Raw frequencies array (ordered by class):
[34105  4362 26569  9368   199]

Raw weights array:
[ 0.4374901  3.4205868  0.5615793  1.5927199 74.97789  ]
Epoch 1/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 135s 92ms/step - loss: 0.9108 - sparse_categorical_accuracy: 0.5829 - val_loss: 0.6861 - val_sparse_categorical_accuracy: 0.7025
Epoch 2/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 142s 129ms/step - loss: 0.5572 - sparse_categorical_accuracy: 0.7406 - val_loss: 0.4683 - val_sparse_categorical_accuracy: 0.7920
Epoch 3/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 100s 91ms/step - loss: 0.4462 - sparse_categorical_accuracy: 0.7908 - val_loss: 0.4253 - val_sparse_categorical_accuracy: 0.8121
Epoch 4/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 100s 91ms/step - loss: 0.3660 - sparse_categorical_accuracy: 0.8208 - val_loss: 0.3973 - val_sparse_categorical_accuracy: 0.8155
Epoch 5/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 100s 90ms/step - loss: 0.3229 - sparse_categorical_accuracy: 0.8513 - val_loss: 0.3405 - val_sparse_categorical_accuracy: 0.8643
Epoch 6/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 142s 129ms/step - loss: 0.3298 - sparse_categorical_accuracy: 0.8681 - val_loss: 0.3088 - val_sparse_categorical_accuracy: 0.8877
Epoch 7/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 100s 90ms/step - loss: 0.2420 - sparse_categorical_accuracy: 0.8927 - val_loss: 0.3203 - val_sparse_categorical_accuracy: 0.8496
Epoch 8/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 142s 129ms/step - loss: 0.2247 - sparse_categorical_accuracy: 0.9011 - val_loss: 0.3009 - val_sparse_categorical_accuracy: 0.8993
Epoch 9/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 100s 91ms/step - loss: 0.2241 - sparse_categorical_accuracy: 0.9136 - val_loss: 0.2440 - val_sparse_categorical_accuracy: 0.9027
Epoch 10/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 142s 128ms/step - loss: 0.2065 - sparse_categorical_accuracy: 0.9193 - val_loss: 0.2334 - val_sparse_categorical_accuracy: 0.9276
Epoch 11/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 141s 128ms/step - loss: 0.1478 - sparse_categorical_accuracy: 0.9409 - val_loss: 0.2660 - val_sparse_categorical_accuracy: 0.9310
Epoch 12/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 100s 90ms/step - loss: 0.1329 - sparse_categorical_accuracy: 0.9468 - val_loss: 0.1872 - val_sparse_categorical_accuracy: 0.9480
Epoch 13/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 141s 128ms/step - loss: 0.1316 - sparse_categorical_accuracy: 0.9505 - val_loss: 0.1954 - val_sparse_categorical_accuracy: 0.9363
Epoch 14/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 100s 91ms/step - loss: 0.1376 - sparse_categorical_accuracy: 0.9562 - val_loss: 0.1497 - val_sparse_categorical_accuracy: 0.9449
Epoch 15/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 141s 128ms/step - loss: 0.0912 - sparse_categorical_accuracy: 0.9673 - val_loss: 0.2812 - val_sparse_categorical_accuracy: 0.9177
Epoch 16/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 142s 129ms/step - loss: 0.0918 - sparse_categorical_accuracy: 0.9643 - val_loss: 0.2329 - val_sparse_categorical_accuracy: 0.9345
Epoch 17/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 142s 128ms/step - loss: 0.1475 - sparse_categorical_accuracy: 0.9473 - val_loss: 0.1590 - val_sparse_categorical_accuracy: 0.9712
Epoch 18/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 142s 128ms/step - loss: 0.1117 - sparse_categorical_accuracy: 0.9713 - val_loss: 0.1577 - val_sparse_categorical_accuracy: 0.9654
Epoch 19/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 142s 128ms/step - loss: 0.0799 - sparse_categorical_accuracy: 0.9762 - val_loss: 0.1777 - val_sparse_categorical_accuracy: 0.9663
Epoch 20/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 142s 128ms/step - loss: 0.0706 - sparse_categorical_accuracy: 0.9761 - val_loss: 0.1992 - val_sparse_categorical_accuracy: 0.9558
Epoch 21/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 100s 90ms/step - loss: 0.0910 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.2057 - val_sparse_categorical_accuracy: 0.9340
Epoch 22/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 99s 90ms/step - loss: 0.0794 - sparse_categorical_accuracy: 0.9673 - val_loss: 0.1595 - val_sparse_categorical_accuracy: 0.9582
Epoch 23/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 142s 128ms/step - loss: 0.0652 - sparse_categorical_accuracy: 0.9770 - val_loss: 0.1500 - val_sparse_categorical_accuracy: 0.9691
Epoch 24/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 143s 129ms/step - loss: 0.0790 - sparse_categorical_accuracy: 0.9768 - val_loss: 0.1299 - val_sparse_categorical_accuracy: 0.9776
Epoch 25/561
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 0s 82ms/step - loss: 0.0715 - sparse_categorical_accuracy: 0.9832
Accuracy alcanzada (0.9834 >= 0.98). Deteniendo entrenamiento.
1105/1105 ━━━━━━━━━━━━━━━━━━━━ 100s 90ms/step - loss: 0.0715 - sparse_categorical_accuracy: 0.9832 - val_loss: 0.1411 - val_sparse_categorical_accuracy: 0.9667
350/350 ━━━━━━━━━━━━━━━━━━━━ 13s 26ms/step - loss: 0.0873 - sparse_categorical_accuracy: 0.9823
Test Loss: 0.0856
Test Accuracy (Compound): 0.9820

Classification Report
              precision    recall  f1-score   support

     Class 0       0.99      0.99      0.99      5086
     Class 1       0.96      0.97      0.96       660
     Class 2       0.98      0.98      0.98      4027
     Class 3       0.97      0.98      0.98      1397
     Class 4       0.43      0.90      0.58        21

    accuracy                           0.98     11191
   macro avg       0.87      0.96      0.90     11191
weighted avg       0.98      0.98      0.98     11191

Mean Absolute Error (MAE):  0.0334
Root Mean Squared Error (RMSE): 0.2694

Top-2 Accuracy: 0.9985
Top-3 Accuracy: 0.9996

Expected Calibration Error (ECE): 0.0039