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my_runs/2/config.json Normal file
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{
"dropout_layer_value": 0.3,
"num_epochs": 200,
"seed": 487013218
}

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my_runs/2/cout.txt Normal file
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2.16.1
1.2.0
3.2.1
1.23.5
1.5.2
C:\Users\obses\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\preprocessing\_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.
warnings.warn(
C:\Users\obses\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Epoch 1/200
2/2 - 1s - 341ms/step - accuracy: 0.2195 - loss: 2.1532 - val_accuracy: 0.1071 - val_loss: 2.0147
Epoch 2/200
2/2 - 0s - 22ms/step - accuracy: 0.2683 - loss: 2.0820 - val_accuracy: 0.1786 - val_loss: 1.9722
Epoch 3/200
2/2 - 0s - 21ms/step - accuracy: 0.3171 - loss: 2.0352 - val_accuracy: 0.2500 - val_loss: 1.9284
Epoch 4/200
2/2 - 0s - 23ms/step - accuracy: 0.3902 - loss: 2.0307 - val_accuracy: 0.2500 - val_loss: 1.8926
Epoch 5/200
2/2 - 0s - 22ms/step - accuracy: 0.3415 - loss: 2.0465 - val_accuracy: 0.4286 - val_loss: 1.8532
Epoch 6/200
2/2 - 0s - 25ms/step - accuracy: 0.4878 - loss: 1.9187 - val_accuracy: 0.6786 - val_loss: 1.8218
Epoch 7/200
2/2 - 0s - 23ms/step - accuracy: 0.4878 - loss: 1.9450 - val_accuracy: 0.7500 - val_loss: 1.7915
Epoch 8/200
2/2 - 0s - 28ms/step - accuracy: 0.4634 - loss: 1.9840 - val_accuracy: 0.8571 - val_loss: 1.7630
Epoch 9/200
2/2 - 0s - 25ms/step - accuracy: 0.5366 - loss: 1.8964 - val_accuracy: 0.8571 - val_loss: 1.7411
Epoch 10/200
2/2 - 0s - 24ms/step - accuracy: 0.6098 - loss: 1.8146 - val_accuracy: 0.8571 - val_loss: 1.7117
Epoch 11/200
2/2 - 0s - 22ms/step - accuracy: 0.6341 - loss: 1.7571 - val_accuracy: 0.8571 - val_loss: 1.6873
Epoch 12/200
2/2 - 0s - 23ms/step - accuracy: 0.6341 - loss: 1.7847 - val_accuracy: 0.8571 - val_loss: 1.6686
Epoch 13/200
2/2 - 0s - 25ms/step - accuracy: 0.6585 - loss: 1.8141 - val_accuracy: 0.8571 - val_loss: 1.6528
Epoch 14/200
2/2 - 0s - 23ms/step - accuracy: 0.6098 - loss: 1.8549 - val_accuracy: 0.8571 - val_loss: 1.6383
Epoch 15/200
2/2 - 0s - 22ms/step - accuracy: 0.7561 - loss: 1.7476 - val_accuracy: 0.8571 - val_loss: 1.6207
Epoch 16/200
2/2 - 0s - 26ms/step - accuracy: 0.7561 - loss: 1.7351 - val_accuracy: 0.8571 - val_loss: 1.6039
Epoch 17/200
2/2 - 0s - 23ms/step - accuracy: 0.7561 - loss: 1.7050 - val_accuracy: 0.8571 - val_loss: 1.5861
Epoch 18/200
2/2 - 0s - 22ms/step - accuracy: 0.7317 - loss: 1.6731 - val_accuracy: 0.8571 - val_loss: 1.5715
Epoch 19/200
2/2 - 0s - 22ms/step - accuracy: 0.7073 - loss: 1.7423 - val_accuracy: 0.8571 - val_loss: 1.5589
Epoch 20/200
2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.6414 - val_accuracy: 0.8571 - val_loss: 1.5441
Epoch 21/200
2/2 - 0s - 21ms/step - accuracy: 0.8293 - loss: 1.6985 - val_accuracy: 0.8571 - val_loss: 1.5328
Epoch 22/200
2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.6529 - val_accuracy: 0.8571 - val_loss: 1.5257
Epoch 23/200
2/2 - 0s - 22ms/step - accuracy: 0.7805 - loss: 1.7366 - val_accuracy: 0.8571 - val_loss: 1.5157
Epoch 24/200
2/2 - 0s - 22ms/step - accuracy: 0.7317 - loss: 1.6614 - val_accuracy: 0.8571 - val_loss: 1.5040
Epoch 25/200
2/2 - 0s - 24ms/step - accuracy: 0.7805 - loss: 1.6441 - val_accuracy: 0.8571 - val_loss: 1.4938
Epoch 26/200
2/2 - 0s - 23ms/step - accuracy: 0.7805 - loss: 1.5172 - val_accuracy: 0.8571 - val_loss: 1.4833
Epoch 27/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.6298 - val_accuracy: 0.8571 - val_loss: 1.4746
Epoch 28/200
2/2 - 0s - 23ms/step - accuracy: 0.8293 - loss: 1.6074 - val_accuracy: 0.8571 - val_loss: 1.4663
Epoch 29/200
2/2 - 0s - 23ms/step - accuracy: 0.8293 - loss: 1.5785 - val_accuracy: 0.8571 - val_loss: 1.4557
Epoch 30/200
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.5357 - val_accuracy: 0.8571 - val_loss: 1.4468
Epoch 31/200
2/2 - 0s - 24ms/step - accuracy: 0.7805 - loss: 1.6030 - val_accuracy: 0.8571 - val_loss: 1.4381
Epoch 32/200
2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.5175 - val_accuracy: 0.8571 - val_loss: 1.4274
Epoch 33/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.5605 - val_accuracy: 0.8571 - val_loss: 1.4181
Epoch 34/200
2/2 - 0s - 23ms/step - accuracy: 0.8049 - loss: 1.5746 - val_accuracy: 0.8571 - val_loss: 1.4086
Epoch 35/200
2/2 - 0s - 23ms/step - accuracy: 0.8293 - loss: 1.5316 - val_accuracy: 0.8571 - val_loss: 1.4028
Epoch 36/200
2/2 - 0s - 23ms/step - accuracy: 0.8293 - loss: 1.4663 - val_accuracy: 0.8571 - val_loss: 1.3950
Epoch 37/200
2/2 - 0s - 22ms/step - accuracy: 0.7805 - loss: 1.5547 - val_accuracy: 0.8571 - val_loss: 1.3888
Epoch 38/200
2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.5016 - val_accuracy: 0.8571 - val_loss: 1.3825
Epoch 39/200
2/2 - 0s - 22ms/step - accuracy: 0.8780 - loss: 1.5481 - val_accuracy: 0.8571 - val_loss: 1.3769
Epoch 40/200
2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.4685 - val_accuracy: 0.8571 - val_loss: 1.3710
Epoch 41/200
2/2 - 0s - 23ms/step - accuracy: 0.8049 - loss: 1.4536 - val_accuracy: 0.8571 - val_loss: 1.3648
Epoch 42/200
2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.5299 - val_accuracy: 0.8571 - val_loss: 1.3620
Epoch 43/200
2/2 - 0s - 22ms/step - accuracy: 0.8780 - loss: 1.4518 - val_accuracy: 0.8571 - val_loss: 1.3558
Epoch 44/200
2/2 - 0s - 23ms/step - accuracy: 0.8293 - loss: 1.3933 - val_accuracy: 0.8571 - val_loss: 1.3482
Epoch 45/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4994 - val_accuracy: 0.8571 - val_loss: 1.3430
Epoch 46/200
2/2 - 0s - 24ms/step - accuracy: 0.8049 - loss: 1.5360 - val_accuracy: 0.8571 - val_loss: 1.3383
Epoch 47/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.5738 - val_accuracy: 0.8571 - val_loss: 1.3366
Epoch 48/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4864 - val_accuracy: 0.8571 - val_loss: 1.3335
Epoch 49/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5336 - val_accuracy: 0.8571 - val_loss: 1.3292
Epoch 50/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4462 - val_accuracy: 0.8571 - val_loss: 1.3244
Epoch 51/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4624 - val_accuracy: 0.8571 - val_loss: 1.3198
Epoch 52/200
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.4040 - val_accuracy: 0.8571 - val_loss: 1.3156
Epoch 53/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4051 - val_accuracy: 0.8571 - val_loss: 1.3118
Epoch 54/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4144 - val_accuracy: 0.8571 - val_loss: 1.3061
Epoch 55/200
2/2 - 0s - 24ms/step - accuracy: 0.8293 - loss: 1.4836 - val_accuracy: 0.8571 - val_loss: 1.3033
Epoch 56/200
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.4531 - val_accuracy: 0.8571 - val_loss: 1.2983
Epoch 57/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4848 - val_accuracy: 0.8571 - val_loss: 1.2964
Epoch 58/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4361 - val_accuracy: 0.8571 - val_loss: 1.2939
Epoch 59/200
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.4567 - val_accuracy: 0.8571 - val_loss: 1.2912
Epoch 60/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3697 - val_accuracy: 0.8571 - val_loss: 1.2865
Epoch 61/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4033 - val_accuracy: 0.8571 - val_loss: 1.2813
Epoch 62/200
2/2 - 0s - 30ms/step - accuracy: 0.8537 - loss: 1.4114 - val_accuracy: 0.8571 - val_loss: 1.2804
Epoch 63/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3471 - val_accuracy: 0.8571 - val_loss: 1.2759
Epoch 64/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4630 - val_accuracy: 0.8571 - val_loss: 1.2738
Epoch 65/200
2/2 - 0s - 23ms/step - accuracy: 0.8293 - loss: 1.3782 - val_accuracy: 0.8571 - val_loss: 1.2697
Epoch 66/200
2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 1.3674 - val_accuracy: 0.8571 - val_loss: 1.2650
Epoch 67/200
2/2 - 0s - 24ms/step - accuracy: 0.8293 - loss: 1.4286 - val_accuracy: 0.8571 - val_loss: 1.2608
Epoch 68/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3000 - val_accuracy: 0.8571 - val_loss: 1.2573
Epoch 69/200
2/2 - 0s - 27ms/step - accuracy: 0.8537 - loss: 1.4976 - val_accuracy: 0.8571 - val_loss: 1.2559
Epoch 70/200
2/2 - 0s - 27ms/step - accuracy: 0.8537 - loss: 1.3845 - val_accuracy: 0.8571 - val_loss: 1.2551
Epoch 71/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3196 - val_accuracy: 0.8571 - val_loss: 1.2515
Epoch 72/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3727 - val_accuracy: 0.8571 - val_loss: 1.2476
Epoch 73/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4068 - val_accuracy: 0.8571 - val_loss: 1.2452
Epoch 74/200
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3918 - val_accuracy: 0.8571 - val_loss: 1.2443
Epoch 75/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3303 - val_accuracy: 0.8571 - val_loss: 1.2417
Epoch 76/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2839 - val_accuracy: 0.8571 - val_loss: 1.2387
Epoch 77/200
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.3413 - val_accuracy: 0.8571 - val_loss: 1.2357
Epoch 78/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3142 - val_accuracy: 0.8571 - val_loss: 1.2324
Epoch 79/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2841 - val_accuracy: 0.8571 - val_loss: 1.2303
Epoch 80/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2535 - val_accuracy: 0.8571 - val_loss: 1.2264
Epoch 81/200
2/2 - 0s - 25ms/step - accuracy: 0.8293 - loss: 1.3405 - val_accuracy: 0.8571 - val_loss: 1.2234
Epoch 82/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3469 - val_accuracy: 0.8571 - val_loss: 1.2209
Epoch 83/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2764 - val_accuracy: 0.8571 - val_loss: 1.2176
Epoch 84/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3213 - val_accuracy: 0.8571 - val_loss: 1.2163
Epoch 85/200
2/2 - 0s - 27ms/step - accuracy: 0.8537 - loss: 1.3561 - val_accuracy: 0.8571 - val_loss: 1.2138
Epoch 86/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3907 - val_accuracy: 0.8571 - val_loss: 1.2119
Epoch 87/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3074 - val_accuracy: 0.8571 - val_loss: 1.2087
Epoch 88/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2177 - val_accuracy: 0.8571 - val_loss: 1.2045
Epoch 89/200
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.2806 - val_accuracy: 0.8571 - val_loss: 1.2026
Epoch 90/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2198 - val_accuracy: 0.8571 - val_loss: 1.1989
Epoch 91/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3314 - val_accuracy: 0.8571 - val_loss: 1.1972
Epoch 92/200
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3292 - val_accuracy: 0.8571 - val_loss: 1.1946
Epoch 93/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2781 - val_accuracy: 0.8571 - val_loss: 1.1925
Epoch 94/200
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3490 - val_accuracy: 0.8571 - val_loss: 1.1915
Epoch 95/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3358 - val_accuracy: 0.8571 - val_loss: 1.1894
Epoch 96/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3639 - val_accuracy: 0.8571 - val_loss: 1.1884
Epoch 97/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3290 - val_accuracy: 0.8571 - val_loss: 1.1859
Epoch 98/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2283 - val_accuracy: 0.8571 - val_loss: 1.1826
Epoch 99/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1849 - val_accuracy: 0.8571 - val_loss: 1.1794
Epoch 100/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2317 - val_accuracy: 0.8571 - val_loss: 1.1760
Epoch 101/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2240 - val_accuracy: 0.8571 - val_loss: 1.1737
Epoch 102/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3227 - val_accuracy: 0.8571 - val_loss: 1.1733
Epoch 103/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2884 - val_accuracy: 0.8571 - val_loss: 1.1714
Epoch 104/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2427 - val_accuracy: 0.8571 - val_loss: 1.1698
Epoch 105/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2661 - val_accuracy: 0.8571 - val_loss: 1.1673
Epoch 106/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2791 - val_accuracy: 0.8571 - val_loss: 1.1659
Epoch 107/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3218 - val_accuracy: 0.8571 - val_loss: 1.1651
Epoch 108/200
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2642 - val_accuracy: 0.8571 - val_loss: 1.1631
Epoch 109/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2632 - val_accuracy: 0.8571 - val_loss: 1.1618
Epoch 110/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2707 - val_accuracy: 0.8571 - val_loss: 1.1596
Epoch 111/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3358 - val_accuracy: 0.8571 - val_loss: 1.1585
Epoch 112/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2766 - val_accuracy: 0.8571 - val_loss: 1.1573
Epoch 113/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2075 - val_accuracy: 0.8571 - val_loss: 1.1543
Epoch 114/200
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2420 - val_accuracy: 0.8571 - val_loss: 1.1516
Epoch 115/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1918 - val_accuracy: 0.8571 - val_loss: 1.1505
Epoch 116/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2107 - val_accuracy: 0.8571 - val_loss: 1.1481
Epoch 117/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1907 - val_accuracy: 0.8571 - val_loss: 1.1456
Epoch 118/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1850 - val_accuracy: 0.8571 - val_loss: 1.1431
Epoch 119/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1576 - val_accuracy: 0.8571 - val_loss: 1.1403
Epoch 120/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2675 - val_accuracy: 0.8571 - val_loss: 1.1398
Epoch 121/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2585 - val_accuracy: 0.8571 - val_loss: 1.1380
Epoch 122/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2300 - val_accuracy: 0.8571 - val_loss: 1.1363
Epoch 123/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2724 - val_accuracy: 0.8571 - val_loss: 1.1346
Epoch 124/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2943 - val_accuracy: 0.8571 - val_loss: 1.1329
Epoch 125/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2581 - val_accuracy: 0.8571 - val_loss: 1.1324
Epoch 126/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2611 - val_accuracy: 0.8571 - val_loss: 1.1317
Epoch 127/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2528 - val_accuracy: 0.8571 - val_loss: 1.1301
Epoch 128/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1598 - val_accuracy: 0.8571 - val_loss: 1.1273
Epoch 129/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2340 - val_accuracy: 0.8571 - val_loss: 1.1253
Epoch 130/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1844 - val_accuracy: 0.8571 - val_loss: 1.1228
Epoch 131/200
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.2072 - val_accuracy: 0.8571 - val_loss: 1.1218
Epoch 132/200
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.2265 - val_accuracy: 0.8571 - val_loss: 1.1200
Epoch 133/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0870 - val_accuracy: 0.8571 - val_loss: 1.1173
Epoch 134/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1787 - val_accuracy: 0.8571 - val_loss: 1.1153
Epoch 135/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1851 - val_accuracy: 0.8571 - val_loss: 1.1136
Epoch 136/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1922 - val_accuracy: 0.8571 - val_loss: 1.1127
Epoch 137/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2262 - val_accuracy: 0.8571 - val_loss: 1.1107
Epoch 138/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2387 - val_accuracy: 0.8571 - val_loss: 1.1089
Epoch 139/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2239 - val_accuracy: 0.8571 - val_loss: 1.1076
Epoch 140/200
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2184 - val_accuracy: 0.8571 - val_loss: 1.1061
Epoch 141/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1624 - val_accuracy: 0.8571 - val_loss: 1.1037
Epoch 142/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1146 - val_accuracy: 0.8571 - val_loss: 1.1016
Epoch 143/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1702 - val_accuracy: 0.8571 - val_loss: 1.1000
Epoch 144/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1891 - val_accuracy: 0.8571 - val_loss: 1.0976
Epoch 145/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1246 - val_accuracy: 0.8571 - val_loss: 1.0954
Epoch 146/200
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Epoch 147/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1355 - val_accuracy: 0.8571 - val_loss: 1.0918
Epoch 148/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1685 - val_accuracy: 0.8571 - val_loss: 1.0902
Epoch 149/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1003 - val_accuracy: 0.8571 - val_loss: 1.0878
Epoch 150/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2653 - val_accuracy: 0.8571 - val_loss: 1.0869
Epoch 151/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1866 - val_accuracy: 0.8571 - val_loss: 1.0850
Epoch 152/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1047 - val_accuracy: 0.8571 - val_loss: 1.0823
Epoch 153/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0743 - val_accuracy: 0.8571 - val_loss: 1.0803
Epoch 154/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1167 - val_accuracy: 0.8571 - val_loss: 1.0783
Epoch 155/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2002 - val_accuracy: 0.8571 - val_loss: 1.0778
Epoch 156/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1217 - val_accuracy: 0.8571 - val_loss: 1.0753
Epoch 157/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1792 - val_accuracy: 0.8571 - val_loss: 1.0732
Epoch 158/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0793 - val_accuracy: 0.8571 - val_loss: 1.0709
Epoch 159/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1644 - val_accuracy: 0.8571 - val_loss: 1.0701
Epoch 160/200
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2049 - val_accuracy: 0.8571 - val_loss: 1.0684
Epoch 161/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0399 - val_accuracy: 0.8571 - val_loss: 1.0663
Epoch 162/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0994 - val_accuracy: 0.8571 - val_loss: 1.0644
Epoch 163/200
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1512 - val_accuracy: 0.8571 - val_loss: 1.0633
Epoch 164/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2293 - val_accuracy: 0.8571 - val_loss: 1.0629
Epoch 165/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0654 - val_accuracy: 0.8571 - val_loss: 1.0609
Epoch 166/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1464 - val_accuracy: 0.8571 - val_loss: 1.0593
Epoch 167/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0558 - val_accuracy: 0.8571 - val_loss: 1.0571
Epoch 168/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1222 - val_accuracy: 0.8571 - val_loss: 1.0559
Epoch 169/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1631 - val_accuracy: 0.8571 - val_loss: 1.0545
Epoch 170/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1823 - val_accuracy: 0.8571 - val_loss: 1.0534
Epoch 171/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1564 - val_accuracy: 0.8571 - val_loss: 1.0521
Epoch 172/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1264 - val_accuracy: 0.8571 - val_loss: 1.0507
Epoch 173/200
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1188 - val_accuracy: 0.8571 - val_loss: 1.0501
Epoch 174/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1830 - val_accuracy: 0.8571 - val_loss: 1.0493
Epoch 175/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0920 - val_accuracy: 0.8571 - val_loss: 1.0481
Epoch 176/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0943 - val_accuracy: 0.8571 - val_loss: 1.0460
Epoch 177/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1190 - val_accuracy: 0.8571 - val_loss: 1.0439
Epoch 178/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1286 - val_accuracy: 0.8571 - val_loss: 1.0428
Epoch 179/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0424 - val_accuracy: 0.8571 - val_loss: 1.0410
Epoch 180/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1394 - val_accuracy: 0.8571 - val_loss: 1.0398
Epoch 181/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1651 - val_accuracy: 0.8571 - val_loss: 1.0384
Epoch 182/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0441 - val_accuracy: 0.8571 - val_loss: 1.0364
Epoch 183/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0563 - val_accuracy: 0.8571 - val_loss: 1.0346
Epoch 184/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0919 - val_accuracy: 0.8571 - val_loss: 1.0331
Epoch 185/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1528 - val_accuracy: 0.8571 - val_loss: 1.0317
Epoch 186/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1534 - val_accuracy: 0.8571 - val_loss: 1.0301
Epoch 187/200
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.0890 - val_accuracy: 0.8571 - val_loss: 1.0286
Epoch 188/200
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1421 - val_accuracy: 0.8571 - val_loss: 1.0277
Epoch 189/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0819 - val_accuracy: 0.8571 - val_loss: 1.0258
Epoch 190/200
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1676 - val_accuracy: 0.8571 - val_loss: 1.0250
Epoch 191/200
2/2 - 0s - 27ms/step - accuracy: 0.8537 - loss: 1.1186 - val_accuracy: 0.8571 - val_loss: 1.0233
Epoch 192/200
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.0423 - val_accuracy: 0.8571 - val_loss: 1.0221
Epoch 193/200
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Epoch 194/200
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Epoch 195/200
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Epoch 196/200
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Epoch 197/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0680 - val_accuracy: 0.8571 - val_loss: 1.0154
Epoch 198/200
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0319 - val_accuracy: 0.8571 - val_loss: 1.0141
Epoch 199/200
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Epoch 200/200
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Dokładność testowa: 85.71%
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.8571 - loss: 1.0109 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.8571 - loss: 1.0109

15
my_runs/2/info.json Normal file
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448
my_runs/2/metrics.json Normal file
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