Validation accuracy increasing but validation loss is also increasing

I am using a CNN network to classify images into 5 classes. The size of my dataset is around 370K. I am using Adam optimizer with learning rate 0.0001 and batch size of 32. Surprisingly, I am getting improvement in validation accuracy over the epochs but validation loss is constantly growing.

I am assuming that the model is becoming less and less unsure about the validation set but the accuracy is more because the value of softmax output is more than the threshold value.

What can be the reason behind this? Any help in this regard would be highly appreciated.

1 Answer
1

I think it’s tending to over fitting. The change of val_loss is so small that it may not immediately change the performance of the model(val_acc). You can try to imagine a linearly separable problem separated by a line.

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