GAN tn, fp: 329, 3 GAN fn, tp: 2, 12 GAN f1 score: 0.828 GAN cohens kappa score: 0.820 -> test with 'LR' LR tn, fp: 178, 154 LR fn, tp: 4, 10 LR f1 score: 0.112 LR cohens kappa score: 0.041 LR average precision score: 0.075 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 6, 8 RF f1 score: 0.727 RF cohens kappa score: 0.719 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 7, 7 GB f1 score: 0.583 GB cohens kappa score: 0.569 -> test with 'KNN' KNN tn, fp: 302, 30 KNN fn, tp: 0, 14 KNN f1 score: 0.483 KNN cohens kappa score: 0.449 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 19s - loss: 0.0034 49/133 [==========>...................] - ETA: 0s - loss: 0.0567  98/133 [=====================>........] - ETA: 0s - loss: 0.0425 133/133 [==============================] - 0s 1ms/step - loss: 0.0422 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0040 50/133 [==========>...................] - ETA: 0s - loss: 0.0390 99/133 [=====================>........] - ETA: 0s - loss: 0.0361 133/133 [==============================] - 0s 1ms/step - loss: 0.0332 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0349 49/133 [==========>...................] - ETA: 0s - loss: 0.0211 98/133 [=====================>........] - ETA: 0s - loss: 0.0296 133/133 [==============================] - 0s 1ms/step - loss: 0.0295 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0401 45/133 [=========>....................] - ETA: 0s - loss: 0.0290 89/133 [===================>..........] - ETA: 0s - loss: 0.0297 133/133 [==============================] - 0s 1ms/step - loss: 0.0273 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.1266 43/133 [========>.....................] - ETA: 0s - loss: 0.0241 90/133 [===================>..........] - ETA: 0s - loss: 0.0278 133/133 [==============================] - 0s 1ms/step - loss: 0.0247 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0056 50/133 [==========>...................] - ETA: 0s - loss: 0.0227 99/133 [=====================>........] - ETA: 0s - loss: 0.0253 133/133 [==============================] - 0s 1ms/step - loss: 0.0241 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0026 50/133 [==========>...................] - ETA: 0s - loss: 0.0207 99/133 [=====================>........] - ETA: 0s - loss: 0.0238 133/133 [==============================] - 0s 1ms/step - loss: 0.0231 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0081 50/133 [==========>...................] - ETA: 0s - loss: 0.0194 99/133 [=====================>........] - ETA: 0s - loss: 0.0141 133/133 [==============================] - 0s 1ms/step - loss: 0.0215 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0120 50/133 [==========>...................] - ETA: 0s - loss: 0.0198 99/133 [=====================>........] - ETA: 0s - loss: 0.0218 133/133 [==============================] - 0s 1ms/step - loss: 0.0200 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0047 50/133 [==========>...................] - ETA: 0s - loss: 0.0144 99/133 [=====================>........] - ETA: 0s - loss: 0.0175 133/133 [==============================] - 0s 1ms/step - loss: 0.0192 -> test with GAN.predict GAN tn, fp: 331, 1 GAN fn, tp: 2, 12 GAN f1 score: 0.889 GAN cohens kappa score: 0.884 -> test with 'LR' LR tn, fp: 174, 158 LR fn, tp: 5, 9 LR f1 score: 0.099 LR cohens kappa score: 0.027 LR average precision score: 0.057 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 5, 9 RF f1 score: 0.783 RF cohens kappa score: 0.775 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 4, 10 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> test with 'KNN' KNN tn, fp: 310, 22 KNN fn, tp: 1, 13 KNN f1 score: 0.531 KNN cohens kappa score: 0.502 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 19s - loss: 0.0043 45/133 [=========>....................] - ETA: 0s - loss: 0.0242  89/133 [===================>..........] - ETA: 0s - loss: 0.0329 133/133 [==============================] - 0s 1ms/step - loss: 0.0333 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.1091 44/133 [========>.....................] - ETA: 0s - loss: 0.0348 89/133 [===================>..........] - ETA: 0s - loss: 0.0298 133/133 [==============================] - 0s 1ms/step - loss: 0.0259 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0050 45/133 [=========>....................] - ETA: 0s - loss: 0.0282 90/133 [===================>..........] - ETA: 0s - loss: 0.0248 133/133 [==============================] - 0s 1ms/step - loss: 0.0222 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0089 46/133 [=========>....................] - ETA: 0s - loss: 0.0209 91/133 [===================>..........] - ETA: 0s - loss: 0.0209 133/133 [==============================] - 0s 1ms/step - loss: 0.0209 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0026 45/133 [=========>....................] - ETA: 0s - loss: 0.0223 87/133 [==================>...........] - ETA: 0s - loss: 0.0209 130/133 [============================>.] - ETA: 0s - loss: 0.0178 133/133 [==============================] - 0s 1ms/step - loss: 0.0175 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0409 44/133 [========>.....................] - ETA: 0s - loss: 0.0183 88/133 [==================>...........] - ETA: 0s - loss: 0.0186 126/133 [===========================>..] - ETA: 0s - loss: 0.0178 133/133 [==============================] - 0s 1ms/step - loss: 0.0171 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0999 43/133 [========>.....................] - ETA: 0s - loss: 0.0131 86/133 [==================>...........] - ETA: 0s - loss: 0.0166 129/133 [============================>.] - ETA: 0s - loss: 0.0159 133/133 [==============================] - 0s 1ms/step - loss: 0.0155 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0032 46/133 [=========>....................] - ETA: 0s - loss: 0.0181 87/133 [==================>...........] - ETA: 0s - loss: 0.0151 131/133 [============================>.] - ETA: 0s - loss: 0.0140 133/133 [==============================] - 0s 1ms/step - loss: 0.0139 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0059 44/133 [========>.....................] - ETA: 0s - loss: 0.0147 87/133 [==================>...........] - ETA: 0s - loss: 0.0142 123/133 [==========================>...] - ETA: 0s - loss: 0.0144 133/133 [==============================] - 0s 1ms/step - loss: 0.0137 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0025 37/133 [=======>......................] - ETA: 0s - loss: 0.0153 79/133 [================>.............] - ETA: 0s - loss: 0.0134 123/133 [==========================>...] - ETA: 0s - loss: 0.0132 133/133 [==============================] - 0s 1ms/step - loss: 0.0129 -> test with GAN.predict GAN tn, fp: 328, 4 GAN fn, tp: 4, 10 GAN f1 score: 0.714 GAN cohens kappa score: 0.702 -> test with 'LR' LR tn, fp: 178, 154 LR fn, tp: 3, 11 LR f1 score: 0.123 LR cohens kappa score: 0.052 LR average precision score: 0.076 -> test with 'RF' RF tn, fp: 331, 1 RF fn, tp: 9, 5 RF f1 score: 0.500 RF cohens kappa score: 0.488 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 6, 8 GB f1 score: 0.696 GB cohens kappa score: 0.686 -> test with 'KNN' KNN tn, fp: 306, 26 KNN fn, tp: 2, 12 KNN f1 score: 0.462 KNN cohens kappa score: 0.428 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0097 46/133 [=========>....................] - ETA: 0s - loss: 0.0501  94/133 [====================>.........] - ETA: 0s - loss: 0.0514 133/133 [==============================] - 0s 1ms/step - loss: 0.0433 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0019 49/133 [==========>...................] - ETA: 0s - loss: 0.0149 98/133 [=====================>........] - ETA: 0s - loss: 0.0351 133/133 [==============================] - 0s 1ms/step - loss: 0.0370 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0693 49/133 [==========>...................] - ETA: 0s - loss: 0.0411 90/133 [===================>..........] - ETA: 0s - loss: 0.0295 132/133 [============================>.] - ETA: 0s - loss: 0.0326 133/133 [==============================] - 0s 1ms/step - loss: 0.0327 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0020 46/133 [=========>....................] - ETA: 0s - loss: 0.0291 93/133 [===================>..........] - ETA: 0s - loss: 0.0250 133/133 [==============================] - 0s 1ms/step - loss: 0.0279 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0093 48/133 [=========>....................] - ETA: 0s - loss: 0.0322 97/133 [====================>.........] - ETA: 0s - loss: 0.0245 133/133 [==============================] - 0s 1ms/step - loss: 0.0259 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0095 50/133 [==========>...................] - ETA: 0s - loss: 0.0148 99/133 [=====================>........] - ETA: 0s - loss: 0.0233 133/133 [==============================] - 0s 1ms/step - loss: 0.0247 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0075 50/133 [==========>...................] - ETA: 0s - loss: 0.0193 98/133 [=====================>........] - ETA: 0s - loss: 0.0215 133/133 [==============================] - 0s 1ms/step - loss: 0.0234 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0011 50/133 [==========>...................] - ETA: 0s - loss: 0.0240 99/133 [=====================>........] - ETA: 0s - loss: 0.0222 133/133 [==============================] - 0s 1ms/step - loss: 0.0209 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0315 49/133 [==========>...................] - ETA: 0s - loss: 0.0168 98/133 [=====================>........] - ETA: 0s - loss: 0.0198 133/133 [==============================] - 0s 1ms/step - loss: 0.0202 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0023 49/133 [==========>...................] - ETA: 0s - loss: 0.0173 98/133 [=====================>........] - ETA: 0s - loss: 0.0181 133/133 [==============================] - 0s 1ms/step - loss: 0.0198 -> test with GAN.predict GAN tn, fp: 325, 6 GAN fn, tp: 2, 11 GAN f1 score: 0.733 GAN cohens kappa score: 0.721 -> test with 'LR' LR tn, fp: 176, 155 LR fn, tp: 4, 9 LR f1 score: 0.102 LR cohens kappa score: 0.034 LR average precision score: 0.056 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 11, 2 RF f1 score: 0.267 RF cohens kappa score: 0.259 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 3, 10 GB f1 score: 0.769 GB cohens kappa score: 0.760 -> test with 'KNN' KNN tn, fp: 316, 15 KNN fn, tp: 1, 12 KNN f1 score: 0.600 KNN cohens kappa score: 0.578 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0081 49/133 [==========>...................] - ETA: 0s - loss: 0.0340  98/133 [=====================>........] - ETA: 0s - loss: 0.0479 133/133 [==============================] - 0s 1ms/step - loss: 0.0485 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0590 50/133 [==========>...................] - ETA: 0s - loss: 0.0399 99/133 [=====================>........] - ETA: 0s - loss: 0.0447 133/133 [==============================] - 0s 1ms/step - loss: 0.0391 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.2036 50/133 [==========>...................] - ETA: 0s - loss: 0.0288 99/133 [=====================>........] - ETA: 0s - loss: 0.0315 133/133 [==============================] - 0s 1ms/step - loss: 0.0349 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0282 50/133 [==========>...................] - ETA: 0s - loss: 0.0164 99/133 [=====================>........] - ETA: 0s - loss: 0.0269 133/133 [==============================] - 0s 1ms/step - loss: 0.0310 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0618 50/133 [==========>...................] - ETA: 0s - loss: 0.0296 98/133 [=====================>........] - ETA: 0s - loss: 0.0328 133/133 [==============================] - 0s 1ms/step - loss: 0.0299 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0389 45/133 [=========>....................] - ETA: 0s - loss: 0.0352 89/133 [===================>..........] - ETA: 0s - loss: 0.0276 129/133 [============================>.] - ETA: 0s - loss: 0.0276 133/133 [==============================] - 0s 1ms/step - loss: 0.0272 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0055 45/133 [=========>....................] - ETA: 0s - loss: 0.0210 92/133 [===================>..........] - ETA: 0s - loss: 0.0288 133/133 [==============================] - 0s 1ms/step - loss: 0.0259 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0057 50/133 [==========>...................] - ETA: 0s - loss: 0.0157 99/133 [=====================>........] - ETA: 0s - loss: 0.0211 133/133 [==============================] - 0s 1ms/step - loss: 0.0232 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.2220 50/133 [==========>...................] - ETA: 0s - loss: 0.0237 98/133 [=====================>........] - ETA: 0s - loss: 0.0199 133/133 [==============================] - 0s 1ms/step - loss: 0.0224 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0516 49/133 [==========>...................] - ETA: 0s - loss: 0.0190 98/133 [=====================>........] - ETA: 0s - loss: 0.0229 133/133 [==============================] - 0s 1ms/step - loss: 0.0212 -> test with GAN.predict GAN tn, fp: 325, 7 GAN fn, tp: 4, 10 GAN f1 score: 0.645 GAN cohens kappa score: 0.629 -> test with 'LR' LR tn, fp: 155, 177 LR fn, tp: 4, 10 LR f1 score: 0.100 LR cohens kappa score: 0.026 LR average precision score: 0.064 -> test with 'RF' RF tn, fp: 331, 1 RF fn, tp: 8, 6 RF f1 score: 0.571 RF cohens kappa score: 0.560 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 7, 7 GB f1 score: 0.609 GB cohens kappa score: 0.596 -> test with 'KNN' KNN tn, fp: 306, 26 KNN fn, tp: 2, 12 KNN f1 score: 0.462 KNN cohens kappa score: 0.428 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 19s - loss: 0.0152 48/133 [=========>....................] - ETA: 0s - loss: 0.0670  96/133 [====================>.........] - ETA: 0s - loss: 0.0588 133/133 [==============================] - 0s 1ms/step - loss: 0.0548 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0312 50/133 [==========>...................] - ETA: 0s - loss: 0.0423 98/133 [=====================>........] - ETA: 0s - loss: 0.0416 133/133 [==============================] - 0s 1ms/step - loss: 0.0382 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0156 42/133 [========>.....................] - ETA: 0s - loss: 0.0294 85/133 [==================>...........] - ETA: 0s - loss: 0.0333 133/133 [==============================] - ETA: 0s - loss: 0.0319 133/133 [==============================] - 0s 1ms/step - loss: 0.0319 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0238 50/133 [==========>...................] - ETA: 0s - loss: 0.0273 98/133 [=====================>........] - ETA: 0s - loss: 0.0308 133/133 [==============================] - 0s 1ms/step - loss: 0.0284 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0088 50/133 [==========>...................] - ETA: 0s - loss: 0.0284 98/133 [=====================>........] - ETA: 0s - loss: 0.0270 133/133 [==============================] - 0s 1ms/step - loss: 0.0253 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0099 50/133 [==========>...................] - ETA: 0s - loss: 0.0293 99/133 [=====================>........] - ETA: 0s - loss: 0.0259 133/133 [==============================] - 0s 1ms/step - loss: 0.0240 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0145 45/133 [=========>....................] - ETA: 0s - loss: 0.0225 94/133 [====================>.........] - ETA: 0s - loss: 0.0247 133/133 [==============================] - 0s 1ms/step - loss: 0.0219 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0133 49/133 [==========>...................] - ETA: 0s - loss: 0.0162 97/133 [====================>.........] - ETA: 0s - loss: 0.0222 133/133 [==============================] - 0s 1ms/step - loss: 0.0201 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0093 50/133 [==========>...................] - ETA: 0s - loss: 0.0178 98/133 [=====================>........] - ETA: 0s - loss: 0.0180 133/133 [==============================] - 0s 1ms/step - loss: 0.0188 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0078 50/133 [==========>...................] - ETA: 0s - loss: 0.0222 99/133 [=====================>........] - ETA: 0s - loss: 0.0197 133/133 [==============================] - 0s 1ms/step - loss: 0.0174 -> test with GAN.predict GAN tn, fp: 328, 4 GAN fn, tp: 1, 13 GAN f1 score: 0.839 GAN cohens kappa score: 0.831 -> test with 'LR' LR tn, fp: 176, 156 LR fn, tp: 4, 10 LR f1 score: 0.111 LR cohens kappa score: 0.039 LR average precision score: 0.064 -> test with 'RF' RF tn, fp: 331, 1 RF fn, tp: 5, 9 RF f1 score: 0.750 RF cohens kappa score: 0.741 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 3, 11 GB f1 score: 0.815 GB cohens kappa score: 0.807 -> test with 'KNN' KNN tn, fp: 312, 20 KNN fn, tp: 1, 13 KNN f1 score: 0.553 KNN cohens kappa score: 0.526 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 19s - loss: 0.0125 44/133 [========>.....................] - ETA: 0s - loss: 0.0576  91/133 [===================>..........] - ETA: 0s - loss: 0.0487 133/133 [==============================] - 0s 1ms/step - loss: 0.0461 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0318 49/133 [==========>...................] - ETA: 0s - loss: 0.0303 97/133 [====================>.........] - ETA: 0s - loss: 0.0317 133/133 [==============================] - 0s 1ms/step - loss: 0.0306 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0070 50/133 [==========>...................] - ETA: 0s - loss: 0.0224 99/133 [=====================>........] - ETA: 0s - loss: 0.0266 133/133 [==============================] - 0s 1ms/step - loss: 0.0257 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0258 50/133 [==========>...................] - ETA: 0s - loss: 0.0231 99/133 [=====================>........] - ETA: 0s - loss: 0.0239 133/133 [==============================] - 0s 1ms/step - loss: 0.0230 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0053 49/133 [==========>...................] - ETA: 0s - loss: 0.0213 97/133 [====================>.........] - ETA: 0s - loss: 0.0238 133/133 [==============================] - 0s 1ms/step - loss: 0.0218 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0111 49/133 [==========>...................] - ETA: 0s - loss: 0.0234 98/133 [=====================>........] - ETA: 0s - loss: 0.0195 133/133 [==============================] - 0s 1ms/step - loss: 0.0191 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0662 49/133 [==========>...................] - ETA: 0s - loss: 0.0197 97/133 [====================>.........] - ETA: 0s - loss: 0.0192 133/133 [==============================] - 0s 1ms/step - loss: 0.0185 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0020 50/133 [==========>...................] - ETA: 0s - loss: 0.0160 98/133 [=====================>........] - ETA: 0s - loss: 0.0191 133/133 [==============================] - 0s 1ms/step - loss: 0.0178 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0039 47/133 [=========>....................] - ETA: 0s - loss: 0.0161 95/133 [====================>.........] - ETA: 0s - loss: 0.0155 133/133 [==============================] - 0s 1ms/step - loss: 0.0149 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0015 50/133 [==========>...................] - ETA: 0s - loss: 0.0153 98/133 [=====================>........] - ETA: 0s - loss: 0.0147 133/133 [==============================] - 0s 1ms/step - loss: 0.0148 -> test with GAN.predict GAN tn, fp: 330, 2 GAN fn, tp: 2, 12 GAN f1 score: 0.857 GAN cohens kappa score: 0.851 -> test with 'LR' LR tn, fp: 191, 141 LR fn, tp: 3, 11 LR f1 score: 0.133 LR cohens kappa score: 0.063 LR average precision score: 0.070 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 8, 6 RF f1 score: 0.600 RF cohens kappa score: 0.590 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 7, 7 GB f1 score: 0.667 GB cohens kappa score: 0.657 -> test with 'KNN' KNN tn, fp: 317, 15 KNN fn, tp: 1, 13 KNN f1 score: 0.619 KNN cohens kappa score: 0.597 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0058 49/133 [==========>...................] - ETA: 0s - loss: 0.0322  98/133 [=====================>........] - ETA: 0s - loss: 0.0325 133/133 [==============================] - 0s 1ms/step - loss: 0.0309 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0040 49/133 [==========>...................] - ETA: 0s - loss: 0.0199 98/133 [=====================>........] - ETA: 0s - loss: 0.0245 133/133 [==============================] - 0s 1ms/step - loss: 0.0240 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0192 49/133 [==========>...................] - ETA: 0s - loss: 0.0268 97/133 [====================>.........] - ETA: 0s - loss: 0.0205 133/133 [==============================] - 0s 1ms/step - loss: 0.0209 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0178 49/133 [==========>...................] - ETA: 0s - loss: 0.0212 97/133 [====================>.........] - ETA: 0s - loss: 0.0202 133/133 [==============================] - 0s 1ms/step - loss: 0.0193 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0098 47/133 [=========>....................] - ETA: 0s - loss: 0.0144 90/133 [===================>..........] - ETA: 0s - loss: 0.0137 132/133 [============================>.] - ETA: 0s - loss: 0.0178 133/133 [==============================] - 0s 1ms/step - loss: 0.0177 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0117 49/133 [==========>...................] - ETA: 0s - loss: 0.0151 91/133 [===================>..........] - ETA: 0s - loss: 0.0159 133/133 [==============================] - 0s 1ms/step - loss: 0.0168 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0025 49/133 [==========>...................] - ETA: 0s - loss: 0.0138 98/133 [=====================>........] - ETA: 0s - loss: 0.0147 133/133 [==============================] - 0s 1ms/step - loss: 0.0155 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0105 50/133 [==========>...................] - ETA: 0s - loss: 0.0157 99/133 [=====================>........] - ETA: 0s - loss: 0.0159 133/133 [==============================] - 0s 1ms/step - loss: 0.0150 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0105 47/133 [=========>....................] - ETA: 0s - loss: 0.0145 95/133 [====================>.........] - ETA: 0s - loss: 0.0146 133/133 [==============================] - 0s 1ms/step - loss: 0.0141 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0038 49/133 [==========>...................] - ETA: 0s - loss: 0.0162 97/133 [====================>.........] - ETA: 0s - loss: 0.0138 133/133 [==============================] - 0s 1ms/step - loss: 0.0129 -> test with GAN.predict GAN tn, fp: 325, 7 GAN fn, tp: 2, 12 GAN f1 score: 0.727 GAN cohens kappa score: 0.714 -> test with 'LR' LR tn, fp: 188, 144 LR fn, tp: 7, 7 LR f1 score: 0.085 LR cohens kappa score: 0.012 LR average precision score: 0.052 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 8, 6 RF f1 score: 0.600 RF cohens kappa score: 0.590 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 3, 11 GB f1 score: 0.846 GB cohens kappa score: 0.840 -> test with 'KNN' KNN tn, fp: 309, 23 KNN fn, tp: 2, 12 KNN f1 score: 0.490 KNN cohens kappa score: 0.458 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0200 42/133 [========>.....................] - ETA: 0s - loss: 0.0351  84/133 [=================>............] - ETA: 0s - loss: 0.0421 128/133 [===========================>..] - ETA: 0s - loss: 0.0387 133/133 [==============================] - 0s 1ms/step - loss: 0.0378 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0269 40/133 [========>.....................] - ETA: 0s - loss: 0.0361 88/133 [==================>...........] - ETA: 0s - loss: 0.0321 133/133 [==============================] - 0s 1ms/step - loss: 0.0290 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0033 50/133 [==========>...................] - ETA: 0s - loss: 0.0260 99/133 [=====================>........] - ETA: 0s - loss: 0.0266 133/133 [==============================] - 0s 1ms/step - loss: 0.0253 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0636 50/133 [==========>...................] - ETA: 0s - loss: 0.0255 99/133 [=====================>........] - ETA: 0s - loss: 0.0212 133/133 [==============================] - 0s 1ms/step - loss: 0.0228 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0061 50/133 [==========>...................] - ETA: 0s - loss: 0.0172 99/133 [=====================>........] - ETA: 0s - loss: 0.0221 133/133 [==============================] - 0s 1ms/step - loss: 0.0216 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0083 50/133 [==========>...................] - ETA: 0s - loss: 0.0191 98/133 [=====================>........] - ETA: 0s - loss: 0.0213 133/133 [==============================] - 0s 1ms/step - loss: 0.0189 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0210 49/133 [==========>...................] - ETA: 0s - loss: 0.0143 98/133 [=====================>........] - ETA: 0s - loss: 0.0196 133/133 [==============================] - 0s 1ms/step - loss: 0.0184 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0229 50/133 [==========>...................] - ETA: 0s - loss: 0.0098 99/133 [=====================>........] - ETA: 0s - loss: 0.0134 133/133 [==============================] - 0s 1ms/step - loss: 0.0162 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0057 50/133 [==========>...................] - ETA: 0s - loss: 0.0130 99/133 [=====================>........] - ETA: 0s - loss: 0.0162 133/133 [==============================] - 0s 1ms/step - loss: 0.0163 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0226 50/133 [==========>...................] - ETA: 0s - loss: 0.0168 99/133 [=====================>........] - ETA: 0s - loss: 0.0144 133/133 [==============================] - 0s 1ms/step - loss: 0.0149 -> test with GAN.predict GAN tn, fp: 329, 2 GAN fn, tp: 3, 10 GAN f1 score: 0.800 GAN cohens kappa score: 0.792 -> test with 'LR' LR tn, fp: 188, 143 LR fn, tp: 5, 8 LR f1 score: 0.098 LR cohens kappa score: 0.030 LR average precision score: 0.073 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 6, 7 RF f1 score: 0.700 RF cohens kappa score: 0.692 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 2, 11 GB f1 score: 0.815 GB cohens kappa score: 0.807 -> test with 'KNN' KNN tn, fp: 307, 24 KNN fn, tp: 0, 13 KNN f1 score: 0.520 KNN cohens kappa score: 0.492 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0014 48/133 [=========>....................] - ETA: 0s - loss: 0.0382  96/133 [====================>.........] - ETA: 0s - loss: 0.0337 133/133 [==============================] - 0s 1ms/step - loss: 0.0370 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0089 50/133 [==========>...................] - ETA: 0s - loss: 0.0215 98/133 [=====================>........] - ETA: 0s - loss: 0.0234 133/133 [==============================] - 0s 1ms/step - loss: 0.0253 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.3728 49/133 [==========>...................] - ETA: 0s - loss: 0.0260 98/133 [=====================>........] - ETA: 0s - loss: 0.0222 133/133 [==============================] - 0s 1ms/step - loss: 0.0217 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.4141 50/133 [==========>...................] - ETA: 0s - loss: 0.0261 99/133 [=====================>........] - ETA: 0s - loss: 0.0218 133/133 [==============================] - 0s 1ms/step - loss: 0.0202 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0364 49/133 [==========>...................] - ETA: 0s - loss: 0.0237 98/133 [=====================>........] - ETA: 0s - loss: 0.0197 133/133 [==============================] - 0s 1ms/step - loss: 0.0181 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 4.3139e-04 50/133 [==========>...................] - ETA: 0s - loss: 0.0156  98/133 [=====================>........] - ETA: 0s - loss: 0.0142 133/133 [==============================] - 0s 1ms/step - loss: 0.0172 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0242 50/133 [==========>...................] - ETA: 0s - loss: 0.0214 99/133 [=====================>........] - ETA: 0s - loss: 0.0169 133/133 [==============================] - 0s 1ms/step - loss: 0.0156 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 8.3619e-04 47/133 [=========>....................] - ETA: 0s - loss: 0.0188  95/133 [====================>.........] - ETA: 0s - loss: 0.0170 133/133 [==============================] - 0s 1ms/step - loss: 0.0156 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0023 49/133 [==========>...................] - ETA: 0s - loss: 0.0115 98/133 [=====================>........] - ETA: 0s - loss: 0.0145 133/133 [==============================] - 0s 1ms/step - loss: 0.0159 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0028 49/133 [==========>...................] - ETA: 0s - loss: 0.0106 98/133 [=====================>........] - ETA: 0s - loss: 0.0126 133/133 [==============================] - 0s 1ms/step - loss: 0.0151 -> test with GAN.predict GAN tn, fp: 328, 4 GAN fn, tp: 2, 12 GAN f1 score: 0.800 GAN cohens kappa score: 0.791 -> test with 'LR' LR tn, fp: 170, 162 LR fn, tp: 3, 11 LR f1 score: 0.118 LR cohens kappa score: 0.046 LR average precision score: 0.068 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 8, 6 RF f1 score: 0.600 RF cohens kappa score: 0.590 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 5, 9 GB f1 score: 0.720 GB cohens kappa score: 0.710 -> test with 'KNN' KNN tn, fp: 321, 11 KNN fn, tp: 2, 12 KNN f1 score: 0.649 KNN cohens kappa score: 0.630 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0043 46/133 [=========>....................] - ETA: 0s - loss: 0.0808  95/133 [====================>.........] - ETA: 0s - loss: 0.0699 133/133 [==============================] - 0s 1ms/step - loss: 0.0637 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0158 50/133 [==========>...................] - ETA: 0s - loss: 0.0436 98/133 [=====================>........] - ETA: 0s - loss: 0.0483 133/133 [==============================] - 0s 1ms/step - loss: 0.0497 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0343 49/133 [==========>...................] - ETA: 0s - loss: 0.0303 98/133 [=====================>........] - ETA: 0s - loss: 0.0355 133/133 [==============================] - 0s 1ms/step - loss: 0.0422 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0377 50/133 [==========>...................] - ETA: 0s - loss: 0.0319 98/133 [=====================>........] - ETA: 0s - loss: 0.0341 133/133 [==============================] - 0s 1ms/step - loss: 0.0382 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0228 49/133 [==========>...................] - ETA: 0s - loss: 0.0274 97/133 [====================>.........] - ETA: 0s - loss: 0.0339 133/133 [==============================] - 0s 1ms/step - loss: 0.0349 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0306 50/133 [==========>...................] - ETA: 0s - loss: 0.0388 99/133 [=====================>........] - ETA: 0s - loss: 0.0368 133/133 [==============================] - 0s 1ms/step - loss: 0.0328 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0048 49/133 [==========>...................] - ETA: 0s - loss: 0.0295 98/133 [=====================>........] - ETA: 0s - loss: 0.0291 133/133 [==============================] - 0s 1ms/step - loss: 0.0314 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0195 49/133 [==========>...................] - ETA: 0s - loss: 0.0351 95/133 [====================>.........] - ETA: 0s - loss: 0.0317 133/133 [==============================] - 0s 1ms/step - loss: 0.0288 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0080 49/133 [==========>...................] - ETA: 0s - loss: 0.0279 98/133 [=====================>........] - ETA: 0s - loss: 0.0288 133/133 [==============================] - 0s 1ms/step - loss: 0.0265 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0167 50/133 [==========>...................] - ETA: 0s - loss: 0.0232 99/133 [=====================>........] - ETA: 0s - loss: 0.0238 133/133 [==============================] - 0s 1ms/step - loss: 0.0257 -> test with GAN.predict GAN tn, fp: 325, 7 GAN fn, tp: 1, 13 GAN f1 score: 0.765 GAN cohens kappa score: 0.753 -> test with 'LR' LR tn, fp: 189, 143 LR fn, tp: 3, 11 LR f1 score: 0.131 LR cohens kappa score: 0.061 LR average precision score: 0.067 -> test with 'RF' RF tn, fp: 331, 1 RF fn, tp: 6, 8 RF f1 score: 0.696 RF cohens kappa score: 0.686 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 3, 11 GB f1 score: 0.815 GB cohens kappa score: 0.807 -> test with 'KNN' KNN tn, fp: 308, 24 KNN fn, tp: 0, 14 KNN f1 score: 0.538 KNN cohens kappa score: 0.509 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 19s - loss: 0.0133 49/133 [==========>...................] - ETA: 0s - loss: 0.1173  98/133 [=====================>........] - ETA: 0s - loss: 0.0800 133/133 [==============================] - 0s 1ms/step - loss: 0.0756 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0406 45/133 [=========>....................] - ETA: 0s - loss: 0.0430 93/133 [===================>..........] - ETA: 0s - loss: 0.0402 133/133 [==============================] - 0s 1ms/step - loss: 0.0415 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0081 50/133 [==========>...................] - ETA: 0s - loss: 0.0253 99/133 [=====================>........] - ETA: 0s - loss: 0.0299 133/133 [==============================] - 0s 1ms/step - loss: 0.0333 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0110 50/133 [==========>...................] - ETA: 0s - loss: 0.0291 99/133 [=====================>........] - ETA: 0s - loss: 0.0323 133/133 [==============================] - 0s 1ms/step - loss: 0.0284 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0617 50/133 [==========>...................] - ETA: 0s - loss: 0.0344 99/133 [=====================>........] - ETA: 0s - loss: 0.0288 133/133 [==============================] - 0s 1ms/step - loss: 0.0264 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0053 50/133 [==========>...................] - ETA: 0s - loss: 0.0264 99/133 [=====================>........] - ETA: 0s - loss: 0.0253 133/133 [==============================] - 0s 1ms/step - loss: 0.0231 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0050 50/133 [==========>...................] - ETA: 0s - loss: 0.0181 99/133 [=====================>........] - ETA: 0s - loss: 0.0189 133/133 [==============================] - 0s 1ms/step - loss: 0.0208 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0074 50/133 [==========>...................] - ETA: 0s - loss: 0.0220 93/133 [===================>..........] - ETA: 0s - loss: 0.0214 133/133 [==============================] - 0s 1ms/step - loss: 0.0195 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0032 44/133 [========>.....................] - ETA: 0s - loss: 0.0085 92/133 [===================>..........] - ETA: 0s - loss: 0.0132 133/133 [==============================] - 0s 1ms/step - loss: 0.0169 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0025 50/133 [==========>...................] - ETA: 0s - loss: 0.0201 99/133 [=====================>........] - ETA: 0s - loss: 0.0171 133/133 [==============================] - 0s 1ms/step - loss: 0.0177 -> test with GAN.predict GAN tn, fp: 330, 2 GAN fn, tp: 6, 8 GAN f1 score: 0.667 GAN cohens kappa score: 0.655 -> test with 'LR' LR tn, fp: 182, 150 LR fn, tp: 6, 8 LR f1 score: 0.093 LR cohens kappa score: 0.020 LR average precision score: 0.055 -> test with 'RF' RF tn, fp: 331, 1 RF fn, tp: 5, 9 RF f1 score: 0.750 RF cohens kappa score: 0.741 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 7, 7 GB f1 score: 0.609 GB cohens kappa score: 0.596 -> test with 'KNN' KNN tn, fp: 304, 28 KNN fn, tp: 2, 12 KNN f1 score: 0.444 KNN cohens kappa score: 0.409 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 21s - loss: 0.0105 39/133 [=======>......................] - ETA: 0s - loss: 0.0446  76/133 [================>.............] - ETA: 0s - loss: 0.0454 116/133 [=========================>....] - ETA: 0s - loss: 0.0557 133/133 [==============================] - 0s 1ms/step - loss: 0.0537 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0021 40/133 [========>.....................] - ETA: 0s - loss: 0.0483 78/133 [================>.............] - ETA: 0s - loss: 0.0377 118/133 [=========================>....] - ETA: 0s - loss: 0.0415 133/133 [==============================] - 0s 1ms/step - loss: 0.0390 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0030 39/133 [=======>......................] - ETA: 0s - loss: 0.0261 72/133 [===============>..............] - ETA: 0s - loss: 0.0313 110/133 [=======================>......] - ETA: 0s - loss: 0.0326 133/133 [==============================] - 0s 1ms/step - loss: 0.0349 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0062 37/133 [=======>......................] - ETA: 0s - loss: 0.0333 74/133 [===============>..............] - ETA: 0s - loss: 0.0297 112/133 [========================>.....] - ETA: 0s - loss: 0.0307 133/133 [==============================] - 0s 1ms/step - loss: 0.0307 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0367 41/133 [========>.....................] - ETA: 0s - loss: 0.0264 80/133 [=================>............] - ETA: 0s - loss: 0.0259 121/133 [==========================>...] - ETA: 0s - loss: 0.0290 133/133 [==============================] - 0s 1ms/step - loss: 0.0276 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0054 41/133 [========>.....................] - ETA: 0s - loss: 0.0218 84/133 [=================>............] - ETA: 0s - loss: 0.0234 128/133 [===========================>..] - ETA: 0s - loss: 0.0252 133/133 [==============================] - 0s 1ms/step - loss: 0.0260 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0077 45/133 [=========>....................] - ETA: 0s - loss: 0.0331 90/133 [===================>..........] - ETA: 0s - loss: 0.0257 133/133 [==============================] - 0s 1ms/step - loss: 0.0245 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0449 45/133 [=========>....................] - ETA: 0s - loss: 0.0169 89/133 [===================>..........] - ETA: 0s - loss: 0.0224 131/133 [============================>.] - ETA: 0s - loss: 0.0226 133/133 [==============================] - 0s 1ms/step - loss: 0.0225 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0133 40/133 [========>.....................] - ETA: 0s - loss: 0.0232 78/133 [================>.............] - ETA: 0s - loss: 0.0243 117/133 [=========================>....] - ETA: 0s - loss: 0.0224 133/133 [==============================] - 0s 1ms/step - loss: 0.0206 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0056 46/133 [=========>....................] - ETA: 0s - loss: 0.0129 90/133 [===================>..........] - ETA: 0s - loss: 0.0142 133/133 [==============================] - 0s 1ms/step - loss: 0.0204 -> test with GAN.predict GAN tn, fp: 326, 6 GAN fn, tp: 1, 13 GAN f1 score: 0.788 GAN cohens kappa score: 0.778 -> test with 'LR' LR tn, fp: 175, 157 LR fn, tp: 2, 12 LR f1 score: 0.131 LR cohens kappa score: 0.061 LR average precision score: 0.073 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 7, 7 RF f1 score: 0.667 RF cohens kappa score: 0.657 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 3, 11 GB f1 score: 0.846 GB cohens kappa score: 0.840 -> test with 'KNN' KNN tn, fp: 309, 23 KNN fn, tp: 1, 13 KNN f1 score: 0.520 KNN cohens kappa score: 0.490 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 20s - loss: 0.0028 49/133 [==========>...................] - ETA: 0s - loss: 0.0333  98/133 [=====================>........] - ETA: 0s - loss: 0.0350 133/133 [==============================] - 0s 1ms/step - loss: 0.0393 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0078 46/133 [=========>....................] - ETA: 0s - loss: 0.0306 95/133 [====================>.........] - ETA: 0s - loss: 0.0300 133/133 [==============================] - 0s 1ms/step - loss: 0.0297 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0027 49/133 [==========>...................] - ETA: 0s - loss: 0.0318 91/133 [===================>..........] - ETA: 0s - loss: 0.0278 133/133 [==============================] - 0s 1ms/step - loss: 0.0277 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0499 50/133 [==========>...................] - ETA: 0s - loss: 0.0249 95/133 [====================>.........] - ETA: 0s - loss: 0.0185 133/133 [==============================] - 0s 1ms/step - loss: 0.0251 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0028 45/133 [=========>....................] - ETA: 0s - loss: 0.0282 93/133 [===================>..........] - ETA: 0s - loss: 0.0238 133/133 [==============================] - 0s 1ms/step - loss: 0.0247 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0050 49/133 [==========>...................] - ETA: 0s - loss: 0.0250 98/133 [=====================>........] - ETA: 0s - loss: 0.0219 133/133 [==============================] - 0s 1ms/step - loss: 0.0222 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0080 50/133 [==========>...................] - ETA: 0s - loss: 0.0254 99/133 [=====================>........] - ETA: 0s - loss: 0.0190 133/133 [==============================] - 0s 1ms/step - loss: 0.0211 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0037 50/133 [==========>...................] - ETA: 0s - loss: 0.0155 99/133 [=====================>........] - ETA: 0s - loss: 0.0181 133/133 [==============================] - 0s 1ms/step - loss: 0.0190 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0012 50/133 [==========>...................] - ETA: 0s - loss: 0.0174 99/133 [=====================>........] - ETA: 0s - loss: 0.0150 133/133 [==============================] - 0s 1ms/step - loss: 0.0178 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0158 49/133 [==========>...................] - ETA: 0s - loss: 0.0177 98/133 [=====================>........] - ETA: 0s - loss: 0.0151 133/133 [==============================] - 0s 1ms/step - loss: 0.0176 -> test with GAN.predict GAN tn, fp: 328, 3 GAN fn, tp: 2, 11 GAN f1 score: 0.815 GAN cohens kappa score: 0.807 -> test with 'LR' LR tn, fp: 168, 163 LR fn, tp: 5, 8 LR f1 score: 0.087 LR cohens kappa score: 0.018 LR average precision score: 0.052 -> test with 'RF' RF tn, fp: 330, 1 RF fn, tp: 5, 8 RF f1 score: 0.727 RF cohens kappa score: 0.719 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 4, 9 GB f1 score: 0.720 GB cohens kappa score: 0.709 -> test with 'KNN' KNN tn, fp: 311, 20 KNN fn, tp: 0, 13 KNN f1 score: 0.565 KNN cohens kappa score: 0.540 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0037 48/133 [=========>....................] - ETA: 0s - loss: 0.0744  97/133 [====================>.........] - ETA: 0s - loss: 0.0619 133/133 [==============================] - 0s 1ms/step - loss: 0.0616 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0252 49/133 [==========>...................] - ETA: 0s - loss: 0.0193 98/133 [=====================>........] - ETA: 0s - loss: 0.0450 133/133 [==============================] - 0s 1ms/step - loss: 0.0448 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0493 50/133 [==========>...................] - ETA: 0s - loss: 0.0465 99/133 [=====================>........] - ETA: 0s - loss: 0.0382 133/133 [==============================] - 0s 1ms/step - loss: 0.0411 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0041 50/133 [==========>...................] - ETA: 0s - loss: 0.0251 99/133 [=====================>........] - ETA: 0s - loss: 0.0326 133/133 [==============================] - 0s 1ms/step - loss: 0.0368 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0124 49/133 [==========>...................] - ETA: 0s - loss: 0.0389 98/133 [=====================>........] - ETA: 0s - loss: 0.0299 133/133 [==============================] - 0s 1ms/step - loss: 0.0342 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0052 48/133 [=========>....................] - ETA: 0s - loss: 0.0197 97/133 [====================>.........] - ETA: 0s - loss: 0.0272 133/133 [==============================] - 0s 1ms/step - loss: 0.0328 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0093 50/133 [==========>...................] - ETA: 0s - loss: 0.0281 99/133 [=====================>........] - ETA: 0s - loss: 0.0305 133/133 [==============================] - 0s 1ms/step - loss: 0.0308 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0036 50/133 [==========>...................] - ETA: 0s - loss: 0.0240 99/133 [=====================>........] - ETA: 0s - loss: 0.0279 133/133 [==============================] - 0s 1ms/step - loss: 0.0281 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0084 50/133 [==========>...................] - ETA: 0s - loss: 0.0191 99/133 [=====================>........] - ETA: 0s - loss: 0.0253 133/133 [==============================] - 0s 1ms/step - loss: 0.0268 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.1208 50/133 [==========>...................] - ETA: 0s - loss: 0.0313 99/133 [=====================>........] - ETA: 0s - loss: 0.0285 133/133 [==============================] - 0s 1ms/step - loss: 0.0260 -> test with GAN.predict GAN tn, fp: 326, 6 GAN fn, tp: 4, 10 GAN f1 score: 0.667 GAN cohens kappa score: 0.652 -> test with 'LR' LR tn, fp: 179, 153 LR fn, tp: 3, 11 LR f1 score: 0.124 LR cohens kappa score: 0.053 LR average precision score: 0.066 -> test with 'RF' RF tn, fp: 330, 2 RF fn, tp: 4, 10 RF f1 score: 0.769 RF cohens kappa score: 0.760 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 4, 10 GB f1 score: 0.833 GB cohens kappa score: 0.828 -> test with 'KNN' KNN tn, fp: 316, 16 KNN fn, tp: 0, 14 KNN f1 score: 0.636 KNN cohens kappa score: 0.615 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 21s - loss: 0.0286 48/133 [=========>....................] - ETA: 0s - loss: 0.0903  94/133 [====================>.........] - ETA: 0s - loss: 0.0732 133/133 [==============================] - 0s 1ms/step - loss: 0.0657 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0363 49/133 [==========>...................] - ETA: 0s - loss: 0.0453 96/133 [====================>.........] - ETA: 0s - loss: 0.0501 133/133 [==============================] - 0s 1ms/step - loss: 0.0478 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0478 49/133 [==========>...................] - ETA: 0s - loss: 0.0588 97/133 [====================>.........] - ETA: 0s - loss: 0.0494 133/133 [==============================] - 0s 1ms/step - loss: 0.0435 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0043 49/133 [==========>...................] - ETA: 0s - loss: 0.0279 97/133 [====================>.........] - ETA: 0s - loss: 0.0376 133/133 [==============================] - 0s 1ms/step - loss: 0.0379 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0076 49/133 [==========>...................] - ETA: 0s - loss: 0.0209 97/133 [====================>.........] - ETA: 0s - loss: 0.0332 133/133 [==============================] - 0s 1ms/step - loss: 0.0352 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0090 48/133 [=========>....................] - ETA: 0s - loss: 0.0373 94/133 [====================>.........] - ETA: 0s - loss: 0.0350 133/133 [==============================] - 0s 1ms/step - loss: 0.0335 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0082 47/133 [=========>....................] - ETA: 0s - loss: 0.0250 95/133 [====================>.........] - ETA: 0s - loss: 0.0308 133/133 [==============================] - 0s 1ms/step - loss: 0.0303 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0187 44/133 [========>.....................] - ETA: 0s - loss: 0.0270 86/133 [==================>...........] - ETA: 0s - loss: 0.0296 133/133 [==============================] - ETA: 0s - loss: 0.0281 133/133 [==============================] - 0s 1ms/step - loss: 0.0281 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0165 49/133 [==========>...................] - ETA: 0s - loss: 0.0228 97/133 [====================>.........] - ETA: 0s - loss: 0.0274 133/133 [==============================] - 0s 1ms/step - loss: 0.0269 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0035 49/133 [==========>...................] - ETA: 0s - loss: 0.0307 97/133 [====================>.........] - ETA: 0s - loss: 0.0272 133/133 [==============================] - 0s 1ms/step - loss: 0.0261 -> test with GAN.predict GAN tn, fp: 325, 7 GAN fn, tp: 4, 10 GAN f1 score: 0.645 GAN cohens kappa score: 0.629 -> test with 'LR' LR tn, fp: 188, 144 LR fn, tp: 6, 8 LR f1 score: 0.096 LR cohens kappa score: 0.024 LR average precision score: 0.056 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 9, 5 RF f1 score: 0.526 RF cohens kappa score: 0.516 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 6, 8 GB f1 score: 0.696 GB cohens kappa score: 0.686 -> test with 'KNN' KNN tn, fp: 297, 35 KNN fn, tp: 0, 14 KNN f1 score: 0.444 KNN cohens kappa score: 0.407 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 19s - loss: 0.0121 49/133 [==========>...................] - ETA: 0s - loss: 0.0435  98/133 [=====================>........] - ETA: 0s - loss: 0.0411 133/133 [==============================] - 0s 1ms/step - loss: 0.0391 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0481 48/133 [=========>....................] - ETA: 0s - loss: 0.0404 97/133 [====================>.........] - ETA: 0s - loss: 0.0358 133/133 [==============================] - 0s 1ms/step - loss: 0.0339 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0151 50/133 [==========>...................] - ETA: 0s - loss: 0.0284 98/133 [=====================>........] - ETA: 0s - loss: 0.0303 133/133 [==============================] - 0s 1ms/step - loss: 0.0306 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0143 49/133 [==========>...................] - ETA: 0s - loss: 0.0219 97/133 [====================>.........] - ETA: 0s - loss: 0.0332 133/133 [==============================] - 0s 1ms/step - loss: 0.0295 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0279 49/133 [==========>...................] - ETA: 0s - loss: 0.0306 95/133 [====================>.........] - ETA: 0s - loss: 0.0288 133/133 [==============================] - 0s 1ms/step - loss: 0.0261 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0059 49/133 [==========>...................] - ETA: 0s - loss: 0.0306 97/133 [====================>.........] - ETA: 0s - loss: 0.0265 133/133 [==============================] - 0s 1ms/step - loss: 0.0249 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0416 49/133 [==========>...................] - ETA: 0s - loss: 0.0225 96/133 [====================>.........] - ETA: 0s - loss: 0.0259 133/133 [==============================] - 0s 1ms/step - loss: 0.0233 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0125 49/133 [==========>...................] - ETA: 0s - loss: 0.0247 97/133 [====================>.........] - ETA: 0s - loss: 0.0208 133/133 [==============================] - 0s 1ms/step - loss: 0.0217 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0061 50/133 [==========>...................] - ETA: 0s - loss: 0.0226 99/133 [=====================>........] - ETA: 0s - loss: 0.0220 133/133 [==============================] - 0s 1ms/step - loss: 0.0212 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0055 50/133 [==========>...................] - ETA: 0s - loss: 0.0198 99/133 [=====================>........] - ETA: 0s - loss: 0.0222 133/133 [==============================] - 0s 1ms/step - loss: 0.0202 -> test with GAN.predict GAN tn, fp: 323, 9 GAN fn, tp: 1, 13 GAN f1 score: 0.722 GAN cohens kappa score: 0.708 -> test with 'LR' LR tn, fp: 172, 160 LR fn, tp: 4, 10 LR f1 score: 0.109 LR cohens kappa score: 0.037 LR average precision score: 0.067 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 7, 7 RF f1 score: 0.667 RF cohens kappa score: 0.657 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 4, 10 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> test with 'KNN' KNN tn, fp: 299, 33 KNN fn, tp: 1, 13 KNN f1 score: 0.433 KNN cohens kappa score: 0.396 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 22s - loss: 0.0186 48/133 [=========>....................] - ETA: 0s - loss: 0.0299  97/133 [====================>.........] - ETA: 0s - loss: 0.0343 133/133 [==============================] - 0s 1ms/step - loss: 0.0380 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0116 50/133 [==========>...................] - ETA: 0s - loss: 0.0266 98/133 [=====================>........] - ETA: 0s - loss: 0.0347 133/133 [==============================] - 0s 1ms/step - loss: 0.0324 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0134 48/133 [=========>....................] - ETA: 0s - loss: 0.0292 90/133 [===================>..........] - ETA: 0s - loss: 0.0267 132/133 [============================>.] - ETA: 0s - loss: 0.0289 133/133 [==============================] - 0s 1ms/step - loss: 0.0288 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0122 47/133 [=========>....................] - ETA: 0s - loss: 0.0241 96/133 [====================>.........] - ETA: 0s - loss: 0.0241 133/133 [==============================] - 0s 1ms/step - loss: 0.0263 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0168 45/133 [=========>....................] - ETA: 0s - loss: 0.0204 90/133 [===================>..........] - ETA: 0s - loss: 0.0273 133/133 [==============================] - 0s 1ms/step - loss: 0.0256 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0205 49/133 [==========>...................] - ETA: 0s - loss: 0.0299 96/133 [====================>.........] - ETA: 0s - loss: 0.0252 133/133 [==============================] - 0s 1ms/step - loss: 0.0234 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 4.9196e-04 50/133 [==========>...................] - ETA: 0s - loss: 0.0172  97/133 [====================>.........] - ETA: 0s - loss: 0.0192 133/133 [==============================] - 0s 1ms/step - loss: 0.0218 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0130 50/133 [==========>...................] - ETA: 0s - loss: 0.0178 99/133 [=====================>........] - ETA: 0s - loss: 0.0194 133/133 [==============================] - 0s 1ms/step - loss: 0.0218 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0020 50/133 [==========>...................] - ETA: 0s - loss: 0.0280 99/133 [=====================>........] - ETA: 0s - loss: 0.0203 133/133 [==============================] - 0s 1ms/step - loss: 0.0212 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0118 50/133 [==========>...................] - ETA: 0s - loss: 0.0200 99/133 [=====================>........] - ETA: 0s - loss: 0.0186 133/133 [==============================] - 0s 1ms/step - loss: 0.0207 -> test with GAN.predict GAN tn, fp: 330, 2 GAN fn, tp: 1, 13 GAN f1 score: 0.897 GAN cohens kappa score: 0.892 -> test with 'LR' LR tn, fp: 191, 141 LR fn, tp: 7, 7 LR f1 score: 0.086 LR cohens kappa score: 0.013 LR average precision score: 0.056 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 6, 8 RF f1 score: 0.727 RF cohens kappa score: 0.719 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 7, 7 GB f1 score: 0.609 GB cohens kappa score: 0.596 -> test with 'KNN' KNN tn, fp: 309, 23 KNN fn, tp: 0, 14 KNN f1 score: 0.549 KNN cohens kappa score: 0.521 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 19s - loss: 0.0234 49/133 [==========>...................] - ETA: 0s - loss: 0.0371  98/133 [=====================>........] - ETA: 0s - loss: 0.0370 133/133 [==============================] - 0s 1ms/step - loss: 0.0351 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0099 49/133 [==========>...................] - ETA: 0s - loss: 0.0243 98/133 [=====================>........] - ETA: 0s - loss: 0.0240 133/133 [==============================] - 0s 1ms/step - loss: 0.0257 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0351 49/133 [==========>...................] - ETA: 0s - loss: 0.0235 96/133 [====================>.........] - ETA: 0s - loss: 0.0241 133/133 [==============================] - 0s 1ms/step - loss: 0.0240 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0480 48/133 [=========>....................] - ETA: 0s - loss: 0.0200 96/133 [====================>.........] - ETA: 0s - loss: 0.0234 133/133 [==============================] - 0s 1ms/step - loss: 0.0208 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.1196 50/133 [==========>...................] - ETA: 0s - loss: 0.0232 99/133 [=====================>........] - ETA: 0s - loss: 0.0206 133/133 [==============================] - 0s 1ms/step - loss: 0.0200 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0013 49/133 [==========>...................] - ETA: 0s - loss: 0.0213 96/133 [====================>.........] - ETA: 0s - loss: 0.0203 133/133 [==============================] - 0s 1ms/step - loss: 0.0179 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0455 43/133 [========>.....................] - ETA: 0s - loss: 0.0115 87/133 [==================>...........] - ETA: 0s - loss: 0.0136 133/133 [==============================] - 0s 1ms/step - loss: 0.0176 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0056 50/133 [==========>...................] - ETA: 0s - loss: 0.0140 93/133 [===================>..........] - ETA: 0s - loss: 0.0153 133/133 [==============================] - 0s 1ms/step - loss: 0.0157 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0045 50/133 [==========>...................] - ETA: 0s - loss: 0.0128 99/133 [=====================>........] - ETA: 0s - loss: 0.0124 133/133 [==============================] - 0s 1ms/step - loss: 0.0147 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0016 50/133 [==========>...................] - ETA: 0s - loss: 0.0173 97/133 [====================>.........] - ETA: 0s - loss: 0.0139 133/133 [==============================] - 0s 1ms/step - loss: 0.0141 -> test with GAN.predict GAN tn, fp: 325, 6 GAN fn, tp: 3, 10 GAN f1 score: 0.690 GAN cohens kappa score: 0.676 -> test with 'LR' LR tn, fp: 177, 154 LR fn, tp: 2, 11 LR f1 score: 0.124 LR cohens kappa score: 0.058 LR average precision score: 0.082 -> test with 'RF' RF tn, fp: 328, 3 RF fn, tp: 7, 6 RF f1 score: 0.545 RF cohens kappa score: 0.531 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 5, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 312, 19 KNN fn, tp: 1, 12 KNN f1 score: 0.545 KNN cohens kappa score: 0.520 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0601 47/133 [=========>....................] - ETA: 0s - loss: 0.0356  95/133 [====================>.........] - ETA: 0s - loss: 0.0367 133/133 [==============================] - 0s 1ms/step - loss: 0.0366 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0231 49/133 [==========>...................] - ETA: 0s - loss: 0.0332 96/133 [====================>.........] - ETA: 0s - loss: 0.0308 133/133 [==============================] - 0s 1ms/step - loss: 0.0301 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0338 49/133 [==========>...................] - ETA: 0s - loss: 0.0345 97/133 [====================>.........] - ETA: 0s - loss: 0.0269 133/133 [==============================] - 0s 1ms/step - loss: 0.0266 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0874 48/133 [=========>....................] - ETA: 0s - loss: 0.0204 96/133 [====================>.........] - ETA: 0s - loss: 0.0223 133/133 [==============================] - 0s 1ms/step - loss: 0.0227 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0191 43/133 [========>.....................] - ETA: 0s - loss: 0.0174 84/133 [=================>............] - ETA: 0s - loss: 0.0195 133/133 [==============================] - ETA: 0s - loss: 0.0206 133/133 [==============================] - 0s 1ms/step - loss: 0.0206 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0439 50/133 [==========>...................] - ETA: 0s - loss: 0.0183 99/133 [=====================>........] - ETA: 0s - loss: 0.0220 133/133 [==============================] - 0s 1ms/step - loss: 0.0209 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0307 50/133 [==========>...................] - ETA: 0s - loss: 0.0208 99/133 [=====================>........] - ETA: 0s - loss: 0.0184 133/133 [==============================] - 0s 1ms/step - loss: 0.0180 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0025 50/133 [==========>...................] - ETA: 0s - loss: 0.0200 99/133 [=====================>........] - ETA: 0s - loss: 0.0161 133/133 [==============================] - 0s 1ms/step - loss: 0.0165 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0047 50/133 [==========>...................] - ETA: 0s - loss: 0.0146 99/133 [=====================>........] - ETA: 0s - loss: 0.0149 133/133 [==============================] - 0s 1ms/step - loss: 0.0161 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0275 50/133 [==========>...................] - ETA: 0s - loss: 0.0127 99/133 [=====================>........] - ETA: 0s - loss: 0.0130 133/133 [==============================] - 0s 1ms/step - loss: 0.0136 -> test with GAN.predict GAN tn, fp: 323, 9 GAN fn, tp: 2, 12 GAN f1 score: 0.686 GAN cohens kappa score: 0.670 -> test with 'LR' LR tn, fp: 187, 145 LR fn, tp: 8, 6 LR f1 score: 0.073 LR cohens kappa score: -0.001 LR average precision score: 0.051 -> test with 'RF' RF tn, fp: 330, 2 RF fn, tp: 9, 5 RF f1 score: 0.476 RF cohens kappa score: 0.462 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 5, 9 GB f1 score: 0.720 GB cohens kappa score: 0.710 -> test with 'KNN' KNN tn, fp: 306, 26 KNN fn, tp: 2, 12 KNN f1 score: 0.462 KNN cohens kappa score: 0.428 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0131 48/133 [=========>....................] - ETA: 0s - loss: 0.0431  97/133 [====================>.........] - ETA: 0s - loss: 0.0370 133/133 [==============================] - 0s 1ms/step - loss: 0.0390 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0196 50/133 [==========>...................] - ETA: 0s - loss: 0.0220 99/133 [=====================>........] - ETA: 0s - loss: 0.0286 133/133 [==============================] - 0s 1ms/step - loss: 0.0290 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0070 50/133 [==========>...................] - ETA: 0s - loss: 0.0265 98/133 [=====================>........] - ETA: 0s - loss: 0.0275 133/133 [==============================] - 0s 1ms/step - loss: 0.0250 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0066 43/133 [========>.....................] - ETA: 0s - loss: 0.0207 86/133 [==================>...........] - ETA: 0s - loss: 0.0233 133/133 [==============================] - ETA: 0s - loss: 0.0236 133/133 [==============================] - 0s 1ms/step - loss: 0.0236 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0206 49/133 [==========>...................] - ETA: 0s - loss: 0.0187 98/133 [=====================>........] - ETA: 0s - loss: 0.0167 133/133 [==============================] - 0s 1ms/step - loss: 0.0206 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0196 47/133 [=========>....................] - ETA: 0s - loss: 0.0134 95/133 [====================>.........] - ETA: 0s - loss: 0.0188 133/133 [==============================] - 0s 1ms/step - loss: 0.0195 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0041 49/133 [==========>...................] - ETA: 0s - loss: 0.0183 97/133 [====================>.........] - ETA: 0s - loss: 0.0194 133/133 [==============================] - 0s 1ms/step - loss: 0.0189 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0158 50/133 [==========>...................] - ETA: 0s - loss: 0.0133 99/133 [=====================>........] - ETA: 0s - loss: 0.0138 133/133 [==============================] - 0s 1ms/step - loss: 0.0170 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.1172 50/133 [==========>...................] - ETA: 0s - loss: 0.0207 98/133 [=====================>........] - ETA: 0s - loss: 0.0154 133/133 [==============================] - 0s 1ms/step - loss: 0.0164 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0041 49/133 [==========>...................] - ETA: 0s - loss: 0.0268 97/133 [====================>.........] - ETA: 0s - loss: 0.0188 133/133 [==============================] - 0s 1ms/step - loss: 0.0156 -> test with GAN.predict GAN tn, fp: 330, 2 GAN fn, tp: 1, 13 GAN f1 score: 0.897 GAN cohens kappa score: 0.892 -> test with 'LR' LR tn, fp: 186, 146 LR fn, tp: 4, 10 LR f1 score: 0.118 LR cohens kappa score: 0.047 LR average precision score: 0.065 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 5, 9 RF f1 score: 0.783 RF cohens kappa score: 0.775 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 6, 8 GB f1 score: 0.640 GB cohens kappa score: 0.627 -> test with 'KNN' KNN tn, fp: 311, 21 KNN fn, tp: 1, 13 KNN f1 score: 0.542 KNN cohens kappa score: 0.514 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0119 49/133 [==========>...................] - ETA: 0s - loss: 0.0604  98/133 [=====================>........] - ETA: 0s - loss: 0.0579 133/133 [==============================] - 0s 1ms/step - loss: 0.0601 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.1930 49/133 [==========>...................] - ETA: 0s - loss: 0.0487 97/133 [====================>.........] - ETA: 0s - loss: 0.0438 133/133 [==============================] - 0s 1ms/step - loss: 0.0426 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.1631 50/133 [==========>...................] - ETA: 0s - loss: 0.0392 99/133 [=====================>........] - ETA: 0s - loss: 0.0366 133/133 [==============================] - 0s 1ms/step - loss: 0.0384 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0067 47/133 [=========>....................] - ETA: 0s - loss: 0.0347 94/133 [====================>.........] - ETA: 0s - loss: 0.0339 133/133 [==============================] - 0s 1ms/step - loss: 0.0319 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0062 49/133 [==========>...................] - ETA: 0s - loss: 0.0288 96/133 [====================>.........] - ETA: 0s - loss: 0.0237 133/133 [==============================] - 0s 1ms/step - loss: 0.0280 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0603 45/133 [=========>....................] - ETA: 0s - loss: 0.0238 87/133 [==================>...........] - ETA: 0s - loss: 0.0240 133/133 [==============================] - ETA: 0s - loss: 0.0257 133/133 [==============================] - 0s 1ms/step - loss: 0.0257 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0352 50/133 [==========>...................] - ETA: 0s - loss: 0.0211 98/133 [=====================>........] - ETA: 0s - loss: 0.0201 133/133 [==============================] - 0s 1ms/step - loss: 0.0223 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0303 49/133 [==========>...................] - ETA: 0s - loss: 0.0285 98/133 [=====================>........] - ETA: 0s - loss: 0.0239 133/133 [==============================] - 0s 1ms/step - loss: 0.0236 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0085 49/133 [==========>...................] - ETA: 0s - loss: 0.0193 97/133 [====================>.........] - ETA: 0s - loss: 0.0197 133/133 [==============================] - 0s 1ms/step - loss: 0.0196 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0135 50/133 [==========>...................] - ETA: 0s - loss: 0.0187 98/133 [=====================>........] - ETA: 0s - loss: 0.0181 133/133 [==============================] - 0s 1ms/step - loss: 0.0186 -> test with GAN.predict GAN tn, fp: 327, 5 GAN fn, tp: 5, 9 GAN f1 score: 0.643 GAN cohens kappa score: 0.628 -> test with 'LR' LR tn, fp: 162, 170 LR fn, tp: 3, 11 LR f1 score: 0.113 LR cohens kappa score: 0.041 LR average precision score: 0.076 -> test with 'RF' RF tn, fp: 329, 3 RF fn, tp: 4, 10 RF f1 score: 0.741 RF cohens kappa score: 0.730 -> test with 'GB' GB tn, fp: 328, 4 GB fn, tp: 2, 12 GB f1 score: 0.800 GB cohens kappa score: 0.791 -> test with 'KNN' KNN tn, fp: 314, 18 KNN fn, tp: 0, 14 KNN f1 score: 0.609 KNN cohens kappa score: 0.585 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0173 47/133 [=========>....................] - ETA: 0s - loss: 0.0296  94/133 [====================>.........] - ETA: 0s - loss: 0.0351 133/133 [==============================] - 0s 1ms/step - loss: 0.0305 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0036 49/133 [==========>...................] - ETA: 0s - loss: 0.0356 98/133 [=====================>........] - ETA: 0s - loss: 0.0268 133/133 [==============================] - 0s 1ms/step - loss: 0.0260 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0181 50/133 [==========>...................] - ETA: 0s - loss: 0.0332 99/133 [=====================>........] - ETA: 0s - loss: 0.0253 133/133 [==============================] - 0s 1ms/step - loss: 0.0220 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0056 49/133 [==========>...................] - ETA: 0s - loss: 0.0173 95/133 [====================>.........] - ETA: 0s - loss: 0.0218 133/133 [==============================] - 0s 1ms/step - loss: 0.0205 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.2358 50/133 [==========>...................] - ETA: 0s - loss: 0.0239 99/133 [=====================>........] - ETA: 0s - loss: 0.0189 133/133 [==============================] - 0s 1ms/step - loss: 0.0204 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0051 50/133 [==========>...................] - ETA: 0s - loss: 0.0193 98/133 [=====================>........] - ETA: 0s - loss: 0.0153 133/133 [==============================] - 0s 1ms/step - loss: 0.0169 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0070 50/133 [==========>...................] - ETA: 0s - loss: 0.0186 94/133 [====================>.........] - ETA: 0s - loss: 0.0148 133/133 [==============================] - 0s 1ms/step - loss: 0.0167 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0034 48/133 [=========>....................] - ETA: 0s - loss: 0.0175 96/133 [====================>.........] - ETA: 0s - loss: 0.0158 133/133 [==============================] - 0s 1ms/step - loss: 0.0159 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0067 46/133 [=========>....................] - ETA: 0s - loss: 0.0134 89/133 [===================>..........] - ETA: 0s - loss: 0.0162 133/133 [==============================] - ETA: 0s - loss: 0.0144 133/133 [==============================] - 0s 1ms/step - loss: 0.0144 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0017 50/133 [==========>...................] - ETA: 0s - loss: 0.0080 98/133 [=====================>........] - ETA: 0s - loss: 0.0105 133/133 [==============================] - 0s 1ms/step - loss: 0.0136 -> test with GAN.predict GAN tn, fp: 331, 1 GAN fn, tp: 6, 8 GAN f1 score: 0.696 GAN cohens kappa score: 0.686 -> test with 'LR' LR tn, fp: 170, 162 LR fn, tp: 3, 11 LR f1 score: 0.118 LR cohens kappa score: 0.046 LR average precision score: 0.069 -> test with 'RF' RF tn, fp: 332, 0 RF fn, tp: 7, 7 RF f1 score: 0.667 RF cohens kappa score: 0.657 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 8, 6 GB f1 score: 0.571 GB cohens kappa score: 0.560 -> test with 'KNN' KNN tn, fp: 310, 22 KNN fn, tp: 0, 14 KNN f1 score: 0.560 KNN cohens kappa score: 0.533 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 19s - loss: 0.0262 49/133 [==========>...................] - ETA: 0s - loss: 0.0647  98/133 [=====================>........] - ETA: 0s - loss: 0.0506 133/133 [==============================] - 0s 1ms/step - loss: 0.0458 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0185 49/133 [==========>...................] - ETA: 0s - loss: 0.0339 98/133 [=====================>........] - ETA: 0s - loss: 0.0366 133/133 [==============================] - 0s 1ms/step - loss: 0.0357 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0032 50/133 [==========>...................] - ETA: 0s - loss: 0.0381 99/133 [=====================>........] - ETA: 0s - loss: 0.0307 133/133 [==============================] - 0s 1ms/step - loss: 0.0311 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0015 50/133 [==========>...................] - ETA: 0s - loss: 0.0170 99/133 [=====================>........] - ETA: 0s - loss: 0.0252 133/133 [==============================] - 0s 1ms/step - loss: 0.0284 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0083 49/133 [==========>...................] - ETA: 0s - loss: 0.0287 98/133 [=====================>........] - ETA: 0s - loss: 0.0302 133/133 [==============================] - 0s 1ms/step - loss: 0.0273 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0070 50/133 [==========>...................] - ETA: 0s - loss: 0.0259 96/133 [====================>.........] - ETA: 0s - loss: 0.0254 133/133 [==============================] - 0s 1ms/step - loss: 0.0242 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0080 50/133 [==========>...................] - ETA: 0s - loss: 0.0187 99/133 [=====================>........] - ETA: 0s - loss: 0.0232 133/133 [==============================] - 0s 1ms/step - loss: 0.0223 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0142 50/133 [==========>...................] - ETA: 0s - loss: 0.0264 99/133 [=====================>........] - ETA: 0s - loss: 0.0247 133/133 [==============================] - 0s 1ms/step - loss: 0.0218 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0056 50/133 [==========>...................] - ETA: 0s - loss: 0.0219 99/133 [=====================>........] - ETA: 0s - loss: 0.0203 133/133 [==============================] - 0s 1ms/step - loss: 0.0199 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0068 50/133 [==========>...................] - ETA: 0s - loss: 0.0174 99/133 [=====================>........] - ETA: 0s - loss: 0.0152 133/133 [==============================] - 0s 1ms/step - loss: 0.0182 -> test with GAN.predict GAN tn, fp: 324, 7 GAN fn, tp: 1, 12 GAN f1 score: 0.750 GAN cohens kappa score: 0.738 -> test with 'LR' LR tn, fp: 176, 155 LR fn, tp: 4, 9 LR f1 score: 0.102 LR cohens kappa score: 0.034 LR average precision score: 0.063 -> test with 'RF' RF tn, fp: 330, 1 RF fn, tp: 5, 8 RF f1 score: 0.727 RF cohens kappa score: 0.719 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 4, 9 GB f1 score: 0.818 GB cohens kappa score: 0.812 -> test with 'KNN' KNN tn, fp: 311, 20 KNN fn, tp: 0, 13 KNN f1 score: 0.565 KNN cohens kappa score: 0.540 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 191, 177 LR fn, tp: 8, 12 LR f1 score: 0.133 LR cohens kappa score: 0.063 LR average precision score: 0.082 average: LR tn, fp: 178.16, 153.64 LR fn, tp: 4.32, 9.48 LR f1 score: 0.107 LR cohens kappa score: 0.036 LR average precision score: 0.064 minimum: LR tn, fp: 155, 141 LR fn, tp: 2, 6 LR f1 score: 0.073 LR cohens kappa score: -0.001 LR average precision score: 0.051 -----[ RF ]----- maximum: RF tn, fp: 332, 3 RF fn, tp: 11, 11 RF f1 score: 0.815 RF cohens kappa score: 0.807 average: RF tn, fp: 331.04, 0.76 RF fn, tp: 6.52, 7.28 RF f1 score: 0.655 RF cohens kappa score: 0.646 minimum: RF tn, fp: 328, 0 RF fn, tp: 3, 2 RF f1 score: 0.267 RF cohens kappa score: 0.259 -----[ GB ]----- maximum: GB tn, fp: 332, 4 GB fn, tp: 8, 12 GB f1 score: 0.857 GB cohens kappa score: 0.851 average: GB tn, fp: 330.0, 1.8 GB fn, tp: 4.68, 9.12 GB f1 score: 0.733 GB cohens kappa score: 0.723 minimum: GB tn, fp: 328, 0 GB fn, tp: 2, 6 GB f1 score: 0.571 GB cohens kappa score: 0.560 -----[ KNN ]----- maximum: KNN tn, fp: 321, 35 KNN fn, tp: 2, 14 KNN f1 score: 0.649 KNN cohens kappa score: 0.630 average: KNN tn, fp: 309.32, 22.48 KNN fn, tp: 0.8, 13.0 KNN f1 score: 0.534 KNN cohens kappa score: 0.505 minimum: KNN tn, fp: 297, 11 KNN fn, tp: 0, 12 KNN f1 score: 0.433 KNN cohens kappa score: 0.396 -----[ GAN ]----- maximum: GAN tn, fp: 331, 9 GAN fn, tp: 6, 13 GAN f1 score: 0.897 GAN cohens kappa score: 0.892 average: GAN tn, fp: 327.24, 4.56 GAN fn, tp: 2.6, 11.2 GAN f1 score: 0.759 GAN cohens kappa score: 0.748 minimum: GAN tn, fp: 323, 1 GAN fn, tp: 1, 8 GAN f1 score: 0.643 GAN cohens kappa score: 0.628