HenryAI/KerasCodeExamples.txt · Datasets at Hugging Face

text stringlengths 0 4.99k
dropout_8 (Dropout) (None, 128) 0 dense[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 2) 258 dropout_8[0][0]
==================================================================================================
Total params: 93,130
Trainable params: 93,130
Non-trainable params: 0
__________________________________________________________________________________________________
Epoch 1/200
45/45 [==============================] - 26s 499ms/step - loss: 1.0233 - sparse_categorical_accuracy: 0.5174 - val_loss: 0.7853 - val_sparse_categorical_accuracy: 0.5368
Epoch 2/200
45/45 [==============================] - 22s 499ms/step - loss: 0.9108 - sparse_categorical_accuracy: 0.5507 - val_loss: 0.7169 - val_sparse_categorical_accuracy: 0.5659
Epoch 3/200
45/45 [==============================] - 23s 509ms/step - loss: 0.8177 - sparse_categorical_accuracy: 0.5851 - val_loss: 0.6851 - val_sparse_categorical_accuracy: 0.5839
Epoch 4/200
45/45 [==============================] - 24s 532ms/step - loss: 0.7494 - sparse_categorical_accuracy: 0.6160 - val_loss: 0.6554 - val_sparse_categorical_accuracy: 0.6214
Epoch 5/200
45/45 [==============================] - 23s 520ms/step - loss: 0.7287 - sparse_categorical_accuracy: 0.6319 - val_loss: 0.6333 - val_sparse_categorical_accuracy: 0.6463
Epoch 6/200
45/45 [==============================] - 23s 509ms/step - loss: 0.7108 - sparse_categorical_accuracy: 0.6424 - val_loss: 0.6185 - val_sparse_categorical_accuracy: 0.6546
Epoch 7/200
45/45 [==============================] - 23s 512ms/step - loss: 0.6624 - sparse_categorical_accuracy: 0.6667 - val_loss: 0.6023 - val_sparse_categorical_accuracy: 0.6657
Epoch 8/200
45/45 [==============================] - 23s 518ms/step - loss: 0.6392 - sparse_categorical_accuracy: 0.6774 - val_loss: 0.5935 - val_sparse_categorical_accuracy: 0.6796
Epoch 9/200
45/45 [==============================] - 23s 513ms/step - loss: 0.5978 - sparse_categorical_accuracy: 0.6955 - val_loss: 0.5778 - val_sparse_categorical_accuracy: 0.6907
Epoch 10/200
45/45 [==============================] - 23s 511ms/step - loss: 0.5909 - sparse_categorical_accuracy: 0.6948 - val_loss: 0.5687 - val_sparse_categorical_accuracy: 0.6935
Epoch 11/200
45/45 [==============================] - 23s 513ms/step - loss: 0.5785 - sparse_categorical_accuracy: 0.7021 - val_loss: 0.5628 - val_sparse_categorical_accuracy: 0.6990
Epoch 12/200
45/45 [==============================] - 23s 514ms/step - loss: 0.5547 - sparse_categorical_accuracy: 0.7247 - val_loss: 0.5545 - val_sparse_categorical_accuracy: 0.7101
Epoch 13/200
45/45 [==============================] - 24s 535ms/step - loss: 0.5705 - sparse_categorical_accuracy: 0.7240 - val_loss: 0.5461 - val_sparse_categorical_accuracy: 0.7240
Epoch 14/200
45/45 [==============================] - 23s 517ms/step - loss: 0.5538 - sparse_categorical_accuracy: 0.7250 - val_loss: 0.5403 - val_sparse_categorical_accuracy: 0.7212
Epoch 15/200
45/45 [==============================] - 23s 515ms/step - loss: 0.5144 - sparse_categorical_accuracy: 0.7500 - val_loss: 0.5318 - val_sparse_categorical_accuracy: 0.7295
Epoch 16/200
45/45 [==============================] - 23s 512ms/step - loss: 0.5200 - sparse_categorical_accuracy: 0.7521 - val_loss: 0.5286 - val_sparse_categorical_accuracy: 0.7379
Epoch 17/200
45/45 [==============================] - 23s 515ms/step - loss: 0.4910 - sparse_categorical_accuracy: 0.7590 - val_loss: 0.5229 - val_sparse_categorical_accuracy: 0.7393
Epoch 18/200
45/45 [==============================] - 23s 514ms/step - loss: 0.5013 - sparse_categorical_accuracy: 0.7427 - val_loss: 0.5157 - val_sparse_categorical_accuracy: 0.7462
Epoch 19/200
45/45 [==============================] - 23s 511ms/step - loss: 0.4883 - sparse_categorical_accuracy: 0.7712 - val_loss: 0.5123 - val_sparse_categorical_accuracy: 0.7490
Epoch 20/200
45/45 [==============================] - 23s 514ms/step - loss: 0.4935 - sparse_categorical_accuracy: 0.7667 - val_loss: 0.5032 - val_sparse_categorical_accuracy: 0.7545
Epoch 21/200
45/45 [==============================] - 23s 514ms/step - loss: 0.4551 - sparse_categorical_accuracy: 0.7799 - val_loss: 0.4978 - val_sparse_categorical_accuracy: 0.7573
Epoch 22/200
45/45 [==============================] - 23s 516ms/step - loss: 0.4477 - sparse_categorical_accuracy: 0.7948 - val_loss: 0.4941 - val_sparse_categorical_accuracy: 0.7531
Epoch 23/200
45/45 [==============================] - 23s 518ms/step - loss: 0.4549 - sparse_categorical_accuracy: 0.7858 - val_loss: 0.4893 - val_sparse_categorical_accuracy: 0.7656
Epoch 24/200
45/45 [==============================] - 23s 516ms/step - loss: 0.4426 - sparse_categorical_accuracy: 0.7948 - val_loss: 0.4842 - val_sparse_categorical_accuracy: 0.7712
Epoch 25/200
45/45 [==============================] - 23s 520ms/step - loss: 0.4360 - sparse_categorical_accuracy: 0.8035 - val_loss: 0.4798 - val_sparse_categorical_accuracy: 0.7809
Epoch 26/200
45/45 [==============================] - 23s 515ms/step - loss: 0.4316 - sparse_categorical_accuracy: 0.8035 - val_loss: 0.4715 - val_sparse_categorical_accuracy: 0.7809
Epoch 27/200
45/45 [==============================] - 23s 518ms/step - loss: 0.4084 - sparse_categorical_accuracy: 0.8146 - val_loss: 0.4676 - val_sparse_categorical_accuracy: 0.7878
Epoch 28/200
45/45 [==============================] - 23s 515ms/step - loss: 0.3998 - sparse_categorical_accuracy: 0.8240 - val_loss: 0.4667 - val_sparse_categorical_accuracy: 0.7933
Epoch 29/200
45/45 [==============================] - 23s 514ms/step - loss: 0.3993 - sparse_categorical_accuracy: 0.8198 - val_loss: 0.4603 - val_sparse_categorical_accuracy: 0.7892
Epoch 30/200
45/45 [==============================] - 23s 515ms/step - loss: 0.4031 - sparse_categorical_accuracy: 0.8243 - val_loss: 0.4562 - val_sparse_categorical_accuracy: 0.7920
Epoch 31/200
45/45 [==============================] - 23s 511ms/step - loss: 0.3891 - sparse_categorical_accuracy: 0.8184 - val_loss: 0.4528 - val_sparse_categorical_accuracy: 0.7920
Epoch 32/200
45/45 [==============================] - 23s 516ms/step - loss: 0.3922 - sparse_categorical_accuracy: 0.8292 - val_loss: 0.4485 - val_sparse_categorical_accuracy: 0.7892
Epoch 33/200
45/45 [==============================] - 23s 516ms/step - loss: 0.3802 - sparse_categorical_accuracy: 0.8309 - val_loss: 0.4463 - val_sparse_categorical_accuracy: 0.8003
Epoch 34/200
45/45 [==============================] - 23s 514ms/step - loss: 0.3711 - sparse_categorical_accuracy: 0.8372 - val_loss: 0.4427 - val_sparse_categorical_accuracy: 0.7975
Epoch 35/200
45/45 [==============================] - 23s 512ms/step - loss: 0.3744 - sparse_categorical_accuracy: 0.8378 - val_loss: 0.4366 - val_sparse_categorical_accuracy: 0.8072
Epoch 36/200
45/45 [==============================] - 23s 511ms/step - loss: 0.3653 - sparse_categorical_accuracy: 0.8372 - val_loss: 0.4338 - val_sparse_categorical_accuracy: 0.8072
Epoch 37/200
45/45 [==============================] - 23s 512ms/step - loss: 0.3681 - sparse_categorical_accuracy: 0.8382 - val_loss: 0.4337 - val_sparse_categorical_accuracy: 0.8058
Epoch 38/200
45/45 [==============================] - 23s 512ms/step - loss: 0.3634 - sparse_categorical_accuracy: 0.8514 - val_loss: 0.4264 - val_sparse_categorical_accuracy: 0.8128
Epoch 39/200
45/45 [==============================] - 23s 512ms/step - loss: 0.3498 - sparse_categorical_accuracy: 0.8535 - val_loss: 0.4211 - val_sparse_categorical_accuracy: 0.8225
Epoch 40/200
45/45 [==============================] - 23s 514ms/step - loss: 0.3358 - sparse_categorical_accuracy: 0.8663 - val_loss: 0.4161 - val_sparse_categorical_accuracy: 0.8197
Epoch 41/200
45/45 [==============================] - 23s 512ms/step - loss: 0.3448 - sparse_categorical_accuracy: 0.8573 - val_loss: 0.4161 - val_sparse_categorical_accuracy: 0.8169
Epoch 42/200
45/45 [==============================] - 23s 512ms/step - loss: 0.3439 - sparse_categorical_accuracy: 0.8552 - val_loss: 0.4119 - val_sparse_categorical_accuracy: 0.8211
Epoch 43/200
45/45 [==============================] - 23s 510ms/step - loss: 0.3335 - sparse_categorical_accuracy: 0.8660 - val_loss: 0.4101 - val_sparse_categorical_accuracy: 0.8266
Epoch 44/200
45/45 [==============================] - 23s 510ms/step - loss: 0.3235 - sparse_categorical_accuracy: 0.8660 - val_loss: 0.4067 - val_sparse_categorical_accuracy: 0.8294
Epoch 45/200
45/45 [==============================] - 23s 510ms/step - loss: 0.3273 - sparse_categorical_accuracy: 0.8656 - val_loss: 0.4033 - val_sparse_categorical_accuracy: 0.8350
Epoch 46/200
45/45 [==============================] - 23s 513ms/step - loss: 0.3277 - sparse_categorical_accuracy: 0.8608 - val_loss: 0.3994 - val_sparse_categorical_accuracy: 0.8336

dropout_8 (Dropout) (None, 128) 0 dense[0][0]

__________________________________________________________________________________________________

dense_1 (Dense) (None, 2) 258 dropout_8[0][0]

==================================================================================================

Total params: 93,130

Trainable params: 93,130

Non-trainable params: 0

__________________________________________________________________________________________________

Epoch 1/200

45/45 [==============================] - 26s 499ms/step - loss: 1.0233 - sparse_categorical_accuracy: 0.5174 - val_loss: 0.7853 - val_sparse_categorical_accuracy: 0.5368

Epoch 2/200

45/45 [==============================] - 22s 499ms/step - loss: 0.9108 - sparse_categorical_accuracy: 0.5507 - val_loss: 0.7169 - val_sparse_categorical_accuracy: 0.5659

Epoch 3/200

45/45 [==============================] - 23s 509ms/step - loss: 0.8177 - sparse_categorical_accuracy: 0.5851 - val_loss: 0.6851 - val_sparse_categorical_accuracy: 0.5839

Epoch 4/200

45/45 [==============================] - 24s 532ms/step - loss: 0.7494 - sparse_categorical_accuracy: 0.6160 - val_loss: 0.6554 - val_sparse_categorical_accuracy: 0.6214

Epoch 5/200

45/45 [==============================] - 23s 520ms/step - loss: 0.7287 - sparse_categorical_accuracy: 0.6319 - val_loss: 0.6333 - val_sparse_categorical_accuracy: 0.6463

Epoch 6/200

45/45 [==============================] - 23s 509ms/step - loss: 0.7108 - sparse_categorical_accuracy: 0.6424 - val_loss: 0.6185 - val_sparse_categorical_accuracy: 0.6546

Epoch 7/200

45/45 [==============================] - 23s 512ms/step - loss: 0.6624 - sparse_categorical_accuracy: 0.6667 - val_loss: 0.6023 - val_sparse_categorical_accuracy: 0.6657

Epoch 8/200

45/45 [==============================] - 23s 518ms/step - loss: 0.6392 - sparse_categorical_accuracy: 0.6774 - val_loss: 0.5935 - val_sparse_categorical_accuracy: 0.6796

Epoch 9/200

45/45 [==============================] - 23s 513ms/step - loss: 0.5978 - sparse_categorical_accuracy: 0.6955 - val_loss: 0.5778 - val_sparse_categorical_accuracy: 0.6907

Epoch 10/200

45/45 [==============================] - 23s 511ms/step - loss: 0.5909 - sparse_categorical_accuracy: 0.6948 - val_loss: 0.5687 - val_sparse_categorical_accuracy: 0.6935

Epoch 11/200

45/45 [==============================] - 23s 513ms/step - loss: 0.5785 - sparse_categorical_accuracy: 0.7021 - val_loss: 0.5628 - val_sparse_categorical_accuracy: 0.6990

Epoch 12/200

45/45 [==============================] - 23s 514ms/step - loss: 0.5547 - sparse_categorical_accuracy: 0.7247 - val_loss: 0.5545 - val_sparse_categorical_accuracy: 0.7101

Epoch 13/200

45/45 [==============================] - 24s 535ms/step - loss: 0.5705 - sparse_categorical_accuracy: 0.7240 - val_loss: 0.5461 - val_sparse_categorical_accuracy: 0.7240

Epoch 14/200

45/45 [==============================] - 23s 517ms/step - loss: 0.5538 - sparse_categorical_accuracy: 0.7250 - val_loss: 0.5403 - val_sparse_categorical_accuracy: 0.7212

Epoch 15/200

45/45 [==============================] - 23s 515ms/step - loss: 0.5144 - sparse_categorical_accuracy: 0.7500 - val_loss: 0.5318 - val_sparse_categorical_accuracy: 0.7295

Epoch 16/200

45/45 [==============================] - 23s 512ms/step - loss: 0.5200 - sparse_categorical_accuracy: 0.7521 - val_loss: 0.5286 - val_sparse_categorical_accuracy: 0.7379

Epoch 17/200

45/45 [==============================] - 23s 515ms/step - loss: 0.4910 - sparse_categorical_accuracy: 0.7590 - val_loss: 0.5229 - val_sparse_categorical_accuracy: 0.7393

Epoch 18/200

45/45 [==============================] - 23s 514ms/step - loss: 0.5013 - sparse_categorical_accuracy: 0.7427 - val_loss: 0.5157 - val_sparse_categorical_accuracy: 0.7462

Epoch 19/200

45/45 [==============================] - 23s 511ms/step - loss: 0.4883 - sparse_categorical_accuracy: 0.7712 - val_loss: 0.5123 - val_sparse_categorical_accuracy: 0.7490

Epoch 20/200

45/45 [==============================] - 23s 514ms/step - loss: 0.4935 - sparse_categorical_accuracy: 0.7667 - val_loss: 0.5032 - val_sparse_categorical_accuracy: 0.7545

Epoch 21/200

45/45 [==============================] - 23s 514ms/step - loss: 0.4551 - sparse_categorical_accuracy: 0.7799 - val_loss: 0.4978 - val_sparse_categorical_accuracy: 0.7573

Epoch 22/200

45/45 [==============================] - 23s 516ms/step - loss: 0.4477 - sparse_categorical_accuracy: 0.7948 - val_loss: 0.4941 - val_sparse_categorical_accuracy: 0.7531

Epoch 23/200

45/45 [==============================] - 23s 518ms/step - loss: 0.4549 - sparse_categorical_accuracy: 0.7858 - val_loss: 0.4893 - val_sparse_categorical_accuracy: 0.7656

Epoch 24/200

45/45 [==============================] - 23s 516ms/step - loss: 0.4426 - sparse_categorical_accuracy: 0.7948 - val_loss: 0.4842 - val_sparse_categorical_accuracy: 0.7712

Epoch 25/200

45/45 [==============================] - 23s 520ms/step - loss: 0.4360 - sparse_categorical_accuracy: 0.8035 - val_loss: 0.4798 - val_sparse_categorical_accuracy: 0.7809

Epoch 26/200

45/45 [==============================] - 23s 515ms/step - loss: 0.4316 - sparse_categorical_accuracy: 0.8035 - val_loss: 0.4715 - val_sparse_categorical_accuracy: 0.7809

Epoch 27/200

45/45 [==============================] - 23s 518ms/step - loss: 0.4084 - sparse_categorical_accuracy: 0.8146 - val_loss: 0.4676 - val_sparse_categorical_accuracy: 0.7878

Epoch 28/200

45/45 [==============================] - 23s 515ms/step - loss: 0.3998 - sparse_categorical_accuracy: 0.8240 - val_loss: 0.4667 - val_sparse_categorical_accuracy: 0.7933

Epoch 29/200

45/45 [==============================] - 23s 514ms/step - loss: 0.3993 - sparse_categorical_accuracy: 0.8198 - val_loss: 0.4603 - val_sparse_categorical_accuracy: 0.7892

Epoch 30/200

45/45 [==============================] - 23s 515ms/step - loss: 0.4031 - sparse_categorical_accuracy: 0.8243 - val_loss: 0.4562 - val_sparse_categorical_accuracy: 0.7920

Epoch 31/200

45/45 [==============================] - 23s 511ms/step - loss: 0.3891 - sparse_categorical_accuracy: 0.8184 - val_loss: 0.4528 - val_sparse_categorical_accuracy: 0.7920

Epoch 32/200

45/45 [==============================] - 23s 516ms/step - loss: 0.3922 - sparse_categorical_accuracy: 0.8292 - val_loss: 0.4485 - val_sparse_categorical_accuracy: 0.7892

Epoch 33/200

45/45 [==============================] - 23s 516ms/step - loss: 0.3802 - sparse_categorical_accuracy: 0.8309 - val_loss: 0.4463 - val_sparse_categorical_accuracy: 0.8003

Epoch 34/200

45/45 [==============================] - 23s 514ms/step - loss: 0.3711 - sparse_categorical_accuracy: 0.8372 - val_loss: 0.4427 - val_sparse_categorical_accuracy: 0.7975

Epoch 35/200

45/45 [==============================] - 23s 512ms/step - loss: 0.3744 - sparse_categorical_accuracy: 0.8378 - val_loss: 0.4366 - val_sparse_categorical_accuracy: 0.8072

Epoch 36/200

45/45 [==============================] - 23s 511ms/step - loss: 0.3653 - sparse_categorical_accuracy: 0.8372 - val_loss: 0.4338 - val_sparse_categorical_accuracy: 0.8072

Epoch 37/200

45/45 [==============================] - 23s 512ms/step - loss: 0.3681 - sparse_categorical_accuracy: 0.8382 - val_loss: 0.4337 - val_sparse_categorical_accuracy: 0.8058

Epoch 38/200

45/45 [==============================] - 23s 512ms/step - loss: 0.3634 - sparse_categorical_accuracy: 0.8514 - val_loss: 0.4264 - val_sparse_categorical_accuracy: 0.8128

Epoch 39/200

45/45 [==============================] - 23s 512ms/step - loss: 0.3498 - sparse_categorical_accuracy: 0.8535 - val_loss: 0.4211 - val_sparse_categorical_accuracy: 0.8225

Epoch 40/200

45/45 [==============================] - 23s 514ms/step - loss: 0.3358 - sparse_categorical_accuracy: 0.8663 - val_loss: 0.4161 - val_sparse_categorical_accuracy: 0.8197

Epoch 41/200

45/45 [==============================] - 23s 512ms/step - loss: 0.3448 - sparse_categorical_accuracy: 0.8573 - val_loss: 0.4161 - val_sparse_categorical_accuracy: 0.8169

Epoch 42/200

45/45 [==============================] - 23s 512ms/step - loss: 0.3439 - sparse_categorical_accuracy: 0.8552 - val_loss: 0.4119 - val_sparse_categorical_accuracy: 0.8211

Epoch 43/200

45/45 [==============================] - 23s 510ms/step - loss: 0.3335 - sparse_categorical_accuracy: 0.8660 - val_loss: 0.4101 - val_sparse_categorical_accuracy: 0.8266

Epoch 44/200

45/45 [==============================] - 23s 510ms/step - loss: 0.3235 - sparse_categorical_accuracy: 0.8660 - val_loss: 0.4067 - val_sparse_categorical_accuracy: 0.8294

Epoch 45/200

45/45 [==============================] - 23s 510ms/step - loss: 0.3273 - sparse_categorical_accuracy: 0.8656 - val_loss: 0.4033 - val_sparse_categorical_accuracy: 0.8350

Epoch 46/200

45/45 [==============================] - 23s 513ms/step - loss: 0.3277 - sparse_categorical_accuracy: 0.8608 - val_loss: 0.3994 - val_sparse_categorical_accuracy: 0.8336