Can anyone explain these two graphs? Is it some kind of overfitting or not?
I'm using HAM10000 dataset for multi-classification with EfficientNetB0 (TensorFlow - Keras). These are the results:
Epoch 1/100
271/271 [==============================] - 90s 247ms/step - loss: 15.8665 - accuracy: 0.3449 - val_loss: 13.1980 - val_accuracy: 0.6562
Epoch 2/100
271/271 [==============================] - 58s 214ms/step - loss: 12.1447 - accuracy: 0.4657 - val_loss: 10.0858 - val_accuracy: 0.6758
Epoch 3/100
271/271 [==============================] - 60s 221ms/step - loss: 9.1016 - accuracy: 0.5254 - val_loss: 7.5525 - val_accuracy: 0.6510
Epoch 4/100
271/271 [==============================] - 57s 209ms/step - loss: 6.7974 - accuracy: 0.5718 - val_loss: 5.6905 - val_accuracy: 0.6562
Epoch 5/100
271/271 [==============================] - 57s 210ms/step - loss: 5.2379 - accuracy: 0.6022 - val_loss: 4.5189 - val_accuracy: 0.6771
Epoch 6/100
271/271 [==============================] - 57s 211ms/step - loss: 4.2807 - accuracy: 0.6372 - val_loss: 3.7868 - val_accuracy: 0.7096
Epoch 7/100
271/271 [==============================] - 64s 236ms/step - loss: 3.7094 - accuracy: 0.6439 - val_loss: 3.4062 - val_accuracy: 0.6745
Epoch 8/100
271/271 [==============================] - 59s 217ms/step - loss: 3.3640 - accuracy: 0.6574 - val_loss: 3.1371 - val_accuracy: 0.7044
Epoch 9/100
271/271 [==============================] - 58s 212ms/step - loss: 3.1725 - accuracy: 0.6660 - val_loss: 3.0400 - val_accuracy: 0.6927
Epoch 10/100
271/271 [==============================] - 58s 215ms/step - loss: 3.0643 - accuracy: 0.6675 - val_loss: 2.9752 - val_accuracy: 0.6615
Epoch 11/100
271/271 [==============================] - 58s 214ms/step - loss: 2.9608 - accuracy: 0.6808 - val_loss: 3.0125 - val_accuracy: 0.6576
Epoch 12/100
271/271 [==============================] - 58s 214ms/step - loss: 2.9182 - accuracy: 0.6932 - val_loss: 2.8904 - val_accuracy: 0.6888
Epoch 13/100
271/271 [==============================] - 57s 210ms/step - loss: 2.9217 - accuracy: 0.6838 - val_loss: 2.9863 - val_accuracy: 0.6133
Epoch 14/100
271/271 [==============================] - 58s 213ms/step - loss: 2.9204 - accuracy: 0.6884 - val_loss: 2.9623 - val_accuracy: 0.6615
Epoch 15/100
271/271 [==============================] - 60s 220ms/step - loss: 2.9028 - accuracy: 0.7006 - val_loss: 2.9699 - val_accuracy: 0.6693
Epoch 16/100
271/271 [==============================] - 58s 214ms/step - loss: 2.9146 - accuracy: 0.7043 - val_loss: 2.9384 - val_accuracy: 0.6953
Epoch 17/100
271/271 [==============================] - 58s 213ms/step - loss: 2.9191 - accuracy: 0.7013 - val_loss: 3.0042 - val_accuracy: 0.6562
Epoch 18/100
271/271 [==============================] - 57s 211ms/step - loss: 2.8990 - accuracy: 0.7131 - val_loss: 3.0141 - val_accuracy: 0.6289
Epoch 19/100
271/271 [==============================] - 59s 217ms/step - loss: 2.9273 - accuracy: 0.7126 - val_loss: 3.0148 - val_accuracy: 0.6276
Epoch 20/100
271/271 [==============================] - 58s 214ms/step - loss: 2.9268 - accuracy: 0.7095 - val_loss: 2.9905 - val_accuracy: 0.6836
Epoch 21/100
271/271 [==============================] - 57s 211ms/step - loss: 2.9329 - accuracy: 0.7152 - val_loss: 2.9601 - val_accuracy: 0.6745
Epoch 22/100
271/271 [==============================] - 57s 210ms/step - loss: 2.9515 - accuracy: 0.7050 - val_loss: 2.9472 - val_accuracy: 0.6836
Epoch 23/100
271/271 [==============================] - 57s 212ms/step - loss: 2.9301 - accuracy: 0.7209 - val_loss: 2.9428 - val_accuracy: 0.6888
Epoch 24/100
271/271 [==============================] - 63s 233ms/step - loss: 2.9532 - accuracy: 0.7184 - val_loss: 2.9262 - val_accuracy: 0.7174
Epoch 25/100
271/271 [==============================] - 58s 214ms/step - loss: 2.9278 - accuracy: 0.7296 - val_loss: 3.0052 - val_accuracy: 0.6693
Epoch 26/100
271/271 [==============================] - 62s 227ms/step - loss: 2.9252 - accuracy: 0.7250 - val_loss: 2.9451 - val_accuracy: 0.6979
Epoch 27/100
271/271 [==============================] - 59s 218ms/step - loss: 2.9511 - accuracy: 0.7155 - val_loss: 2.9671 - val_accuracy: 0.7083
Epoch 28/100
271/271 [==============================] - 58s 213ms/step - loss: 2.9318 - accuracy: 0.7237 - val_loss: 2.9785 - val_accuracy: 0.6914
Epoch 29/100
271/271 [==============================] - 57s 210ms/step - loss: 2.9225 - accuracy: 0.7223 - val_loss: 3.1599 - val_accuracy: 0.6185
Epoch 30/100
271/271 [==============================] - 57s 212ms/step - loss: 2.9411 - accuracy: 0.7250 - val_loss: 2.9453 - val_accuracy: 0.7057
Epoch 31/100
271/271 [==============================] - 63s 231ms/step - loss: 2.9477 - accuracy: 0.7221 - val_loss: 3.0061 - val_accuracy: 0.6888
Epoch 32/100
271/271 [==============================] - 58s 214ms/step - loss: 2.9596 - accuracy: 0.7230 - val_loss: 3.0180 - val_accuracy: 0.6654
Epoch 32: early stopping
Execution time of the program is- 2073.7081892490387
Understand the results and overcome the problem if there is any.