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4.99k
validation_split=0.1,
callbacks=[checkpoint_callback],
)
model.load_weights(checkpoint_filepath)
_, accuracy, top_5_accuracy = model.evaluate(x_test, y_test)
print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")
print(f\"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%\")
return history
vit_classifier = create_vit_classifier()
history = run_experiment(vit_classifier)
Epoch 1/100
176/176 [==============================] - 33s 136ms/step - loss: 4.8863 - accuracy: 0.0294 - top-5-accuracy: 0.1117 - val_loss: 3.9661 - val_accuracy: 0.0992 - val_top-5-accuracy: 0.3056
Epoch 2/100
176/176 [==============================] - 22s 127ms/step - loss: 4.0162 - accuracy: 0.0865 - top-5-accuracy: 0.2683 - val_loss: 3.5691 - val_accuracy: 0.1630 - val_top-5-accuracy: 0.4226
Epoch 3/100
176/176 [==============================] - 22s 127ms/step - loss: 3.7313 - accuracy: 0.1254 - top-5-accuracy: 0.3535 - val_loss: 3.3455 - val_accuracy: 0.1976 - val_top-5-accuracy: 0.4756
Epoch 4/100
176/176 [==============================] - 23s 128ms/step - loss: 3.5411 - accuracy: 0.1541 - top-5-accuracy: 0.4121 - val_loss: 3.1925 - val_accuracy: 0.2274 - val_top-5-accuracy: 0.5126
Epoch 5/100
176/176 [==============================] - 22s 127ms/step - loss: 3.3749 - accuracy: 0.1847 - top-5-accuracy: 0.4572 - val_loss: 3.1043 - val_accuracy: 0.2388 - val_top-5-accuracy: 0.5320
Epoch 6/100
176/176 [==============================] - 22s 127ms/step - loss: 3.2589 - accuracy: 0.2057 - top-5-accuracy: 0.4906 - val_loss: 2.9319 - val_accuracy: 0.2782 - val_top-5-accuracy: 0.5756
Epoch 7/100
176/176 [==============================] - 22s 127ms/step - loss: 3.1165 - accuracy: 0.2331 - top-5-accuracy: 0.5273 - val_loss: 2.8072 - val_accuracy: 0.2972 - val_top-5-accuracy: 0.5946
Epoch 8/100
176/176 [==============================] - 22s 127ms/step - loss: 2.9902 - accuracy: 0.2563 - top-5-accuracy: 0.5556 - val_loss: 2.7207 - val_accuracy: 0.3188 - val_top-5-accuracy: 0.6258
Epoch 9/100
176/176 [==============================] - 22s 127ms/step - loss: 2.8828 - accuracy: 0.2800 - top-5-accuracy: 0.5827 - val_loss: 2.6396 - val_accuracy: 0.3244 - val_top-5-accuracy: 0.6402
Epoch 10/100
176/176 [==============================] - 23s 128ms/step - loss: 2.7824 - accuracy: 0.2997 - top-5-accuracy: 0.6110 - val_loss: 2.5580 - val_accuracy: 0.3494 - val_top-5-accuracy: 0.6568
Epoch 11/100
176/176 [==============================] - 23s 130ms/step - loss: 2.6743 - accuracy: 0.3209 - top-5-accuracy: 0.6333 - val_loss: 2.5000 - val_accuracy: 0.3594 - val_top-5-accuracy: 0.6726
Epoch 12/100
176/176 [==============================] - 23s 130ms/step - loss: 2.5800 - accuracy: 0.3431 - top-5-accuracy: 0.6522 - val_loss: 2.3900 - val_accuracy: 0.3798 - val_top-5-accuracy: 0.6878
Epoch 13/100
176/176 [==============================] - 23s 128ms/step - loss: 2.5019 - accuracy: 0.3559 - top-5-accuracy: 0.6671 - val_loss: 2.3464 - val_accuracy: 0.3960 - val_top-5-accuracy: 0.7002
Epoch 14/100
176/176 [==============================] - 22s 128ms/step - loss: 2.4207 - accuracy: 0.3728 - top-5-accuracy: 0.6905 - val_loss: 2.3130 - val_accuracy: 0.4032 - val_top-5-accuracy: 0.7040
Epoch 15/100
176/176 [==============================] - 23s 128ms/step - loss: 2.3371 - accuracy: 0.3932 - top-5-accuracy: 0.7093 - val_loss: 2.2447 - val_accuracy: 0.4136 - val_top-5-accuracy: 0.7202
Epoch 16/100
176/176 [==============================] - 23s 128ms/step - loss: 2.2650 - accuracy: 0.4077 - top-5-accuracy: 0.7201 - val_loss: 2.2101 - val_accuracy: 0.4222 - val_top-5-accuracy: 0.7246
Epoch 17/100
176/176 [==============================] - 22s 127ms/step - loss: 2.1822 - accuracy: 0.4204 - top-5-accuracy: 0.7376 - val_loss: 2.1446 - val_accuracy: 0.4344 - val_top-5-accuracy: 0.7416
Epoch 18/100
176/176 [==============================] - 22s 128ms/step - loss: 2.1485 - accuracy: 0.4284 - top-5-accuracy: 0.7476 - val_loss: 2.1094 - val_accuracy: 0.4432 - val_top-5-accuracy: 0.7454
Epoch 19/100
176/176 [==============================] - 22s 128ms/step - loss: 2.0717 - accuracy: 0.4464 - top-5-accuracy: 0.7618 - val_loss: 2.0718 - val_accuracy: 0.4584 - val_top-5-accuracy: 0.7570
Epoch 20/100
176/176 [==============================] - 22s 127ms/step - loss: 2.0031 - accuracy: 0.4605 - top-5-accuracy: 0.7731 - val_loss: 2.0286 - val_accuracy: 0.4610 - val_top-5-accuracy: 0.7654
Epoch 21/100
176/176 [==============================] - 22s 127ms/step - loss: 1.9650 - accuracy: 0.4700 - top-5-accuracy: 0.7820 - val_loss: 2.0225 - val_accuracy: 0.4642 - val_top-5-accuracy: 0.7628
Epoch 22/100
176/176 [==============================] - 22s 127ms/step - loss: 1.9066 - accuracy: 0.4839 - top-5-accuracy: 0.7904 - val_loss: 1.9961 - val_accuracy: 0.4746 - val_top-5-accuracy: 0.7656
Epoch 23/100
176/176 [==============================] - 22s 127ms/step - loss: 1.8564 - accuracy: 0.4952 - top-5-accuracy: 0.8030 - val_loss: 1.9769 - val_accuracy: 0.4828 - val_top-5-accuracy: 0.7742
Epoch 24/100
176/176 [==============================] - 22s 128ms/step - loss: 1.8167 - accuracy: 0.5034 - top-5-accuracy: 0.8099 - val_loss: 1.9730 - val_accuracy: 0.4766 - val_top-5-accuracy: 0.7728
Epoch 25/100
176/176 [==============================] - 22s 128ms/step - loss: 1.7788 - accuracy: 0.5124 - top-5-accuracy: 0.8174 - val_loss: 1.9187 - val_accuracy: 0.4926 - val_top-5-accuracy: 0.7854
Epoch 26/100
176/176 [==============================] - 23s 128ms/step - loss: 1.7437 - accuracy: 0.5187 - top-5-accuracy: 0.8206 - val_loss: 1.9732 - val_accuracy: 0.4792 - val_top-5-accuracy: 0.7772
Epoch 27/100
176/176 [==============================] - 23s 128ms/step - loss: 1.6929 - accuracy: 0.5300 - top-5-accuracy: 0.8287 - val_loss: 1.9109 - val_accuracy: 0.4928 - val_top-5-accuracy: 0.7912
Epoch 28/100
176/176 [==============================] - 23s 129ms/step - loss: 1.6647 - accuracy: 0.5400 - top-5-accuracy: 0.8362 - val_loss: 1.9031 - val_accuracy: 0.4984 - val_top-5-accuracy: 0.7824
Epoch 29/100
176/176 [==============================] - 23s 129ms/step - loss: 1.6295 - accuracy: 0.5488 - top-5-accuracy: 0.8402 - val_loss: 1.8744 - val_accuracy: 0.4982 - val_top-5-accuracy: 0.7910
Epoch 30/100
176/176 [==============================] - 22s 128ms/step - loss: 1.5860 - accuracy: 0.5548 - top-5-accuracy: 0.8504 - val_loss: 1.8551 - val_accuracy: 0.5108 - val_top-5-accuracy: 0.7946
Epoch 31/100
176/176 [==============================] - 22s 127ms/step - loss: 1.5666 - accuracy: 0.5614 - top-5-accuracy: 0.8548 - val_loss: 1.8720 - val_accuracy: 0.5076 - val_top-5-accuracy: 0.7960
Epoch 32/100
176/176 [==============================] - 22s 127ms/step - loss: 1.5272 - accuracy: 0.5712 - top-5-accuracy: 0.8596 - val_loss: 1.8840 - val_accuracy: 0.5106 - val_top-5-accuracy: 0.7966
Epoch 33/100
176/176 [==============================] - 22s 128ms/step - loss: 1.4995 - accuracy: 0.5779 - top-5-accuracy: 0.8651 - val_loss: 1.8660 - val_accuracy: 0.5116 - val_top-5-accuracy: 0.7904
Epoch 34/100
176/176 [==============================] - 22s 128ms/step - loss: 1.4686 - accuracy: 0.5849 - top-5-accuracy: 0.8685 - val_loss: 1.8544 - val_accuracy: 0.5126 - val_top-5-accuracy: 0.7954
Epoch 35/100
176/176 [==============================] - 22s 127ms/step - loss: 1.4276 - accuracy: 0.5992 - top-5-accuracy: 0.8743 - val_loss: 1.8497 - val_accuracy: 0.5164 - val_top-5-accuracy: 0.7990
Epoch 36/100
176/176 [==============================] - 22s 127ms/step - loss: 1.4102 - accuracy: 0.5970 - top-5-accuracy: 0.8768 - val_loss: 1.8496 - val_accuracy: 0.5198 - val_top-5-accuracy: 0.7948
Epoch 37/100
176/176 [==============================] - 22s 126ms/step - loss: 1.3800 - accuracy: 0.6112 - top-5-accuracy: 0.8814 - val_loss: 1.8033 - val_accuracy: 0.5284 - val_top-5-accuracy: 0.8068
Epoch 38/100
176/176 [==============================] - 22s 126ms/step - loss: 1.3500 - accuracy: 0.6103 - top-5-accuracy: 0.8862 - val_loss: 1.8092 - val_accuracy: 0.5214 - val_top-5-accuracy: 0.8128
Epoch 39/100
176/176 [==============================] - 22s 127ms/step - loss: 1.3575 - accuracy: 0.6127 - top-5-accuracy: 0.8857 - val_loss: 1.8175 - val_accuracy: 0.5198 - val_top-5-accuracy: 0.8086
Epoch 40/100
176/176 [==============================] - 22s 126ms/step - loss: 1.3030 - accuracy: 0.6283 - top-5-accuracy: 0.8927 - val_loss: 1.8361 - val_accuracy: 0.5170 - val_top-5-accuracy: 0.8056
Epoch 41/100
176/176 [==============================] - 22s 125ms/step - loss: 1.3160 - accuracy: 0.6247 - top-5-accuracy: 0.8923 - val_loss: 1.8074 - val_accuracy: 0.5260 - val_top-5-accuracy: 0.8082
Epoch 42/100
176/176 [==============================] - 22s 126ms/step - loss: 1.2679 - accuracy: 0.6329 - top-5-accuracy: 0.9002 - val_loss: 1.8430 - val_accuracy: 0.5244 - val_top-5-accuracy: 0.8100
Epoch 43/100
176/176 [==============================] - 22s 126ms/step - loss: 1.2514 - accuracy: 0.6375 - top-5-accuracy: 0.9034 - val_loss: 1.8318 - val_accuracy: 0.5196 - val_top-5-accuracy: 0.8034