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s464903 2024-06-09 14:05:06 +02:00
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my_runs/1/config.json Normal file
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{
"dropout_layer_value": 0.4,
"num_epochs": 100,
"seed": 512638064
}

213
my_runs/1/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 1/100
2/2 - 1s - 340ms/step - accuracy: 0.4390 - loss: 2.1215 - val_accuracy: 0.6429 - val_loss: 2.0350
Epoch 2/100
2/2 - 0s - 23ms/step - accuracy: 0.3659 - loss: 2.0694 - val_accuracy: 0.7143 - val_loss: 2.0104
Epoch 3/100
2/2 - 0s - 22ms/step - accuracy: 0.3659 - loss: 2.1309 - val_accuracy: 0.8214 - val_loss: 1.9882
Epoch 4/100
2/2 - 0s - 23ms/step - accuracy: 0.4390 - loss: 2.0289 - val_accuracy: 0.8214 - val_loss: 1.9593
Epoch 5/100
2/2 - 0s - 21ms/step - accuracy: 0.6341 - loss: 1.9654 - val_accuracy: 0.8214 - val_loss: 1.9378
Epoch 6/100
2/2 - 0s - 22ms/step - accuracy: 0.6098 - loss: 2.0383 - val_accuracy: 0.8214 - val_loss: 1.9154
Epoch 7/100
2/2 - 0s - 22ms/step - accuracy: 0.6098 - loss: 2.0238 - val_accuracy: 0.8214 - val_loss: 1.8964
Epoch 8/100
2/2 - 0s - 23ms/step - accuracy: 0.6098 - loss: 1.9397 - val_accuracy: 0.8571 - val_loss: 1.8766
Epoch 9/100
2/2 - 0s - 25ms/step - accuracy: 0.5366 - loss: 1.9641 - val_accuracy: 0.8571 - val_loss: 1.8561
Epoch 10/100
2/2 - 0s - 23ms/step - accuracy: 0.5610 - loss: 1.9581 - val_accuracy: 0.8571 - val_loss: 1.8380
Epoch 11/100
2/2 - 0s - 23ms/step - accuracy: 0.7561 - loss: 1.9044 - val_accuracy: 0.8571 - val_loss: 1.8207
Epoch 12/100
2/2 - 0s - 23ms/step - accuracy: 0.5854 - loss: 1.9392 - val_accuracy: 0.8571 - val_loss: 1.8004
Epoch 13/100
2/2 - 0s - 24ms/step - accuracy: 0.7073 - loss: 1.8718 - val_accuracy: 0.8571 - val_loss: 1.7812
Epoch 14/100
2/2 - 0s - 22ms/step - accuracy: 0.7561 - loss: 1.8057 - val_accuracy: 0.8571 - val_loss: 1.7620
Epoch 15/100
2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.8354 - val_accuracy: 0.8571 - val_loss: 1.7440
Epoch 16/100
2/2 - 0s - 24ms/step - accuracy: 0.7073 - loss: 1.8501 - val_accuracy: 0.8571 - val_loss: 1.7269
Epoch 17/100
2/2 - 0s - 24ms/step - accuracy: 0.7561 - loss: 1.7831 - val_accuracy: 0.8571 - val_loss: 1.7084
Epoch 18/100
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.7120 - val_accuracy: 0.8571 - val_loss: 1.6931
Epoch 19/100
2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.8020 - val_accuracy: 0.8571 - val_loss: 1.6786
Epoch 20/100
2/2 - 0s - 24ms/step - accuracy: 0.8049 - loss: 1.7531 - val_accuracy: 0.8571 - val_loss: 1.6630
Epoch 21/100
2/2 - 0s - 21ms/step - accuracy: 0.7561 - loss: 1.7808 - val_accuracy: 0.8571 - val_loss: 1.6489
Epoch 22/100
2/2 - 0s - 21ms/step - accuracy: 0.7561 - loss: 1.7794 - val_accuracy: 0.8571 - val_loss: 1.6352
Epoch 23/100
2/2 - 0s - 23ms/step - accuracy: 0.7805 - loss: 1.6697 - val_accuracy: 0.8571 - val_loss: 1.6184
Epoch 24/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.6814 - val_accuracy: 0.8571 - val_loss: 1.6058
Epoch 25/100
2/2 - 0s - 29ms/step - accuracy: 0.8293 - loss: 1.6687 - val_accuracy: 0.8571 - val_loss: 1.5919
Epoch 26/100
2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.7052 - val_accuracy: 0.8571 - val_loss: 1.5786
Epoch 27/100
2/2 - 0s - 21ms/step - accuracy: 0.8293 - loss: 1.6147 - val_accuracy: 0.8571 - val_loss: 1.5663
Epoch 28/100
2/2 - 0s - 21ms/step - accuracy: 0.7805 - loss: 1.6207 - val_accuracy: 0.8571 - val_loss: 1.5529
Epoch 29/100
2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.5964 - val_accuracy: 0.8571 - val_loss: 1.5413
Epoch 30/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.6258 - val_accuracy: 0.8571 - val_loss: 1.5294
Epoch 31/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5512 - val_accuracy: 0.8571 - val_loss: 1.5175
Epoch 32/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.6674 - val_accuracy: 0.8571 - val_loss: 1.5070
Epoch 33/100
2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.5848 - val_accuracy: 0.8571 - val_loss: 1.4977
Epoch 34/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5467 - val_accuracy: 0.8571 - val_loss: 1.4869
Epoch 35/100
2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.6244 - val_accuracy: 0.8571 - val_loss: 1.4778
Epoch 36/100
2/2 - 0s - 24ms/step - accuracy: 0.8293 - loss: 1.5275 - val_accuracy: 0.8571 - val_loss: 1.4672
Epoch 37/100
2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.7105 - val_accuracy: 0.8571 - val_loss: 1.4595
Epoch 38/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.6137 - val_accuracy: 0.8571 - val_loss: 1.4509
Epoch 39/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5726 - val_accuracy: 0.8571 - val_loss: 1.4425
Epoch 40/100
2/2 - 0s - 21ms/step - accuracy: 0.8293 - loss: 1.5345 - val_accuracy: 0.8571 - val_loss: 1.4347
Epoch 41/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5689 - val_accuracy: 0.8571 - val_loss: 1.4269
Epoch 42/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4239 - val_accuracy: 0.8571 - val_loss: 1.4175
Epoch 43/100
2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.5922 - val_accuracy: 0.8571 - val_loss: 1.4099
Epoch 44/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5006 - val_accuracy: 0.8571 - val_loss: 1.4021
Epoch 45/100
2/2 - 0s - 21ms/step - accuracy: 0.8049 - loss: 1.4858 - val_accuracy: 0.8571 - val_loss: 1.3944
Epoch 46/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4769 - val_accuracy: 0.8571 - val_loss: 1.3874
Epoch 47/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4211 - val_accuracy: 0.8571 - val_loss: 1.3796
Epoch 48/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4060 - val_accuracy: 0.8571 - val_loss: 1.3717
Epoch 49/100
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4741 - val_accuracy: 0.8571 - val_loss: 1.3652
Epoch 50/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4989 - val_accuracy: 0.8571 - val_loss: 1.3588
Epoch 51/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4718 - val_accuracy: 0.8571 - val_loss: 1.3521
Epoch 52/100
2/2 - 0s - 24ms/step - accuracy: 0.8293 - loss: 1.4712 - val_accuracy: 0.8571 - val_loss: 1.3482
Epoch 53/100
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.3657 - val_accuracy: 0.8571 - val_loss: 1.3425
Epoch 54/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3847 - val_accuracy: 0.8571 - val_loss: 1.3366
Epoch 55/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3766 - val_accuracy: 0.8571 - val_loss: 1.3315
Epoch 56/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4275 - val_accuracy: 0.8571 - val_loss: 1.3253
Epoch 57/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4709 - val_accuracy: 0.8571 - val_loss: 1.3209
Epoch 58/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4226 - val_accuracy: 0.8571 - val_loss: 1.3176
Epoch 59/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4491 - val_accuracy: 0.8571 - val_loss: 1.3126
Epoch 60/100
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.4165 - val_accuracy: 0.8571 - val_loss: 1.3073
Epoch 61/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3914 - val_accuracy: 0.8571 - val_loss: 1.3027
Epoch 62/100
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3560 - val_accuracy: 0.8571 - val_loss: 1.2974
Epoch 63/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3432 - val_accuracy: 0.8571 - val_loss: 1.2915
Epoch 64/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3833 - val_accuracy: 0.8571 - val_loss: 1.2866
Epoch 65/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4477 - val_accuracy: 0.8571 - val_loss: 1.2834
Epoch 66/100
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3054 - val_accuracy: 0.8571 - val_loss: 1.2794
Epoch 67/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2840 - val_accuracy: 0.8571 - val_loss: 1.2742
Epoch 68/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4320 - val_accuracy: 0.8571 - val_loss: 1.2703
Epoch 69/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4165 - val_accuracy: 0.8571 - val_loss: 1.2664
Epoch 70/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3210 - val_accuracy: 0.8571 - val_loss: 1.2617
Epoch 71/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3698 - val_accuracy: 0.8571 - val_loss: 1.2580
Epoch 72/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3679 - val_accuracy: 0.8571 - val_loss: 1.2546
Epoch 73/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3647 - val_accuracy: 0.8571 - val_loss: 1.2506
Epoch 74/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3088 - val_accuracy: 0.8571 - val_loss: 1.2465
Epoch 75/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3304 - val_accuracy: 0.8571 - val_loss: 1.2425
Epoch 76/100
2/2 - 0s - 29ms/step - accuracy: 0.8537 - loss: 1.2813 - val_accuracy: 0.8571 - val_loss: 1.2389
Epoch 77/100
2/2 - 0s - 28ms/step - accuracy: 0.8537 - loss: 1.3477 - val_accuracy: 0.8571 - val_loss: 1.2352
Epoch 78/100
2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 1.3885 - val_accuracy: 0.8571 - val_loss: 1.2315
Epoch 79/100
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2939 - val_accuracy: 0.8571 - val_loss: 1.2280
Epoch 80/100
2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 1.3508 - val_accuracy: 0.8571 - val_loss: 1.2254
Epoch 81/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3219 - val_accuracy: 0.8571 - val_loss: 1.2223
Epoch 82/100
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.3559 - val_accuracy: 0.8571 - val_loss: 1.2190
Epoch 83/100
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3289 - val_accuracy: 0.8571 - val_loss: 1.2159
Epoch 84/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1656 - val_accuracy: 0.8571 - val_loss: 1.2123
Epoch 85/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2954 - val_accuracy: 0.8571 - val_loss: 1.2088
Epoch 86/100
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2298 - val_accuracy: 0.8571 - val_loss: 1.2058
Epoch 87/100
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3323 - val_accuracy: 0.8571 - val_loss: 1.2037
Epoch 88/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3758 - val_accuracy: 0.8571 - val_loss: 1.2010
Epoch 89/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3587 - val_accuracy: 0.8571 - val_loss: 1.1981
Epoch 90/100
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3240 - val_accuracy: 0.8571 - val_loss: 1.1954
Epoch 91/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2884 - val_accuracy: 0.8571 - val_loss: 1.1922
Epoch 92/100
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3293 - val_accuracy: 0.8571 - val_loss: 1.1901
Epoch 93/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2706 - val_accuracy: 0.8571 - val_loss: 1.1879
Epoch 94/100
2/2 - 0s - 27ms/step - accuracy: 0.8537 - loss: 1.2715 - val_accuracy: 0.8571 - val_loss: 1.1848
Epoch 95/100
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2628 - val_accuracy: 0.8571 - val_loss: 1.1818
Epoch 96/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2770 - val_accuracy: 0.8571 - val_loss: 1.1786
Epoch 97/100
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3039 - val_accuracy: 0.8571 - val_loss: 1.1762
Epoch 98/100
2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 1.2908 - val_accuracy: 0.8571 - val_loss: 1.1731
Epoch 99/100
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3510 - val_accuracy: 0.8571 - val_loss: 1.1707
Epoch 100/100
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2447 - val_accuracy: 0.8571 - val_loss: 1.1683
1/1 - 0s - 26ms/step - accuracy: 0.8571 - loss: 1.1683
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Dokładność testowa: 85.71%
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.8571 - loss: 1.1683 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.8571 - loss: 1.1683

15
my_runs/1/info.json Normal file
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{
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{
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"py/object": "datetime.datetime"
}
}

248
my_runs/1/metrics.json Normal file
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