HenryAI/KerasCodeExamples.txt · Datasets at Hugging Face

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Epoch 303/500
90/90 [==============================] - 0s 5ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1003 - val_sparse_categorical_accuracy: 0.9612
Epoch 304/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0538 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9612
Epoch 305/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0528 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9626
Epoch 306/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0949 - val_sparse_categorical_accuracy: 0.9612
Epoch 307/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0534 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.0902 - val_sparse_categorical_accuracy: 0.9639
Epoch 308/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0929 - val_sparse_categorical_accuracy: 0.9681
Epoch 309/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0904 - val_sparse_categorical_accuracy: 0.9626
Epoch 310/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0518 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9598
Epoch 311/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0945 - val_sparse_categorical_accuracy: 0.9626
Epoch 312/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0586 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0957 - val_sparse_categorical_accuracy: 0.9626
Epoch 313/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0470 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0984 - val_sparse_categorical_accuracy: 0.9598
Epoch 314/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0533 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9598
Epoch 315/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9639
Epoch 316/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0912 - val_sparse_categorical_accuracy: 0.9639
Epoch 317/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0515 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9626
Epoch 318/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0522 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0916 - val_sparse_categorical_accuracy: 0.9639
Epoch 319/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0919 - val_sparse_categorical_accuracy: 0.9639
Epoch 320/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0446 - sparse_categorical_accuracy: 0.9906 - val_loss: 0.0901 - val_sparse_categorical_accuracy: 0.9626
Epoch 321/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0910 - val_sparse_categorical_accuracy: 0.9598
Epoch 322/500
90/90 [==============================] - 0s 6ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1029 - val_sparse_categorical_accuracy: 0.9598
Epoch 323/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0505 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0939 - val_sparse_categorical_accuracy: 0.9626
Epoch 324/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0505 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0900 - val_sparse_categorical_accuracy: 0.9612
Epoch 325/500
90/90 [==============================] - 0s 6ms/step - loss: 0.0516 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1024 - val_sparse_categorical_accuracy: 0.9626
Epoch 326/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0512 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0946 - val_sparse_categorical_accuracy: 0.9598
Epoch 327/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0509 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.0988 - val_sparse_categorical_accuracy: 0.9626
Epoch 328/500
90/90 [==============================] - 0s 5ms/step - loss: 0.0427 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9639
Epoch 329/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0515 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0962 - val_sparse_categorical_accuracy: 0.9612
Epoch 330/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0917 - val_sparse_categorical_accuracy: 0.9598
Epoch 331/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0911 - val_sparse_categorical_accuracy: 0.9626
Epoch 332/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0479 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9612
Epoch 333/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.0877 - val_sparse_categorical_accuracy: 0.9639
Epoch 334/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0500 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1073 - val_sparse_categorical_accuracy: 0.9626
Epoch 335/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9612
Epoch 336/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1075 - val_sparse_categorical_accuracy: 0.9639
Epoch 337/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0953 - val_sparse_categorical_accuracy: 0.9626
Epoch 338/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0904 - val_sparse_categorical_accuracy: 0.9639
Epoch 339/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9584
Epoch 340/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0512 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9626
Epoch 341/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0468 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9626
Epoch 342/500
90/90 [==============================] - 0s 5ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0950 - val_sparse_categorical_accuracy: 0.9653
Epoch 343/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0518 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0937 - val_sparse_categorical_accuracy: 0.9598
Epoch 344/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0958 - val_sparse_categorical_accuracy: 0.9639
Epoch 345/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1467 - val_sparse_categorical_accuracy: 0.9515
Epoch 346/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0482 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0917 - val_sparse_categorical_accuracy: 0.9667
Epoch 347/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1134 - val_sparse_categorical_accuracy: 0.9626
Epoch 348/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9612
Epoch 349/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0462 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.0898 - val_sparse_categorical_accuracy: 0.9667
Epoch 350/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0912 - val_sparse_categorical_accuracy: 0.9639
Epoch 351/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0462 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0932 - val_sparse_categorical_accuracy: 0.9626
Epoch 352/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0515 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9653

Epoch 303/500

90/90 [==============================] - 0s 5ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1003 - val_sparse_categorical_accuracy: 0.9612

Epoch 304/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0538 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9612

Epoch 305/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0528 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9626

Epoch 306/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0949 - val_sparse_categorical_accuracy: 0.9612

Epoch 307/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0534 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.0902 - val_sparse_categorical_accuracy: 0.9639

Epoch 308/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0929 - val_sparse_categorical_accuracy: 0.9681

Epoch 309/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0904 - val_sparse_categorical_accuracy: 0.9626

Epoch 310/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0518 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9598

Epoch 311/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0945 - val_sparse_categorical_accuracy: 0.9626

Epoch 312/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0586 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0957 - val_sparse_categorical_accuracy: 0.9626

Epoch 313/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0470 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0984 - val_sparse_categorical_accuracy: 0.9598

Epoch 314/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0533 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9598

Epoch 315/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9639

Epoch 316/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0912 - val_sparse_categorical_accuracy: 0.9639

Epoch 317/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0515 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9626

Epoch 318/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0522 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0916 - val_sparse_categorical_accuracy: 0.9639

Epoch 319/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0919 - val_sparse_categorical_accuracy: 0.9639

Epoch 320/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0446 - sparse_categorical_accuracy: 0.9906 - val_loss: 0.0901 - val_sparse_categorical_accuracy: 0.9626

Epoch 321/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0910 - val_sparse_categorical_accuracy: 0.9598

Epoch 322/500

90/90 [==============================] - 0s 6ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1029 - val_sparse_categorical_accuracy: 0.9598

Epoch 323/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0505 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0939 - val_sparse_categorical_accuracy: 0.9626

Epoch 324/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0505 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0900 - val_sparse_categorical_accuracy: 0.9612

Epoch 325/500

90/90 [==============================] - 0s 6ms/step - loss: 0.0516 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1024 - val_sparse_categorical_accuracy: 0.9626

Epoch 326/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0512 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0946 - val_sparse_categorical_accuracy: 0.9598

Epoch 327/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0509 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.0988 - val_sparse_categorical_accuracy: 0.9626

Epoch 328/500

90/90 [==============================] - 0s 5ms/step - loss: 0.0427 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9639

Epoch 329/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0515 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0962 - val_sparse_categorical_accuracy: 0.9612

Epoch 330/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0917 - val_sparse_categorical_accuracy: 0.9598

Epoch 331/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0911 - val_sparse_categorical_accuracy: 0.9626

Epoch 332/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0479 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9612

Epoch 333/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.0877 - val_sparse_categorical_accuracy: 0.9639

Epoch 334/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0500 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1073 - val_sparse_categorical_accuracy: 0.9626

Epoch 335/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9612

Epoch 336/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1075 - val_sparse_categorical_accuracy: 0.9639

Epoch 337/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0953 - val_sparse_categorical_accuracy: 0.9626

Epoch 338/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0904 - val_sparse_categorical_accuracy: 0.9639

Epoch 339/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9584

Epoch 340/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0512 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9626

Epoch 341/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0468 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9626

Epoch 342/500

90/90 [==============================] - 0s 5ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0950 - val_sparse_categorical_accuracy: 0.9653

Epoch 343/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0518 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0937 - val_sparse_categorical_accuracy: 0.9598

Epoch 344/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0958 - val_sparse_categorical_accuracy: 0.9639

Epoch 345/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1467 - val_sparse_categorical_accuracy: 0.9515

Epoch 346/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0482 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0917 - val_sparse_categorical_accuracy: 0.9667

Epoch 347/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1134 - val_sparse_categorical_accuracy: 0.9626

Epoch 348/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9612

Epoch 349/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0462 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.0898 - val_sparse_categorical_accuracy: 0.9667

Epoch 350/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0912 - val_sparse_categorical_accuracy: 0.9639

Epoch 351/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0462 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0932 - val_sparse_categorical_accuracy: 0.9626

Epoch 352/500

90/90 [==============================] - 1s 6ms/step - loss: 0.0515 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9653