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Epoch 53/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1397 - sparse_categorical_accuracy: 0.9531 - val_loss: 0.1667 - val_sparse_categorical_accuracy: 0.9487
Epoch 54/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1205 - sparse_categorical_accuracy: 0.9601 - val_loss: 0.2904 - val_sparse_categorical_accuracy: 0.8821
Epoch 55/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1302 - sparse_categorical_accuracy: 0.9538 - val_loss: 0.9437 - val_sparse_categorical_accuracy: 0.7060
Epoch 56/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1241 - sparse_categorical_accuracy: 0.9580 - val_loss: 0.1346 - val_sparse_categorical_accuracy: 0.9501
Epoch 57/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1158 - sparse_categorical_accuracy: 0.9646 - val_loss: 0.9489 - val_sparse_categorical_accuracy: 0.6907
Epoch 58/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1175 - sparse_categorical_accuracy: 0.9573 - val_loss: 0.6089 - val_sparse_categorical_accuracy: 0.7212
Epoch 59/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1160 - sparse_categorical_accuracy: 0.9611 - val_loss: 0.1294 - val_sparse_categorical_accuracy: 0.9487
Epoch 60/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1096 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1527 - val_sparse_categorical_accuracy: 0.9417
Epoch 61/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1163 - sparse_categorical_accuracy: 0.9611 - val_loss: 0.5554 - val_sparse_categorical_accuracy: 0.7684
Epoch 62/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.2433 - val_sparse_categorical_accuracy: 0.8904
Epoch 63/500
90/90 [==============================] - 0s 6ms/step - loss: 0.1105 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.3426 - val_sparse_categorical_accuracy: 0.8571
Epoch 64/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1058 - sparse_categorical_accuracy: 0.9667 - val_loss: 2.1389 - val_sparse_categorical_accuracy: 0.5520
Epoch 65/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1037 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.3875 - val_sparse_categorical_accuracy: 0.8738
Epoch 66/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1135 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1783 - val_sparse_categorical_accuracy: 0.9459
Epoch 67/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1006 - sparse_categorical_accuracy: 0.9681 - val_loss: 0.1462 - val_sparse_categorical_accuracy: 0.9515
Epoch 68/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0994 - sparse_categorical_accuracy: 0.9684 - val_loss: 0.1140 - val_sparse_categorical_accuracy: 0.9584
Epoch 69/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.9635 - val_loss: 1.6500 - val_sparse_categorical_accuracy: 0.5589
Epoch 70/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1118 - sparse_categorical_accuracy: 0.9628 - val_loss: 1.3355 - val_sparse_categorical_accuracy: 0.6768
Epoch 71/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1155 - sparse_categorical_accuracy: 0.9608 - val_loss: 0.3167 - val_sparse_categorical_accuracy: 0.8793
Epoch 72/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1041 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1329 - val_sparse_categorical_accuracy: 0.9417
Epoch 73/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1001 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1385 - val_sparse_categorical_accuracy: 0.9417
Epoch 74/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0997 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1369 - val_sparse_categorical_accuracy: 0.9473
Epoch 75/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1051 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.5135 - val_sparse_categorical_accuracy: 0.7781
Epoch 76/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0945 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1440 - val_sparse_categorical_accuracy: 0.9556
Epoch 77/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1081 - sparse_categorical_accuracy: 0.9618 - val_loss: 0.2210 - val_sparse_categorical_accuracy: 0.9196
Epoch 78/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1109 - sparse_categorical_accuracy: 0.9618 - val_loss: 0.2181 - val_sparse_categorical_accuracy: 0.9196
Epoch 79/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1047 - sparse_categorical_accuracy: 0.9608 - val_loss: 0.2074 - val_sparse_categorical_accuracy: 0.9237
Epoch 80/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1035 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.3792 - val_sparse_categorical_accuracy: 0.8571
Epoch 81/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1040 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.7353 - val_sparse_categorical_accuracy: 0.7420
Epoch 82/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1106 - sparse_categorical_accuracy: 0.9649 - val_loss: 0.2948 - val_sparse_categorical_accuracy: 0.9140
Epoch 83/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1066 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1338 - val_sparse_categorical_accuracy: 0.9570
Epoch 84/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0988 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1095 - val_sparse_categorical_accuracy: 0.9570
Epoch 85/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1065 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1717 - val_sparse_categorical_accuracy: 0.9417
Epoch 86/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1087 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.1206 - val_sparse_categorical_accuracy: 0.9570
Epoch 87/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0991 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.4285 - val_sparse_categorical_accuracy: 0.8474
Epoch 88/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0984 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.1589 - val_sparse_categorical_accuracy: 0.9334
Epoch 89/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1023 - sparse_categorical_accuracy: 0.9701 - val_loss: 1.5442 - val_sparse_categorical_accuracy: 0.6782
Epoch 90/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0995 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1211 - val_sparse_categorical_accuracy: 0.9528
Epoch 91/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0908 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9556
Epoch 92/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0919 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.2109 - val_sparse_categorical_accuracy: 0.9140
Epoch 93/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0890 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.1509 - val_sparse_categorical_accuracy: 0.9431
Epoch 94/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0958 - sparse_categorical_accuracy: 0.9694 - val_loss: 0.1761 - val_sparse_categorical_accuracy: 0.9417
Epoch 95/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1000 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1466 - val_sparse_categorical_accuracy: 0.9293
Epoch 96/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0913 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.6963 - val_sparse_categorical_accuracy: 0.7725
Epoch 97/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0954 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.3042 - val_sparse_categorical_accuracy: 0.8738
Epoch 98/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0866 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1115 - val_sparse_categorical_accuracy: 0.9584
Epoch 99/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1017 - sparse_categorical_accuracy: 0.9615 - val_loss: 0.1195 - val_sparse_categorical_accuracy: 0.9584
Epoch 100/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1012 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1975 - val_sparse_categorical_accuracy: 0.9196
Epoch 101/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1058 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1960 - val_sparse_categorical_accuracy: 0.9487
Epoch 102/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0914 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.9598