Sun Dec 15 10:46:28 2024 paraheat_gaussian_parameters(): python version: 3.10.12 numpy version: 1.26.4 tensorflow version: 2.16.1 keras version: 3.1.1 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 Model: "sequential" ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ dense (Dense) │ (None, 200) │ 10,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_1 (Dense) │ (None, 200) │ 40,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_2 (Dense) │ (None, 200) │ 40,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_3 (Dense) │ (None, 200) │ 40,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_4 (Dense) │ (None, 200) │ 40,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_5 (Dense) │ (None, 200) │ 40,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_6 (Dense) │ (None, 200) │ 40,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_7 (Dense) │ (None, 200) │ 40,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_8 (Dense) │ (None, 200) │ 40,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_9 (Dense) │ (None, 200) │ 40,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_10 (Dense) │ (None, 200) │ 40,200 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_11 (Dense) │ (None, 4) │ 804 │ └─────────────────────────────────┴────────────────────────┴───────────────┘ Total params: 413,004 (1.58 MB) Trainable params: 413,004 (1.58 MB) Non-trainable params: 0 (0.00 B) Training: Epoch 1/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 1:38 2s/step - loss: 2.2126 - mean_squared_error: 2.2126 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 1.7080 - mean_squared_error: 1.7080  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 1.5900 - mean_squared_error: 1.5900 48/48 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - loss: 1.5357 - mean_squared_error: 1.5358 - val_loss: 0.9139 - val_mean_squared_error: 0.9131 Epoch 2/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 1.2667 - mean_squared_error: 1.2667 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.9327 - mean_squared_error: 0.9327  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.8410 - mean_squared_error: 0.8410 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.7944 - mean_squared_error: 0.7946 - val_loss: 0.2242 - val_mean_squared_error: 0.2242 Epoch 3/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - loss: 0.2427 - mean_squared_error: 0.2427 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.2621 - mean_squared_error: 0.2621  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.2512 - mean_squared_error: 0.2512 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.2428 - mean_squared_error: 0.2429 - val_loss: 0.1656 - val_mean_squared_error: 0.1651 Epoch 4/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.1696 - mean_squared_error: 0.1696 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1331 - mean_squared_error: 0.1331  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1379 - mean_squared_error: 0.1379 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1379 - mean_squared_error: 0.1379 - val_loss: 0.1499 - val_mean_squared_error: 0.1493 Epoch 5/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.1999 - mean_squared_error: 0.1999 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1440 - mean_squared_error: 0.1440  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1410 - mean_squared_error: 0.1410 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1399 - mean_squared_error: 0.1399 - val_loss: 0.1542 - val_mean_squared_error: 0.1537 Epoch 6/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.1527 - mean_squared_error: 0.1527 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1367 - mean_squared_error: 0.1367  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1335 - mean_squared_error: 0.1335 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1329 - mean_squared_error: 0.1329 - val_loss: 0.1575 - val_mean_squared_error: 0.1575 Epoch 7/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0821 - mean_squared_error: 0.0821 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0958 - mean_squared_error: 0.0958  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0956 - mean_squared_error: 0.0956 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0971 - mean_squared_error: 0.0970 - val_loss: 0.1262 - val_mean_squared_error: 0.1260 Epoch 8/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.1076 - mean_squared_error: 0.1076 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1074 - mean_squared_error: 0.1074  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1017 - mean_squared_error: 0.1017 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1007 - mean_squared_error: 0.1007 - val_loss: 0.1275 - val_mean_squared_error: 0.1272 Epoch 9/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - loss: 0.0734 - mean_squared_error: 0.0734 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0919 - mean_squared_error: 0.0919  36/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1000 - mean_squared_error: 0.1000 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.1021 - mean_squared_error: 0.1020 - val_loss: 0.1404 - val_mean_squared_error: 0.1402 Epoch 10/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - loss: 0.0550 - mean_squared_error: 0.0550 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0750 - mean_squared_error: 0.0750  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0799 - mean_squared_error: 0.0799 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0842 - mean_squared_error: 0.0842 - val_loss: 0.1464 - val_mean_squared_error: 0.1462 Epoch 11/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0934 - mean_squared_error: 0.0934 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1074 - mean_squared_error: 0.1074  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1056 - mean_squared_error: 0.1056 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1054 - mean_squared_error: 0.1054 - val_loss: 0.1295 - val_mean_squared_error: 0.1292 Epoch 12/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0923 - mean_squared_error: 0.0923 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0857 - mean_squared_error: 0.0857  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0854 - mean_squared_error: 0.0854 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0855 - mean_squared_error: 0.0855 - val_loss: 0.0978 - val_mean_squared_error: 0.0974 Epoch 13/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0480 - mean_squared_error: 0.0480 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0747 - mean_squared_error: 0.0747  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0714 - mean_squared_error: 0.0714 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0720 - mean_squared_error: 0.0720 - val_loss: 0.1079 - val_mean_squared_error: 0.1076 Epoch 14/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0619 - mean_squared_error: 0.0619 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0637 - mean_squared_error: 0.0637  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0697 - mean_squared_error: 0.0697 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0713 - mean_squared_error: 0.0713 - val_loss: 0.0941 - val_mean_squared_error: 0.0939 Epoch 15/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0391 - mean_squared_error: 0.0391 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0812 - mean_squared_error: 0.0812  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0780 - mean_squared_error: 0.0780 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0769 - mean_squared_error: 0.0769 - val_loss: 0.1361 - val_mean_squared_error: 0.1356 Epoch 16/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.1000 - mean_squared_error: 0.1000 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1036 - mean_squared_error: 0.1036  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0972 - mean_squared_error: 0.0972 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0937 - mean_squared_error: 0.0937 - val_loss: 0.1018 - val_mean_squared_error: 0.1012 Epoch 17/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0461 - mean_squared_error: 0.0461 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0828 - mean_squared_error: 0.0828  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0894 - mean_squared_error: 0.0894 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0899 - mean_squared_error: 0.0899 - val_loss: 0.0980 - val_mean_squared_error: 0.0978 Epoch 18/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0379 - mean_squared_error: 0.0379 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0720 - mean_squared_error: 0.0720  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0755 - mean_squared_error: 0.0755 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0774 - mean_squared_error: 0.0774 - val_loss: 0.1297 - val_mean_squared_error: 0.1298 Epoch 19/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - loss: 0.2111 - mean_squared_error: 0.2111 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1010 - mean_squared_error: 0.1010  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0880 - mean_squared_error: 0.0880 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0848 - mean_squared_error: 0.0848 - val_loss: 0.0991 - val_mean_squared_error: 0.0985 Epoch 20/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0404 - mean_squared_error: 0.0404 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0747 - mean_squared_error: 0.0747  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0756 - mean_squared_error: 0.0756 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.0754 - mean_squared_error: 0.0754 - val_loss: 0.0959 - val_mean_squared_error: 0.0953 Epoch 21/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0524 - mean_squared_error: 0.0524 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0621 - mean_squared_error: 0.0621  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0649 - mean_squared_error: 0.0649 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0666 - mean_squared_error: 0.0666 - val_loss: 0.1434 - val_mean_squared_error: 0.1427 Epoch 22/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.1190 - mean_squared_error: 0.1190 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0940 - mean_squared_error: 0.0940  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0879 - mean_squared_error: 0.0879 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0846 - mean_squared_error: 0.0846 - val_loss: 0.1155 - val_mean_squared_error: 0.1152 Epoch 23/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0357 - mean_squared_error: 0.0357 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0758 - mean_squared_error: 0.0758  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0727 - mean_squared_error: 0.0727 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0733 - mean_squared_error: 0.0733 - val_loss: 0.1719 - val_mean_squared_error: 0.1715 Epoch 24/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.1382 - mean_squared_error: 0.1382 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1018 - mean_squared_error: 0.1018  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1014 - mean_squared_error: 0.1014 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1008 - mean_squared_error: 0.1008 - val_loss: 0.1484 - val_mean_squared_error: 0.1473 Epoch 25/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.1100 - mean_squared_error: 0.1100 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0780 - mean_squared_error: 0.0780  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0775 - mean_squared_error: 0.0775 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0772 - mean_squared_error: 0.0771 - val_loss: 0.1266 - val_mean_squared_error: 0.1260 Epoch 26/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0271 - mean_squared_error: 0.0271 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0673 - mean_squared_error: 0.0673  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0720 - mean_squared_error: 0.0720 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0743 - mean_squared_error: 0.0742 - val_loss: 0.1249 - val_mean_squared_error: 0.1243 Epoch 27/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0917 - mean_squared_error: 0.0917 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0798 - mean_squared_error: 0.0798  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0797 - mean_squared_error: 0.0797 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0812 - mean_squared_error: 0.0812 - val_loss: 0.1276 - val_mean_squared_error: 0.1270 Epoch 28/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.1065 - mean_squared_error: 0.1065 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0957 - mean_squared_error: 0.0957  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0928 - mean_squared_error: 0.0928 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0907 - mean_squared_error: 0.0907 - val_loss: 0.1073 - val_mean_squared_error: 0.1068 Epoch 29/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0368 - mean_squared_error: 0.0368 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0679 - mean_squared_error: 0.0679  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0753 - mean_squared_error: 0.0753 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0776 - mean_squared_error: 0.0776 - val_loss: 0.1135 - val_mean_squared_error: 0.1130 Epoch 30/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0440 - mean_squared_error: 0.0440 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0765 - mean_squared_error: 0.0765  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0765 - mean_squared_error: 0.0765 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0759 - mean_squared_error: 0.0759 - val_loss: 0.0944 - val_mean_squared_error: 0.0937 Epoch 31/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - loss: 0.0638 - mean_squared_error: 0.0638 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0772 - mean_squared_error: 0.0772  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0767 - mean_squared_error: 0.0767 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0758 - mean_squared_error: 0.0758 - val_loss: 0.1292 - val_mean_squared_error: 0.1290 Epoch 32/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0641 - mean_squared_error: 0.0641 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0654 - mean_squared_error: 0.0654  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0650 - mean_squared_error: 0.0650 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0648 - mean_squared_error: 0.0648 - val_loss: 0.1266 - val_mean_squared_error: 0.1262 Epoch 33/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0300 - mean_squared_error: 0.0300 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0650 - mean_squared_error: 0.0650  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0738 - mean_squared_error: 0.0738 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0803 - mean_squared_error: 0.0801 - val_loss: 0.1583 - val_mean_squared_error: 0.1581 Epoch 34/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.1576 - mean_squared_error: 0.1576 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1022 - mean_squared_error: 0.1022  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0953 - mean_squared_error: 0.0953 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0922 - mean_squared_error: 0.0922 - val_loss: 0.0931 - val_mean_squared_error: 0.0930 Epoch 35/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0653 - mean_squared_error: 0.0653 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0649 - mean_squared_error: 0.0649  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0672 - mean_squared_error: 0.0672 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0674 - mean_squared_error: 0.0674 - val_loss: 0.0899 - val_mean_squared_error: 0.0894 Epoch 36/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0821 - mean_squared_error: 0.0821 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0649 - mean_squared_error: 0.0649  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0645 - mean_squared_error: 0.0645 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0655 - mean_squared_error: 0.0655 - val_loss: 0.1079 - val_mean_squared_error: 0.1075 Epoch 37/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - loss: 0.0458 - mean_squared_error: 0.0458 19/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0622 - mean_squared_error: 0.0622  37/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0652 - mean_squared_error: 0.0652 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0662 - mean_squared_error: 0.0662 - val_loss: 0.1053 - val_mean_squared_error: 0.1047 Epoch 38/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - loss: 0.0965 - mean_squared_error: 0.0965 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0713 - mean_squared_error: 0.0713  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0682 - mean_squared_error: 0.0682 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0684 - mean_squared_error: 0.0684 - val_loss: 0.1196 - val_mean_squared_error: 0.1190 Epoch 39/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - loss: 0.0816 - mean_squared_error: 0.0816 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0681 - mean_squared_error: 0.0681  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0684 - mean_squared_error: 0.0684 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0686 - mean_squared_error: 0.0686 - val_loss: 0.1080 - val_mean_squared_error: 0.1074 Epoch 40/40  1/48 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - loss: 0.1331 - mean_squared_error: 0.1331 20/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0645 - mean_squared_error: 0.0645  38/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0601 - mean_squared_error: 0.0601 48/48 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.0587 - mean_squared_error: 0.0588 - val_loss: 0.1034 - val_mean_squared_error: 0.1033 Testing: Case True Estimate 1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 76ms/step 4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step 4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step 0: 0.0533 0.1762 0.4148 0.3883 0.7065 0.8284 4.1899 4.3364 1: 0.2524 0.3039 0.8448 0.8602 0.8071 0.9623 3.9149 4.0030 2: 0.0107 0.1771 0.8647 0.8798 0.5868 0.6944 4.1577 4.2060 3: 0.8701 0.8538 0.8770 0.8923 0.8049 0.9345 1.2361 1.2290 4: 0.2565 0.2665 0.8393 0.8182 0.2386 0.3007 2.4532 2.7728 5: 0.4201 0.3588 0.5087 0.5060 0.7088 0.8837 0.6869 0.6648 6: 0.2081 0.2478 0.8591 0.8970 0.3090 0.3711 4.6909 4.3767 7: 0.4647 0.4935 0.9222 0.9206 0.2337 0.2956 4.6600 4.3489 8: 0.4288 0.3756 0.3561 0.1404 0.3357 0.6096 1.9844 1.9137 9: 0.3106 0.3269 0.0175 0.0455 0.5570 0.6097 2.8915 2.9651 10: 0.1094 0.1945 0.8587 0.9405 0.6004 0.7561 3.2209 3.3809 11: 0.5180 0.4834 0.1388 0.1812 0.8209 0.8372 1.4869 1.5776 12: 0.3125 0.3244 0.2865 0.2265 0.6393 0.8694 4.6788 4.6729 13: 0.6529 0.6631 0.0296 0.0163 0.5605 0.6794 2.2257 2.2830 14: 0.9364 1.0462 0.8226 0.9029 0.4616 0.5543 2.6613 2.8217 15: 0.5630 0.5535 0.2812 0.2854 0.6169 0.8337 0.6284 0.6779 16: 0.3616 0.3297 0.8103 0.9729 0.4467 0.6539 3.4632 3.6235 17: 0.7339 0.8581 0.8993 0.9958 0.6990 0.9066 1.7295 1.7472 18: 0.6534 0.7455 0.4544 0.4983 0.9891 1.0645 3.4852 3.6295 19: 0.9088 1.1924 0.5415 0.5748 0.5444 0.8486 3.9484 4.0265 20: 0.1705 0.2520 0.9272 1.0032 0.2299 0.2251 0.5865 1.0681 21: 0.3395 0.3622 0.2021 0.1589 0.6757 0.8237 3.6248 3.8375 22: 0.7887 0.8950 0.7724 0.9694 0.1817 0.3110 0.9908 1.3432 23: 0.7317 0.7401 0.3035 0.2288 0.1156 0.2688 3.0580 1.5979 24: 0.2770 0.3105 0.2465 0.1528 0.2878 0.3683 3.9347 4.2900 25: 0.1238 0.1496 0.2075 0.2328 0.5581 0.5833 0.5233 0.6577 26: 0.7485 0.7634 0.3051 0.2499 0.1202 0.2756 4.9072 3.1491 27: 0.7581 0.7359 0.0065 -0.0195 0.4610 0.4929 2.2979 2.2221 28: 0.8421 0.8536 0.6602 0.7888 0.0556 0.2075 3.6176 1.3977 29: 0.6801 0.8440 0.5420 0.6066 0.9614 1.0273 4.7790 4.7499 30: 0.8121 0.7516 0.0026 0.1226 0.7774 0.8476 2.0628 1.9370 31: 0.0380 -0.0203 0.4476 0.4545 0.2429 0.2835 2.3668 2.9262 32: 0.5319 0.5175 0.1126 0.1772 0.8375 0.8545 1.7688 1.8215 33: 0.7075 0.6826 0.0003 -0.0044 0.4658 0.5112 4.0787 4.2189 34: 0.7354 0.8631 0.2681 0.1962 0.6206 0.9286 2.5739 2.6575 35: 0.7628 0.8235 0.8371 0.9019 0.2978 0.3684 3.1344 3.3349 36: 0.9028 1.0313 0.4520 0.4100 0.4978 0.8119 2.0777 2.0383 37: 0.3399 0.4202 0.3203 0.3336 0.8856 0.9546 4.8253 4.8271 38: 0.6548 0.7898 0.4837 0.4758 0.4054 0.7415 0.5180 0.5505 39: 0.2854 0.1523 0.0057 0.0807 0.1188 0.1830 2.1917 1.8578 40: 0.3691 0.4416 0.5878 0.6120 0.9780 1.0002 4.6161 4.6617 41: 0.3546 0.2828 0.0032 0.0245 0.2646 0.2484 0.7880 1.4441 42: 0.4440 0.4704 0.9089 1.0868 0.0931 0.2043 1.7749 1.4987 43: 0.6250 0.6013 0.9024 0.9263 0.3330 0.3758 2.3724 2.4784 44: 0.3722 0.2992 0.3524 0.3368 0.8641 0.8680 1.1542 1.2650 45: 0.4103 0.3604 0.3601 0.3032 0.2971 0.4542 4.3382 4.3941 46: 0.5909 0.7706 0.6346 0.8363 0.5343 0.9410 2.7246 2.5825 47: 0.9204 1.1426 0.4849 0.4301 0.5915 0.9010 3.9574 3.9745 48: 0.6227 0.7494 0.2042 0.2132 0.8024 0.9591 4.2847 4.4010 49: 0.2587 0.1999 0.7354 0.9109 0.4308 0.6125 2.5532 2.7319 50: 0.6691 0.8157 0.6353 0.7890 0.4778 0.8686 0.8438 0.7471 51: 0.2882 0.4686 0.9447 0.8621 0.9761 0.9892 4.7717 4.6565 52: 0.7061 0.8317 0.7395 1.0110 0.2938 0.5270 1.3358 1.1812 53: 0.4320 0.4243 0.4675 0.5023 0.9109 0.9921 2.5809 2.6598 54: 0.4020 0.2248 0.4713 0.4294 0.4717 0.8240 2.6574 2.4423 55: 0.5900 0.6233 0.2135 0.0349 0.3570 0.4868 2.1319 2.2558 56: 0.9848 0.9816 0.6102 0.7274 0.9061 1.0566 3.1403 2.9637 57: 0.0936 0.1837 0.8355 0.9106 0.4327 0.4937 3.6403 3.9030 58: 0.5904 0.5754 0.1213 0.0362 0.2625 0.4267 0.8720 0.9316 59: 0.4737 0.4854 0.9788 1.0309 0.7095 0.8289 1.2136 1.2914 60: 0.1257 0.2209 0.3469 0.3076 0.7414 0.8598 4.1900 4.3885 61: 0.9835 0.9190 0.1586 0.2137 0.3143 0.3859 0.7065 1.1079 62: 0.4537 0.4729 0.8514 0.9642 0.7394 0.9435 2.4153 2.3944 63: 0.6947 0.8893 0.3217 0.2824 0.7330 0.9879 4.2552 4.3380 64: 0.2335 0.2954 0.9420 0.9801 0.7337 0.8244 1.7124 1.7565 65: 0.8378 0.6958 0.0217 0.2316 0.1609 0.1900 0.7521 1.6291 66: 0.9188 0.8591 0.9132 0.9327 0.1539 0.2158 2.9260 2.7915 67: 0.3143 0.1686 0.5737 0.6528 0.4188 0.7367 1.0060 0.9503 68: 0.3345 0.3493 0.2548 0.2923 0.8979 0.9079 2.2827 2.3618 69: 0.6127 0.5466 0.9824 0.8998 0.2780 0.2919 3.4744 3.7347 70: 0.9612 1.1353 0.5480 0.5750 0.5631 0.8281 3.1952 3.1708 71: 0.0258 0.1777 0.2652 0.2619 0.6344 0.7575 4.3315 4.3892 72: 0.9546 0.8393 0.8774 0.9432 0.1186 0.2550 1.1039 1.2527 73: 0.6963 0.7988 0.5258 0.5555 0.6016 0.9157 0.7927 0.7340 74: 0.3073 0.2449 0.3303 0.2247 0.5871 0.8119 3.0712 3.2949 75: 0.1994 0.2471 0.4540 0.4879 0.9377 0.9276 2.4727 2.5484 76: 0.1998 0.1444 0.2792 0.1628 0.1592 0.2911 2.7715 2.8895 77: 0.2810 0.2998 0.4269 0.4602 0.9022 0.9668 3.2933 3.4395 78: 0.4083 0.3300 0.0197 0.0475 0.3970 0.4549 0.6634 0.8889 79: 0.0717 0.2928 0.1969 0.2919 0.9169 0.8900 4.9400 4.8513 80: 0.9691 1.0646 0.4231 0.4244 0.8040 1.0007 4.8340 4.5944 81: 0.8564 1.0104 0.7131 0.8281 0.8571 1.0210 4.5947 4.4807 82: 0.2946 0.3186 0.8774 0.9294 0.7630 0.8687 1.8841 1.9107 83: 0.1502 0.1721 0.3170 0.2890 0.6733 0.8046 2.5900 2.7698 84: 0.1820 0.1608 0.5310 0.5361 0.7158 0.8281 2.0510 2.1160 85: 0.5881 0.5896 0.4428 0.4067 0.8698 0.9241 1.7303 1.7948 86: 0.5541 0.5830 0.7448 0.8291 0.9015 0.9538 1.7317 1.7950 87: 0.2368 0.1718 0.2523 0.1416 0.4487 0.5945 2.5717 2.8090 88: 0.4347 0.3822 0.9371 1.0158 0.0872 0.1883 4.0897 2.5051 89: 0.3972 0.4268 0.9291 0.8683 0.7655 0.8111 0.7357 0.7403 90: 0.8445 1.0057 0.4464 0.4481 0.2954 0.4441 2.6918 2.6432 91: 0.8179 0.9303 0.8538 1.0152 0.5248 0.6864 3.6616 3.7850 92: 0.1815 0.1528 0.2137 0.1402 0.0787 0.1749 1.8056 1.4261 93: 0.9630 1.2641 0.6113 0.6698 0.2411 0.3780 4.6328 3.8373 94: 0.2454 0.1998 0.4190 0.3520 0.6189 0.8501 4.0559 4.2587 95: 0.3092 0.3307 0.9984 0.9833 0.6071 0.7750 4.1939 4.1505 96: 0.6085 0.7251 0.6672 0.8355 0.2185 0.4139 1.0588 0.9052 97: 0.2330 0.3412 0.0190 0.2069 0.9669 0.8557 2.3691 2.4264 98: 0.6278 0.7772 0.0504 -0.0107 0.1438 0.2896 3.9237 3.9715 99: 0.6469 0.6821 0.2624 0.3005 0.9307 0.9918 2.5781 2.6799 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. Sun Dec 15 10:46:49 2024