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my_runs/1/config.json
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my_runs/1/config.json
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{
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"dropout_layer_value": 0.4,
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"num_epochs": 100,
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"seed": 512638064
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}
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my_runs/1/cout.txt
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my_runs/1/cout.txt
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2.16.1
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1.2.0
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3.2.1
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1.23.5
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1.5.2
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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.
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warnings.warn(
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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.
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super().__init__(activity_regularizer=activity_regularizer, **kwargs)
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Epoch 1/100
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2/2 - 1s - 340ms/step - accuracy: 0.4390 - loss: 2.1215 - val_accuracy: 0.6429 - val_loss: 2.0350
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Epoch 2/100
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2/2 - 0s - 23ms/step - accuracy: 0.3659 - loss: 2.0694 - val_accuracy: 0.7143 - val_loss: 2.0104
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Epoch 3/100
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2/2 - 0s - 22ms/step - accuracy: 0.3659 - loss: 2.1309 - val_accuracy: 0.8214 - val_loss: 1.9882
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Epoch 4/100
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2/2 - 0s - 23ms/step - accuracy: 0.4390 - loss: 2.0289 - val_accuracy: 0.8214 - val_loss: 1.9593
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Epoch 5/100
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2/2 - 0s - 21ms/step - accuracy: 0.6341 - loss: 1.9654 - val_accuracy: 0.8214 - val_loss: 1.9378
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Epoch 6/100
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2/2 - 0s - 22ms/step - accuracy: 0.6098 - loss: 2.0383 - val_accuracy: 0.8214 - val_loss: 1.9154
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Epoch 7/100
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2/2 - 0s - 22ms/step - accuracy: 0.6098 - loss: 2.0238 - val_accuracy: 0.8214 - val_loss: 1.8964
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Epoch 8/100
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2/2 - 0s - 23ms/step - accuracy: 0.6098 - loss: 1.9397 - val_accuracy: 0.8571 - val_loss: 1.8766
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Epoch 9/100
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2/2 - 0s - 25ms/step - accuracy: 0.5366 - loss: 1.9641 - val_accuracy: 0.8571 - val_loss: 1.8561
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Epoch 10/100
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2/2 - 0s - 23ms/step - accuracy: 0.5610 - loss: 1.9581 - val_accuracy: 0.8571 - val_loss: 1.8380
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Epoch 11/100
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2/2 - 0s - 23ms/step - accuracy: 0.7561 - loss: 1.9044 - val_accuracy: 0.8571 - val_loss: 1.8207
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Epoch 12/100
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2/2 - 0s - 23ms/step - accuracy: 0.5854 - loss: 1.9392 - val_accuracy: 0.8571 - val_loss: 1.8004
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Epoch 13/100
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2/2 - 0s - 24ms/step - accuracy: 0.7073 - loss: 1.8718 - val_accuracy: 0.8571 - val_loss: 1.7812
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Epoch 14/100
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2/2 - 0s - 22ms/step - accuracy: 0.7561 - loss: 1.8057 - val_accuracy: 0.8571 - val_loss: 1.7620
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Epoch 15/100
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2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.8354 - val_accuracy: 0.8571 - val_loss: 1.7440
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Epoch 16/100
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2/2 - 0s - 24ms/step - accuracy: 0.7073 - loss: 1.8501 - val_accuracy: 0.8571 - val_loss: 1.7269
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Epoch 17/100
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2/2 - 0s - 24ms/step - accuracy: 0.7561 - loss: 1.7831 - val_accuracy: 0.8571 - val_loss: 1.7084
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Epoch 18/100
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2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.7120 - val_accuracy: 0.8571 - val_loss: 1.6931
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Epoch 19/100
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2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.8020 - val_accuracy: 0.8571 - val_loss: 1.6786
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Epoch 20/100
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2/2 - 0s - 24ms/step - accuracy: 0.8049 - loss: 1.7531 - val_accuracy: 0.8571 - val_loss: 1.6630
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Epoch 21/100
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2/2 - 0s - 21ms/step - accuracy: 0.7561 - loss: 1.7808 - val_accuracy: 0.8571 - val_loss: 1.6489
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Epoch 22/100
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2/2 - 0s - 21ms/step - accuracy: 0.7561 - loss: 1.7794 - val_accuracy: 0.8571 - val_loss: 1.6352
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Epoch 23/100
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2/2 - 0s - 23ms/step - accuracy: 0.7805 - loss: 1.6697 - val_accuracy: 0.8571 - val_loss: 1.6184
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Epoch 24/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.6814 - val_accuracy: 0.8571 - val_loss: 1.6058
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Epoch 25/100
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2/2 - 0s - 29ms/step - accuracy: 0.8293 - loss: 1.6687 - val_accuracy: 0.8571 - val_loss: 1.5919
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Epoch 26/100
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2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.7052 - val_accuracy: 0.8571 - val_loss: 1.5786
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Epoch 27/100
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2/2 - 0s - 21ms/step - accuracy: 0.8293 - loss: 1.6147 - val_accuracy: 0.8571 - val_loss: 1.5663
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Epoch 28/100
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2/2 - 0s - 21ms/step - accuracy: 0.7805 - loss: 1.6207 - val_accuracy: 0.8571 - val_loss: 1.5529
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Epoch 29/100
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2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.5964 - val_accuracy: 0.8571 - val_loss: 1.5413
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Epoch 30/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.6258 - val_accuracy: 0.8571 - val_loss: 1.5294
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Epoch 31/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5512 - val_accuracy: 0.8571 - val_loss: 1.5175
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Epoch 32/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.6674 - val_accuracy: 0.8571 - val_loss: 1.5070
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Epoch 33/100
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2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.5848 - val_accuracy: 0.8571 - val_loss: 1.4977
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Epoch 34/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5467 - val_accuracy: 0.8571 - val_loss: 1.4869
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Epoch 35/100
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2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.6244 - val_accuracy: 0.8571 - val_loss: 1.4778
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Epoch 36/100
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2/2 - 0s - 24ms/step - accuracy: 0.8293 - loss: 1.5275 - val_accuracy: 0.8571 - val_loss: 1.4672
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Epoch 37/100
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2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.7105 - val_accuracy: 0.8571 - val_loss: 1.4595
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Epoch 38/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.6137 - val_accuracy: 0.8571 - val_loss: 1.4509
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Epoch 39/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5726 - val_accuracy: 0.8571 - val_loss: 1.4425
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Epoch 40/100
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2/2 - 0s - 21ms/step - accuracy: 0.8293 - loss: 1.5345 - val_accuracy: 0.8571 - val_loss: 1.4347
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Epoch 41/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5689 - val_accuracy: 0.8571 - val_loss: 1.4269
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Epoch 42/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4239 - val_accuracy: 0.8571 - val_loss: 1.4175
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Epoch 43/100
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2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.5922 - val_accuracy: 0.8571 - val_loss: 1.4099
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Epoch 44/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5006 - val_accuracy: 0.8571 - val_loss: 1.4021
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Epoch 45/100
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2/2 - 0s - 21ms/step - accuracy: 0.8049 - loss: 1.4858 - val_accuracy: 0.8571 - val_loss: 1.3944
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Epoch 46/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4769 - val_accuracy: 0.8571 - val_loss: 1.3874
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Epoch 47/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4211 - val_accuracy: 0.8571 - val_loss: 1.3796
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Epoch 48/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4060 - val_accuracy: 0.8571 - val_loss: 1.3717
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Epoch 49/100
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2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4741 - val_accuracy: 0.8571 - val_loss: 1.3652
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Epoch 50/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4989 - val_accuracy: 0.8571 - val_loss: 1.3588
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Epoch 51/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4718 - val_accuracy: 0.8571 - val_loss: 1.3521
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Epoch 52/100
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2/2 - 0s - 24ms/step - accuracy: 0.8293 - loss: 1.4712 - val_accuracy: 0.8571 - val_loss: 1.3482
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Epoch 53/100
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2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.3657 - val_accuracy: 0.8571 - val_loss: 1.3425
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Epoch 54/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3847 - val_accuracy: 0.8571 - val_loss: 1.3366
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Epoch 55/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3766 - val_accuracy: 0.8571 - val_loss: 1.3315
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Epoch 56/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4275 - val_accuracy: 0.8571 - val_loss: 1.3253
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Epoch 57/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4709 - val_accuracy: 0.8571 - val_loss: 1.3209
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Epoch 58/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4226 - val_accuracy: 0.8571 - val_loss: 1.3176
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Epoch 59/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4491 - val_accuracy: 0.8571 - val_loss: 1.3126
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Epoch 60/100
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2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.4165 - val_accuracy: 0.8571 - val_loss: 1.3073
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Epoch 61/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3914 - val_accuracy: 0.8571 - val_loss: 1.3027
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Epoch 62/100
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2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3560 - val_accuracy: 0.8571 - val_loss: 1.2974
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Epoch 63/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3432 - val_accuracy: 0.8571 - val_loss: 1.2915
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Epoch 64/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3833 - val_accuracy: 0.8571 - val_loss: 1.2866
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Epoch 65/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4477 - val_accuracy: 0.8571 - val_loss: 1.2834
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Epoch 66/100
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2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3054 - val_accuracy: 0.8571 - val_loss: 1.2794
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Epoch 67/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2840 - val_accuracy: 0.8571 - val_loss: 1.2742
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Epoch 68/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4320 - val_accuracy: 0.8571 - val_loss: 1.2703
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Epoch 69/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4165 - val_accuracy: 0.8571 - val_loss: 1.2664
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Epoch 70/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3210 - val_accuracy: 0.8571 - val_loss: 1.2617
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Epoch 71/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3698 - val_accuracy: 0.8571 - val_loss: 1.2580
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Epoch 72/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3679 - val_accuracy: 0.8571 - val_loss: 1.2546
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Epoch 73/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3647 - val_accuracy: 0.8571 - val_loss: 1.2506
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Epoch 74/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3088 - val_accuracy: 0.8571 - val_loss: 1.2465
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Epoch 75/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3304 - val_accuracy: 0.8571 - val_loss: 1.2425
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Epoch 76/100
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2/2 - 0s - 29ms/step - accuracy: 0.8537 - loss: 1.2813 - val_accuracy: 0.8571 - val_loss: 1.2389
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Epoch 77/100
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2/2 - 0s - 28ms/step - accuracy: 0.8537 - loss: 1.3477 - val_accuracy: 0.8571 - val_loss: 1.2352
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Epoch 78/100
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2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 1.3885 - val_accuracy: 0.8571 - val_loss: 1.2315
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Epoch 79/100
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2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2939 - val_accuracy: 0.8571 - val_loss: 1.2280
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Epoch 80/100
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2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 1.3508 - val_accuracy: 0.8571 - val_loss: 1.2254
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Epoch 81/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3219 - val_accuracy: 0.8571 - val_loss: 1.2223
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Epoch 82/100
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2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.3559 - val_accuracy: 0.8571 - val_loss: 1.2190
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Epoch 83/100
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2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3289 - val_accuracy: 0.8571 - val_loss: 1.2159
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Epoch 84/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1656 - val_accuracy: 0.8571 - val_loss: 1.2123
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Epoch 85/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2954 - val_accuracy: 0.8571 - val_loss: 1.2088
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Epoch 86/100
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2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2298 - val_accuracy: 0.8571 - val_loss: 1.2058
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Epoch 87/100
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2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3323 - val_accuracy: 0.8571 - val_loss: 1.2037
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Epoch 88/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3758 - val_accuracy: 0.8571 - val_loss: 1.2010
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Epoch 89/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3587 - val_accuracy: 0.8571 - val_loss: 1.1981
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Epoch 90/100
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2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3240 - val_accuracy: 0.8571 - val_loss: 1.1954
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Epoch 91/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2884 - val_accuracy: 0.8571 - val_loss: 1.1922
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Epoch 92/100
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2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3293 - val_accuracy: 0.8571 - val_loss: 1.1901
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Epoch 93/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2706 - val_accuracy: 0.8571 - val_loss: 1.1879
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Epoch 94/100
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2/2 - 0s - 27ms/step - accuracy: 0.8537 - loss: 1.2715 - val_accuracy: 0.8571 - val_loss: 1.1848
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Epoch 95/100
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2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2628 - val_accuracy: 0.8571 - val_loss: 1.1818
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Epoch 96/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2770 - val_accuracy: 0.8571 - val_loss: 1.1786
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Epoch 97/100
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2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3039 - val_accuracy: 0.8571 - val_loss: 1.1762
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Epoch 98/100
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2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 1.2908 - val_accuracy: 0.8571 - val_loss: 1.1731
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Epoch 99/100
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2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3510 - val_accuracy: 0.8571 - val_loss: 1.1707
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Epoch 100/100
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2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2447 - val_accuracy: 0.8571 - val_loss: 1.1683
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1/1 - 0s - 26ms/step - accuracy: 0.8571 - loss: 1.1683
|
||||
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|
||||
Dokładność testowa: 85.71%
|
||||
[1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step - accuracy: 0.8571 - loss: 1.1683
[1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step - accuracy: 0.8571 - loss: 1.1683
|
15
my_runs/1/info.json
Normal file
15
my_runs/1/info.json
Normal file
@ -0,0 +1,15 @@
|
||||
{
|
||||
"dropout_layer_value": 0.4,
|
||||
"num_epochs": 100,
|
||||
"training_ts": {
|
||||
"__reduce__": [
|
||||
{
|
||||
"py/type": "datetime.datetime"
|
||||
},
|
||||
[
|
||||
"B+gGCQ06DAHl8g=="
|
||||
]
|
||||
],
|
||||
"py/object": "datetime.datetime"
|
||||
}
|
||||
}
|
248
my_runs/1/metrics.json
Normal file
248
my_runs/1/metrics.json
Normal file
@ -0,0 +1,248 @@
|
||||
{
|
||||
"test accuracy": {
|
||||
"steps": [
|
||||
0
|
||||
],
|
||||
"timestamps": [
|
||||
"2024-06-09T11:58:17.715614"
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||||
],
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||||
"values": [
|
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||||
]
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||||
},
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||||
"test loss": {
|
||||
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|
||||
0
|
||||
],
|
||||
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|
||||
"2024-06-09T11:58:17.715614"
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||||
],
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||||
"values": [
|
||||
1.168319582939148
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||||
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|
||||
},
|
||||
"train accuracy": {
|
||||
"steps": [
|
||||
0
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||||
],
|
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|
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||||
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||||
"train loss": {
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
}
|
||||
}
|
BIN
my_runs/1/model.keras
Normal file
BIN
my_runs/1/model.keras
Normal file
Binary file not shown.
Loading…
Reference in New Issue
Block a user