Wed May 15 13:14:46 2019 paraheat_gaussian_parameters: python version: 3.6.8 keras version: 2.2.4 Neural network to solve a multivariate regression problem. Estimate the parameters xc, yc, sc, vc used in a Gaussian diffusivity given vs, 50 samples of the resulting heat distribution Data of many records is available. The data is read from an external file. Read data from xcycscvc2000.txt Data contains 2000 records with 55 features. Training data uses 1900 records with 50 features and 4 targets. Test data uses 100 records with 50 features and 4 targets. train_data[0,0:10]: [ 7.7787565 11.135787 22.006292 18.514299 20.117976 16.892016 9.8785432 5.0562469 18.853287 18.549175 ] train_targets[0,0:4]: [0.53283302 0.5341366 0.53407538 3.7110381 ] test_data[0,0:10]: [ 5.4219116 7.9005456 20.476215 18.187996 20.09777 14.412781 7.3193087 4.3184267 15.293117 19.785568 ] test_targets[0,0:4]: [0.05329344 0.41479466 0.70645339 4.1899415 ] Using 200 nodes per layer _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 200) 10200 _________________________________________________________________ dense_2 (Dense) (None, 200) 40200 _________________________________________________________________ dense_3 (Dense) (None, 200) 40200 _________________________________________________________________ dense_4 (Dense) (None, 200) 40200 _________________________________________________________________ dense_5 (Dense) (None, 200) 40200 _________________________________________________________________ dense_6 (Dense) (None, 200) 40200 _________________________________________________________________ dense_7 (Dense) (None, 200) 40200 _________________________________________________________________ dense_8 (Dense) (None, 200) 40200 _________________________________________________________________ dense_9 (Dense) (None, 200) 40200 _________________________________________________________________ dense_10 (Dense) (None, 200) 40200 _________________________________________________________________ dense_11 (Dense) (None, 200) 40200 _________________________________________________________________ dense_12 (Dense) (None, 4) 804 ================================================================= Total params: 413,004 Trainable params: 413,004 Non-trainable params: 0 _________________________________________________________________ Training: Train on 1520 samples, validate on 380 samples Epoch 1/40 32/1520 [..............................] - ETA: 22s - loss: 5.1041 - mean_squared_error: 5.1041 544/1520 [=========>....................] - ETA: 0s - loss: 1.6799 - mean_squared_error: 1.6799 1088/1520 [====================>.........] - ETA: 0s - loss: 1.4758 - mean_squared_error: 1.4758 1520/1520 [==============================] - 1s 440us/step - loss: 1.3347 - mean_squared_error: 1.3347 - val_loss: 0.9160 - val_mean_squared_error: 0.9160 Epoch 2/40 32/1520 [..............................] - ETA: 0s - loss: 0.6747 - mean_squared_error: 0.6747 576/1520 [==========>...................] - ETA: 0s - loss: 0.8262 - mean_squared_error: 0.8262 1120/1520 [=====================>........] - ETA: 0s - loss: 0.7279 - mean_squared_error: 0.7279 1520/1520 [==============================] - 0s 103us/step - loss: 0.6427 - mean_squared_error: 0.6427 - val_loss: 0.4014 - val_mean_squared_error: 0.4014 Epoch 3/40 32/1520 [..............................] - ETA: 0s - loss: 0.4303 - mean_squared_error: 0.4303 576/1520 [==========>...................] - ETA: 0s - loss: 0.2882 - mean_squared_error: 0.2882 1120/1520 [=====================>........] - ETA: 0s - loss: 0.2354 - mean_squared_error: 0.2354 1520/1520 [==============================] - 0s 114us/step - loss: 0.2127 - mean_squared_error: 0.2127 - val_loss: 0.1624 - val_mean_squared_error: 0.1624 Epoch 4/40 32/1520 [..............................] - ETA: 0s - loss: 0.1645 - mean_squared_error: 0.1645 544/1520 [=========>....................] - ETA: 0s - loss: 0.1378 - mean_squared_error: 0.1378 864/1520 [================>.............] - ETA: 0s - loss: 0.1322 - mean_squared_error: 0.1322 1248/1520 [=======================>......] - ETA: 0s - loss: 0.1298 - mean_squared_error: 0.1298 1520/1520 [==============================] - 0s 132us/step - loss: 0.1260 - mean_squared_error: 0.1260 - val_loss: 0.1369 - val_mean_squared_error: 0.1369 Epoch 5/40 32/1520 [..............................] - ETA: 0s - loss: 0.1101 - mean_squared_error: 0.1101 512/1520 [=========>....................] - ETA: 0s - loss: 0.1391 - mean_squared_error: 0.1391 1024/1520 [===================>..........] - ETA: 0s - loss: 0.1441 - mean_squared_error: 0.1441 1504/1520 [============================>.] - ETA: 0s - loss: 0.1445 - mean_squared_error: 0.1445 1520/1520 [==============================] - 0s 117us/step - loss: 0.1440 - mean_squared_error: 0.1440 - val_loss: 0.1655 - val_mean_squared_error: 0.1655 Epoch 6/40 32/1520 [..............................] - ETA: 0s - loss: 0.1465 - mean_squared_error: 0.1465 512/1520 [=========>....................] - ETA: 0s - loss: 0.1165 - mean_squared_error: 0.1165 992/1520 [==================>...........] - ETA: 0s - loss: 0.1194 - mean_squared_error: 0.1194 1472/1520 [============================>.] - ETA: 0s - loss: 0.1170 - mean_squared_error: 0.1170 1520/1520 [==============================] - 0s 118us/step - loss: 0.1185 - mean_squared_error: 0.1185 - val_loss: 0.1270 - val_mean_squared_error: 0.1270 Epoch 7/40 32/1520 [..............................] - ETA: 0s - loss: 0.0986 - mean_squared_error: 0.0986 512/1520 [=========>....................] - ETA: 0s - loss: 0.1158 - mean_squared_error: 0.1158 992/1520 [==================>...........] - ETA: 0s - loss: 0.1115 - mean_squared_error: 0.1115 1472/1520 [============================>.] - ETA: 0s - loss: 0.1114 - mean_squared_error: 0.1114 1520/1520 [==============================] - 0s 127us/step - loss: 0.1108 - mean_squared_error: 0.1108 - val_loss: 0.1310 - val_mean_squared_error: 0.1310 Epoch 8/40 32/1520 [..............................] - ETA: 0s - loss: 0.0804 - mean_squared_error: 0.0804 384/1520 [======>.......................] - ETA: 0s - loss: 0.0869 - mean_squared_error: 0.0869 864/1520 [================>.............] - ETA: 0s - loss: 0.0994 - mean_squared_error: 0.0994 1280/1520 [========================>.....] - ETA: 0s - loss: 0.1030 - mean_squared_error: 0.1030 1520/1520 [==============================] - 0s 136us/step - loss: 0.1115 - mean_squared_error: 0.1115 - val_loss: 0.1265 - val_mean_squared_error: 0.1265 Epoch 9/40 32/1520 [..............................] - ETA: 0s - loss: 0.1133 - mean_squared_error: 0.1133 480/1520 [========>.....................] - ETA: 0s - loss: 0.1035 - mean_squared_error: 0.1035 960/1520 [=================>............] - ETA: 0s - loss: 0.0957 - mean_squared_error: 0.0957 1440/1520 [===========================>..] - ETA: 0s - loss: 0.0954 - mean_squared_error: 0.0954 1520/1520 [==============================] - 0s 125us/step - loss: 0.0960 - mean_squared_error: 0.0960 - val_loss: 0.1488 - val_mean_squared_error: 0.1488 Epoch 10/40 32/1520 [..............................] - ETA: 0s - loss: 0.0870 - mean_squared_error: 0.0870 480/1520 [========>.....................] - ETA: 0s - loss: 0.1257 - mean_squared_error: 0.1257 896/1520 [================>.............] - ETA: 0s - loss: 0.1112 - mean_squared_error: 0.1112 1120/1520 [=====================>........] - ETA: 0s - loss: 0.1115 - mean_squared_error: 0.1115 1344/1520 [=========================>....] - ETA: 0s - loss: 0.1122 - mean_squared_error: 0.1122 1520/1520 [==============================] - 0s 161us/step - loss: 0.1115 - mean_squared_error: 0.1115 - val_loss: 0.1645 - val_mean_squared_error: 0.1645 Epoch 11/40 32/1520 [..............................] - ETA: 0s - loss: 0.1547 - mean_squared_error: 0.1547 480/1520 [========>.....................] - ETA: 0s - loss: 0.0926 - mean_squared_error: 0.0926 960/1520 [=================>............] - ETA: 0s - loss: 0.1008 - mean_squared_error: 0.1008 1440/1520 [===========================>..] - ETA: 0s - loss: 0.1003 - mean_squared_error: 0.1003 1520/1520 [==============================] - 0s 123us/step - loss: 0.1025 - mean_squared_error: 0.1025 - val_loss: 0.1286 - val_mean_squared_error: 0.1286 Epoch 12/40 32/1520 [..............................] - ETA: 0s - loss: 0.1565 - mean_squared_error: 0.1565 352/1520 [=====>........................] - ETA: 0s - loss: 0.1059 - mean_squared_error: 0.1059 768/1520 [==============>...............] - ETA: 0s - loss: 0.1011 - mean_squared_error: 0.1011 1152/1520 [=====================>........] - ETA: 0s - loss: 0.0946 - mean_squared_error: 0.0946 1520/1520 [==============================] - 0s 141us/step - loss: 0.0873 - mean_squared_error: 0.0873 - val_loss: 0.0953 - val_mean_squared_error: 0.0953 Epoch 13/40 32/1520 [..............................] - ETA: 0s - loss: 0.0604 - mean_squared_error: 0.0604 480/1520 [========>.....................] - ETA: 0s - loss: 0.0847 - mean_squared_error: 0.0847 832/1520 [===============>..............] - ETA: 0s - loss: 0.0906 - mean_squared_error: 0.0906 1216/1520 [=======================>......] - ETA: 0s - loss: 0.0995 - mean_squared_error: 0.0995 1520/1520 [==============================] - 0s 137us/step - loss: 0.1098 - mean_squared_error: 0.1098 - val_loss: 0.1713 - val_mean_squared_error: 0.1713 Epoch 14/40 32/1520 [..............................] - ETA: 0s - loss: 0.2388 - mean_squared_error: 0.2388 320/1520 [=====>........................] - ETA: 0s - loss: 0.1606 - mean_squared_error: 0.1606 768/1520 [==============>...............] - ETA: 0s - loss: 0.1466 - mean_squared_error: 0.1466 1184/1520 [======================>.......] - ETA: 0s - loss: 0.1336 - mean_squared_error: 0.1336 1472/1520 [============================>.] - ETA: 0s - loss: 0.1275 - mean_squared_error: 0.1275 1520/1520 [==============================] - 0s 168us/step - loss: 0.1272 - mean_squared_error: 0.1272 - val_loss: 0.1160 - val_mean_squared_error: 0.1160 Epoch 15/40 32/1520 [..............................] - ETA: 0s - loss: 0.0916 - mean_squared_error: 0.0916 512/1520 [=========>....................] - ETA: 0s - loss: 0.1172 - mean_squared_error: 0.1172 960/1520 [=================>............] - ETA: 0s - loss: 0.1075 - mean_squared_error: 0.1075 1440/1520 [===========================>..] - ETA: 0s - loss: 0.0989 - mean_squared_error: 0.0989 1520/1520 [==============================] - 0s 123us/step - loss: 0.0970 - mean_squared_error: 0.0970 - val_loss: 0.0995 - val_mean_squared_error: 0.0995 Epoch 16/40 32/1520 [..............................] - ETA: 0s - loss: 0.0432 - mean_squared_error: 0.0432 512/1520 [=========>....................] - ETA: 0s - loss: 0.0839 - mean_squared_error: 0.0839 992/1520 [==================>...........] - ETA: 0s - loss: 0.0938 - mean_squared_error: 0.0938 1408/1520 [==========================>...] - ETA: 0s - loss: 0.1009 - mean_squared_error: 0.1009 1520/1520 [==============================] - 0s 139us/step - loss: 0.1034 - mean_squared_error: 0.1034 - val_loss: 0.1122 - val_mean_squared_error: 0.1122 Epoch 17/40 32/1520 [..............................] - ETA: 0s - loss: 0.1083 - mean_squared_error: 0.1083 480/1520 [========>.....................] - ETA: 0s - loss: 0.1029 - mean_squared_error: 0.1029 896/1520 [================>.............] - ETA: 0s - loss: 0.0947 - mean_squared_error: 0.0947 1312/1520 [========================>.....] - ETA: 0s - loss: 0.0944 - mean_squared_error: 0.0944 1472/1520 [============================>.] - ETA: 0s - loss: 0.0924 - mean_squared_error: 0.0924 1520/1520 [==============================] - 0s 165us/step - loss: 0.0914 - mean_squared_error: 0.0914 - val_loss: 0.1177 - val_mean_squared_error: 0.1177 Epoch 18/40 32/1520 [..............................] - ETA: 0s - loss: 0.0477 - mean_squared_error: 0.0477 352/1520 [=====>........................] - ETA: 0s - loss: 0.0808 - mean_squared_error: 0.0808 736/1520 [=============>................] - ETA: 0s - loss: 0.0827 - mean_squared_error: 0.0827 1184/1520 [======================>.......] - ETA: 0s - loss: 0.0793 - mean_squared_error: 0.0793 1376/1520 [==========================>...] - ETA: 0s - loss: 0.0774 - mean_squared_error: 0.0774 1520/1520 [==============================] - 0s 163us/step - loss: 0.0778 - mean_squared_error: 0.0778 - val_loss: 0.1075 - val_mean_squared_error: 0.1075 Epoch 19/40 32/1520 [..............................] - ETA: 0s - loss: 0.0801 - mean_squared_error: 0.0801 544/1520 [=========>....................] - ETA: 0s - loss: 0.0937 - mean_squared_error: 0.0937 1056/1520 [===================>..........] - ETA: 0s - loss: 0.0828 - mean_squared_error: 0.0828 1520/1520 [==============================] - 0s 111us/step - loss: 0.0814 - mean_squared_error: 0.0814 - val_loss: 0.1138 - val_mean_squared_error: 0.1138 Epoch 20/40 32/1520 [..............................] - ETA: 0s - loss: 0.0964 - mean_squared_error: 0.0964 544/1520 [=========>....................] - ETA: 0s - loss: 0.0838 - mean_squared_error: 0.0838 1088/1520 [====================>.........] - ETA: 0s - loss: 0.0829 - mean_squared_error: 0.0829 1520/1520 [==============================] - 0s 107us/step - loss: 0.0802 - mean_squared_error: 0.0802 - val_loss: 0.1362 - val_mean_squared_error: 0.1362 Epoch 21/40 32/1520 [..............................] - ETA: 0s - loss: 0.1122 - mean_squared_error: 0.1122 544/1520 [=========>....................] - ETA: 0s - loss: 0.0948 - mean_squared_error: 0.0948 1088/1520 [====================>.........] - ETA: 0s - loss: 0.0807 - mean_squared_error: 0.0807 1520/1520 [==============================] - 0s 106us/step - loss: 0.0800 - mean_squared_error: 0.0800 - val_loss: 0.1058 - val_mean_squared_error: 0.1058 Epoch 22/40 32/1520 [..............................] - ETA: 0s - loss: 0.0583 - mean_squared_error: 0.0583 512/1520 [=========>....................] - ETA: 0s - loss: 0.1063 - mean_squared_error: 0.1063 1024/1520 [===================>..........] - ETA: 0s - loss: 0.1091 - mean_squared_error: 0.1091 1520/1520 [==============================] - 0s 110us/step - loss: 0.1040 - mean_squared_error: 0.1040 - val_loss: 0.1084 - val_mean_squared_error: 0.1084 Epoch 23/40 32/1520 [..............................] - ETA: 0s - loss: 0.0541 - mean_squared_error: 0.0541 576/1520 [==========>...................] - ETA: 0s - loss: 0.0755 - mean_squared_error: 0.0755 1120/1520 [=====================>........] - ETA: 0s - loss: 0.0710 - mean_squared_error: 0.0710 1520/1520 [==============================] - 0s 104us/step - loss: 0.0802 - mean_squared_error: 0.0802 - val_loss: 0.1206 - val_mean_squared_error: 0.1206 Epoch 24/40 32/1520 [..............................] - ETA: 0s - loss: 0.0504 - mean_squared_error: 0.0504 192/1520 [==>...........................] - ETA: 0s - loss: 0.0567 - mean_squared_error: 0.0567 320/1520 [=====>........................] - ETA: 0s - loss: 0.0711 - mean_squared_error: 0.0711 416/1520 [=======>......................] - ETA: 0s - loss: 0.0722 - mean_squared_error: 0.0722 896/1520 [================>.............] - ETA: 0s - loss: 0.0746 - mean_squared_error: 0.0746 1344/1520 [=========================>....] - ETA: 0s - loss: 0.0781 - mean_squared_error: 0.0781 1520/1520 [==============================] - 0s 246us/step - loss: 0.0775 - mean_squared_error: 0.0775 - val_loss: 0.1110 - val_mean_squared_error: 0.1110 Epoch 25/40 32/1520 [..............................] - ETA: 0s - loss: 0.0858 - mean_squared_error: 0.0858 416/1520 [=======>......................] - ETA: 0s - loss: 0.0764 - mean_squared_error: 0.0764 768/1520 [==============>...............] - ETA: 0s - loss: 0.0781 - mean_squared_error: 0.0781 1184/1520 [======================>.......] - ETA: 0s - loss: 0.0799 - mean_squared_error: 0.0799 1440/1520 [===========================>..] - ETA: 0s - loss: 0.0801 - mean_squared_error: 0.0801 1520/1520 [==============================] - 0s 186us/step - loss: 0.0827 - mean_squared_error: 0.0827 - val_loss: 0.1463 - val_mean_squared_error: 0.1463 Epoch 26/40 32/1520 [..............................] - ETA: 0s - loss: 0.1251 - mean_squared_error: 0.1251 448/1520 [=======>......................] - ETA: 0s - loss: 0.1128 - mean_squared_error: 0.1128 896/1520 [================>.............] - ETA: 0s - loss: 0.1003 - mean_squared_error: 0.1003 1312/1520 [========================>.....] - ETA: 0s - loss: 0.1016 - mean_squared_error: 0.1016 1520/1520 [==============================] - 0s 137us/step - loss: 0.0993 - mean_squared_error: 0.0993 - val_loss: 0.0968 - val_mean_squared_error: 0.0968 Epoch 27/40 32/1520 [..............................] - ETA: 0s - loss: 0.0597 - mean_squared_error: 0.0597 480/1520 [========>.....................] - ETA: 0s - loss: 0.0690 - mean_squared_error: 0.0690 896/1520 [================>.............] - ETA: 0s - loss: 0.0674 - mean_squared_error: 0.0674 1344/1520 [=========================>....] - ETA: 0s - loss: 0.0717 - mean_squared_error: 0.0717 1520/1520 [==============================] - 0s 139us/step - loss: 0.0691 - mean_squared_error: 0.0691 - val_loss: 0.1190 - val_mean_squared_error: 0.1190 Epoch 28/40 32/1520 [..............................] - ETA: 0s - loss: 0.1990 - mean_squared_error: 0.1990 416/1520 [=======>......................] - ETA: 0s - loss: 0.0720 - mean_squared_error: 0.0720 768/1520 [==============>...............] - ETA: 0s - loss: 0.0754 - mean_squared_error: 0.0754 1120/1520 [=====================>........] - ETA: 0s - loss: 0.0706 - mean_squared_error: 0.0706 1520/1520 [==============================] - 0s 148us/step - loss: 0.0695 - mean_squared_error: 0.0695 - val_loss: 0.1077 - val_mean_squared_error: 0.1077 Epoch 29/40 32/1520 [..............................] - ETA: 0s - loss: 0.0273 - mean_squared_error: 0.0273 512/1520 [=========>....................] - ETA: 0s - loss: 0.0763 - mean_squared_error: 0.0763 960/1520 [=================>............] - ETA: 0s - loss: 0.0776 - mean_squared_error: 0.0776 1408/1520 [==========================>...] - ETA: 0s - loss: 0.0751 - mean_squared_error: 0.0751 1520/1520 [==============================] - 0s 151us/step - loss: 0.0743 - mean_squared_error: 0.0743 - val_loss: 0.1031 - val_mean_squared_error: 0.1031 Epoch 30/40 32/1520 [..............................] - ETA: 1s - loss: 0.0977 - mean_squared_error: 0.0977 224/1520 [===>..........................] - ETA: 0s - loss: 0.0950 - mean_squared_error: 0.0950 640/1520 [===========>..................] - ETA: 0s - loss: 0.0730 - mean_squared_error: 0.0730 1088/1520 [====================>.........] - ETA: 0s - loss: 0.0796 - mean_squared_error: 0.0796 1520/1520 [==============================] - 0s 174us/step - loss: 0.0858 - mean_squared_error: 0.0858 - val_loss: 0.0988 - val_mean_squared_error: 0.0988 Epoch 31/40 32/1520 [..............................] - ETA: 0s - loss: 0.0316 - mean_squared_error: 0.0316 480/1520 [========>.....................] - ETA: 0s - loss: 0.0814 - mean_squared_error: 0.0814 864/1520 [================>.............] - ETA: 0s - loss: 0.0896 - mean_squared_error: 0.0896 1280/1520 [========================>.....] - ETA: 0s - loss: 0.0850 - mean_squared_error: 0.0850 1520/1520 [==============================] - 0s 141us/step - loss: 0.0852 - mean_squared_error: 0.0852 - val_loss: 0.0982 - val_mean_squared_error: 0.0982 Epoch 32/40 32/1520 [..............................] - ETA: 0s - loss: 0.0568 - mean_squared_error: 0.0568 416/1520 [=======>......................] - ETA: 0s - loss: 0.0506 - mean_squared_error: 0.0506 768/1520 [==============>...............] - ETA: 0s - loss: 0.0631 - mean_squared_error: 0.0631 1120/1520 [=====================>........] - ETA: 0s - loss: 0.0625 - mean_squared_error: 0.0625 1472/1520 [============================>.] - ETA: 0s - loss: 0.0615 - mean_squared_error: 0.0615 1520/1520 [==============================] - 0s 184us/step - loss: 0.0632 - mean_squared_error: 0.0632 - val_loss: 0.1006 - val_mean_squared_error: 0.1006 Epoch 33/40 32/1520 [..............................] - ETA: 0s - loss: 0.0497 - mean_squared_error: 0.0497 480/1520 [========>.....................] - ETA: 0s - loss: 0.0976 - mean_squared_error: 0.0976 928/1520 [=================>............] - ETA: 0s - loss: 0.0834 - mean_squared_error: 0.0834 1344/1520 [=========================>....] - ETA: 0s - loss: 0.0782 - mean_squared_error: 0.0782 1520/1520 [==============================] - 0s 124us/step - loss: 0.0745 - mean_squared_error: 0.0745 - val_loss: 0.1001 - val_mean_squared_error: 0.1001 Epoch 34/40 32/1520 [..............................] - ETA: 0s - loss: 0.0581 - mean_squared_error: 0.0581 480/1520 [========>.....................] - ETA: 0s - loss: 0.0607 - mean_squared_error: 0.0607 928/1520 [=================>............] - ETA: 0s - loss: 0.0664 - mean_squared_error: 0.0664 1408/1520 [==========================>...] - ETA: 0s - loss: 0.0793 - mean_squared_error: 0.0793 1520/1520 [==============================] - 0s 127us/step - loss: 0.0806 - mean_squared_error: 0.0806 - val_loss: 0.1877 - val_mean_squared_error: 0.1877 Epoch 35/40 32/1520 [..............................] - ETA: 0s - loss: 0.2112 - mean_squared_error: 0.2112 352/1520 [=====>........................] - ETA: 0s - loss: 0.0947 - mean_squared_error: 0.0947 768/1520 [==============>...............] - ETA: 0s - loss: 0.0954 - mean_squared_error: 0.0954 1184/1520 [======================>.......] - ETA: 0s - loss: 0.0933 - mean_squared_error: 0.0933 1520/1520 [==============================] - 0s 144us/step - loss: 0.0890 - mean_squared_error: 0.0890 - val_loss: 0.1059 - val_mean_squared_error: 0.1059 Epoch 36/40 32/1520 [..............................] - ETA: 0s - loss: 0.0823 - mean_squared_error: 0.0823 480/1520 [========>.....................] - ETA: 0s - loss: 0.0698 - mean_squared_error: 0.0698 928/1520 [=================>............] - ETA: 0s - loss: 0.0638 - mean_squared_error: 0.0638 1344/1520 [=========================>....] - ETA: 0s - loss: 0.0649 - mean_squared_error: 0.0649 1520/1520 [==============================] - 0s 133us/step - loss: 0.0667 - mean_squared_error: 0.0667 - val_loss: 0.0983 - val_mean_squared_error: 0.0983 Epoch 37/40 32/1520 [..............................] - ETA: 0s - loss: 0.0650 - mean_squared_error: 0.0650 384/1520 [======>.......................] - ETA: 0s - loss: 0.0719 - mean_squared_error: 0.0719 832/1520 [===============>..............] - ETA: 0s - loss: 0.0719 - mean_squared_error: 0.0719 1248/1520 [=======================>......] - ETA: 0s - loss: 0.0752 - mean_squared_error: 0.0752 1520/1520 [==============================] - 0s 143us/step - loss: 0.0711 - mean_squared_error: 0.0711 - val_loss: 0.0960 - val_mean_squared_error: 0.0960 Epoch 38/40 32/1520 [..............................] - ETA: 0s - loss: 0.0302 - mean_squared_error: 0.0302 448/1520 [=======>......................] - ETA: 0s - loss: 0.0590 - mean_squared_error: 0.0590 864/1520 [================>.............] - ETA: 0s - loss: 0.0568 - mean_squared_error: 0.0568 1312/1520 [========================>.....] - ETA: 0s - loss: 0.0596 - mean_squared_error: 0.0596 1520/1520 [==============================] - 0s 129us/step - loss: 0.0619 - mean_squared_error: 0.0619 - val_loss: 0.1141 - val_mean_squared_error: 0.1141 Epoch 39/40 32/1520 [..............................] - ETA: 0s - loss: 0.0383 - mean_squared_error: 0.0383 384/1520 [======>.......................] - ETA: 0s - loss: 0.0631 - mean_squared_error: 0.0631 768/1520 [==============>...............] - ETA: 0s - loss: 0.0678 - mean_squared_error: 0.0678 1216/1520 [=======================>......] - ETA: 0s - loss: 0.0728 - mean_squared_error: 0.0728 1520/1520 [==============================] - 0s 138us/step - loss: 0.0790 - mean_squared_error: 0.0790 - val_loss: 0.1356 - val_mean_squared_error: 0.1356 Epoch 40/40 32/1520 [..............................] - ETA: 0s - loss: 0.1052 - mean_squared_error: 0.1052 512/1520 [=========>....................] - ETA: 0s - loss: 0.0925 - mean_squared_error: 0.0925 960/1520 [=================>............] - ETA: 0s - loss: 0.0886 - mean_squared_error: 0.0886 1408/1520 [==========================>...] - ETA: 0s - loss: 0.0798 - mean_squared_error: 0.0798 1520/1520 [==============================] - 0s 132us/step - loss: 0.0780 - mean_squared_error: 0.0780 - val_loss: 0.1057 - val_mean_squared_error: 0.1057 Testing: Case True Estimate 0: 0.0533 0.0653 0.4148 0.3941 0.7065 0.7176 4.1899 4.1265 1: 0.2524 0.2705 0.8448 0.8525 0.8071 0.8133 3.9149 3.9823 2: 0.0107 0.0886 0.8647 0.8357 0.5868 0.5467 4.1577 4.1061 3: 0.8701 0.8204 0.8770 0.8804 0.8049 0.7599 1.2361 1.3346 4: 0.2565 0.1268 0.8393 0.9450 0.2386 0.2343 2.4532 3.5182 5: 0.4201 0.5136 0.5087 0.6415 0.7088 0.8034 0.6869 0.6949 6: 0.2081 0.2102 0.8591 0.9020 0.3090 0.2833 4.6909 4.3528 7: 0.4647 0.4342 0.9222 1.0168 0.2337 0.2247 4.6600 4.4691 8: 0.4288 0.3320 0.3561 0.1605 0.3357 0.5287 1.9844 1.7088 9: 0.3106 0.3406 0.0175 0.0824 0.5570 0.4864 2.8915 2.6277 10: 0.1094 0.1264 0.8587 0.8426 0.6004 0.5895 3.2209 3.2777 11: 0.5180 0.6727 0.1388 0.1648 0.8209 0.8296 1.4869 1.4697 12: 0.3125 0.2648 0.2865 0.2294 0.6393 0.7742 4.6788 4.5336 13: 0.6529 0.7383 0.0296 0.0662 0.5605 0.5903 2.2257 1.9316 14: 0.9364 0.9484 0.8226 0.9121 0.4616 0.4572 2.6613 2.6513 15: 0.5630 0.7275 0.2812 0.2803 0.6169 0.5996 0.6284 0.7119 16: 0.3616 0.2888 0.8103 0.9244 0.4467 0.5350 3.4632 3.6915 17: 0.7339 0.7426 0.8993 0.9546 0.6990 0.7140 1.7295 1.8398 18: 0.6534 0.7288 0.4544 0.4658 0.9891 0.9255 3.4852 3.7429 19: 0.9088 1.0650 0.5415 0.5020 0.5444 0.6775 3.9484 3.8999 20: 0.1705 0.1444 0.9272 0.9359 0.2299 0.1967 0.5865 1.5115 21: 0.3395 0.3523 0.2021 0.1198 0.6757 0.7787 3.6248 3.5705 22: 0.7887 0.8977 0.7724 0.9703 0.1817 0.1772 0.9908 1.7169 23: 0.7317 0.7537 0.3035 0.2902 0.1156 0.1912 3.0580 1.7175 24: 0.2770 0.2218 0.2465 0.1953 0.2878 0.2863 3.9347 3.9596 25: 0.1238 0.1743 0.2075 0.1904 0.5581 0.4370 0.5233 0.7744 26: 0.7485 0.8436 0.3051 0.2988 0.1202 0.1949 4.9072 3.3132 27: 0.7581 0.7674 0.0065 0.0490 0.4610 0.3923 2.2979 1.9639 28: 0.8421 0.8459 0.6602 0.8169 0.0556 0.0647 3.6176 1.9347 29: 0.6801 0.7064 0.5420 0.5663 0.9614 0.9277 4.7790 4.7897 30: 0.8121 0.8676 0.0026 0.1743 0.7774 0.6999 2.0628 1.8264 31: 0.0380 0.1006 0.4476 0.5664 0.2429 0.2221 2.3668 3.0684 32: 0.5319 0.6760 0.1126 0.1662 0.8375 0.8276 1.7688 1.7140 33: 0.7075 0.7974 0.0003 0.0369 0.4658 0.4320 4.0787 3.3093 34: 0.7354 0.9119 0.2681 0.2083 0.6206 0.7449 2.5739 2.5238 35: 0.7628 0.8968 0.8371 0.9470 0.2978 0.3019 3.1344 3.7177 36: 0.9028 1.0562 0.4520 0.3602 0.4978 0.5704 2.0777 1.9965 37: 0.3399 0.3615 0.3203 0.3232 0.8856 0.8561 4.8253 4.7635 38: 0.6548 0.7866 0.4837 0.5651 0.4054 0.3932 0.5180 1.0770 39: 0.2854 0.3102 0.0057 0.0676 0.1188 0.1624 2.1917 1.6915 40: 0.3691 0.3715 0.5878 0.6035 0.9780 0.8886 4.6161 4.7122 41: 0.3546 0.4220 0.0032 0.0520 0.2646 0.1787 0.7880 1.5034 42: 0.4440 0.4383 0.9089 1.0908 0.0931 0.1524 1.7749 1.6071 43: 0.6250 0.6653 0.9024 0.9659 0.3330 0.2773 2.3724 2.8220 44: 0.3722 0.4865 0.3524 0.4006 0.8641 0.8905 1.1542 1.2139 45: 0.4103 0.3619 0.3601 0.3046 0.2971 0.4008 4.3382 3.7572 46: 0.5909 0.7061 0.6346 0.8628 0.5343 0.7951 2.7246 2.6683 47: 0.9204 1.0700 0.4849 0.4188 0.5915 0.7277 3.9574 3.8706 48: 0.6227 0.7528 0.2042 0.2176 0.8024 0.8827 4.2847 4.2205 49: 0.2587 0.1670 0.7354 0.8353 0.4308 0.5417 2.5532 2.6410 50: 0.6691 0.8087 0.6353 0.8308 0.4778 0.6532 0.8438 0.9494 51: 0.2882 0.3311 0.9447 0.8395 0.9761 0.8482 4.7717 4.6733 52: 0.7061 0.7551 0.7395 0.9317 0.2938 0.3400 1.3358 1.4769 53: 0.4320 0.5406 0.4675 0.4857 0.9109 0.9016 2.5809 2.6061 54: 0.4020 0.2646 0.4713 0.4614 0.4717 0.8376 2.6574 2.3163 55: 0.5900 0.6908 0.2135 0.0456 0.3570 0.4051 2.1319 1.9723 56: 0.9848 0.9588 0.6102 0.6102 0.9061 0.8525 3.1403 3.1641 57: 0.0936 0.0968 0.8355 0.8656 0.4327 0.4276 3.6403 3.8821 58: 0.5904 0.6644 0.1213 0.1163 0.2625 0.2041 0.8720 1.5174 59: 0.4737 0.5159 0.9788 0.9926 0.7095 0.6826 1.2136 1.3677 60: 0.1257 0.1168 0.3469 0.3295 0.7414 0.7515 4.1900 4.1944 61: 0.9835 0.9375 0.1586 0.3188 0.3143 0.1224 0.7065 2.0372 62: 0.4537 0.5028 0.8514 0.9279 0.7394 0.7850 2.4153 2.4502 63: 0.6947 0.8711 0.3217 0.2878 0.7330 0.8770 4.2552 4.2358 64: 0.2335 0.2524 0.9420 0.8774 0.7337 0.7133 1.7124 1.7704 65: 0.8378 0.8442 0.0217 0.2497 0.1609 0.0720 0.7521 2.2691 66: 0.9188 0.9459 0.9132 1.1044 0.1539 0.1859 2.9260 2.7526 67: 0.3143 0.1538 0.5737 0.6966 0.4188 0.6618 1.0060 1.0903 68: 0.3345 0.4091 0.2548 0.2364 0.8979 0.8669 2.2827 2.2597 69: 0.6127 0.6707 0.9824 1.0526 0.2780 0.2351 3.4744 3.9310 70: 0.9612 1.1047 0.5480 0.4902 0.5631 0.6655 3.1952 3.0783 71: 0.0258 0.0763 0.2652 0.3231 0.6344 0.6285 4.3315 4.1631 72: 0.9546 0.8708 0.8774 0.7757 0.1186 0.0290 1.1039 2.1612 73: 0.6963 0.8415 0.5258 0.6202 0.6016 0.7389 0.7927 0.8201 74: 0.3073 0.1921 0.3303 0.2353 0.5871 0.7669 3.0712 3.0243 75: 0.1994 0.2771 0.4540 0.4321 0.9377 0.8580 2.4727 2.5196 76: 0.1998 0.1202 0.2792 0.2740 0.1592 0.2712 2.7715 2.3012 77: 0.2810 0.2895 0.4269 0.4140 0.9022 0.8669 3.2933 3.5128 78: 0.4083 0.4645 0.0197 0.0906 0.3970 0.2675 0.6634 1.1792 79: 0.0717 0.2134 0.1969 0.2857 0.9169 0.7884 4.9400 4.6611 80: 0.9691 0.9921 0.4231 0.4132 0.8040 0.8321 4.8340 4.6064 81: 0.8564 0.8661 0.7131 0.7776 0.8571 0.8538 4.5947 4.5699 82: 0.2946 0.3054 0.8774 0.8651 0.7630 0.7628 1.8841 1.9408 83: 0.1502 0.1160 0.3170 0.2718 0.6733 0.7513 2.5900 2.5059 84: 0.1820 0.1468 0.5310 0.4890 0.7158 0.7952 2.0510 2.0374 85: 0.5881 0.7081 0.4428 0.4920 0.8698 0.9052 1.7303 1.7339 86: 0.5541 0.6360 0.7448 0.7559 0.9015 0.8777 1.7317 1.7683 87: 0.2368 0.1130 0.2523 0.2068 0.4487 0.4905 2.5717 2.5869 88: 0.4347 0.4132 0.9371 1.1999 0.0872 0.2093 4.0897 2.3810 89: 0.3972 0.4657 0.9291 0.8982 0.7655 0.7148 0.7357 0.8671 90: 0.8445 0.8952 0.4464 0.4395 0.2954 0.2842 2.6918 2.7484 91: 0.8179 0.8166 0.8538 0.9894 0.5248 0.5928 3.6616 3.7700 92: 0.1815 0.2782 0.2137 0.1561 0.0787 0.1504 1.8056 1.4813 93: 0.9630 1.0803 0.6113 0.6269 0.2411 0.2353 4.6328 3.7698 94: 0.2454 0.1111 0.4190 0.3652 0.6189 0.7615 4.0559 4.0735 95: 0.3092 0.3330 0.9984 0.9841 0.6071 0.6046 4.1939 4.1423 96: 0.6085 0.6921 0.6672 0.8122 0.2185 0.3032 1.0588 1.2465 97: 0.2330 0.3510 0.0190 0.1246 0.9669 0.8133 2.3691 2.2510 98: 0.6278 0.8647 0.0504 0.1203 0.1438 0.1742 3.9237 4.1267 99: 0.6469 0.7458 0.2624 0.2954 0.9307 0.8790 2.5781 2.6336 Graphics saved as "paraheat_gaussian_parameters_xc.png" Graphics saved as "paraheat_gaussian_parameters_yc.png" Graphics saved as "paraheat_gaussian_parameters_sc.png" Graphics saved as "paraheat_gaussian_parameters_vc.png" paraheat_gaussian_parameters: Normal end of execution. Wed May 15 13:15:23 2019