# What can be the best dropout value and the FC layer for good accurate predictions?

I am retraining the pre-trained model VGG16 in the last FC layers. I used the below function .

what can be the best combination of FC layers and dropout values for the best predictions. ?

In addition to rithwik kukunuri, this thing is called hyperparameter optimization or hyperparameter tuning. As the name suggests, there are a bunch of variables comes out of nowhere. We just decide intuitively (or based on some educated guess).
Some of them are how many layers you need to use?, size of conv layer, dropout, learning rate, activation, kernel size, number of epoch etc. After trying many different combinations of these and multiple iterations for the same set of variables, you can end up with significantly higher accuracy.