Hello, guys, welcome back.
Now, we have seen that our accuracy was around 89, nine percent and our loss was a little bit too
high when we train this neural network.
Now, ends or not magic, that means you need to know how to parameterize your CNN in order to get it
to work on your data in order to get the highest accuracy and the lowest loss possible.
Now, it's a blackout, meaning that you really need to do a lot of trial and error with some sense
in order to get your skin and to give you better results.
Now, I'm going to teach you what kind of tricks you can do on your skin and in order to give better
results.
One of the things that I could think about is by increasing the depth of our model so far, we are we
are not really using that deep neural network.
So maybe by adding another layer, maybe things will get better and it will help our skin.
And to learn more features, especially because we have ten classes and ten classes, would require
that you have more deployers in order to get and store more features.
Another way, in order to increase the number of features stored is by increasing the number of filters
itself.
So maybe let us duplicate those filters to all of these.
So now we have 32, 64, 64 and 128.
OK, so by that we are increasing the number of filters which we will learn the new features.
Now the technique of increasing the number of filters does not really work all the time because if you
have too many filters, maybe you will be too large for the features you have.
Maybe you have only a couple of features.
So if you're CNN is way too large, you will get also the useless model that can barely learn anything.
So you need to try to optimize the number of features that your image has with the number of filters
that you can do here.
And you can do that only and mostly by trial and error, unless you want to do some really deep analysis
on your data.
For now, we are not going to learn the deepest, darkest science about deep learning.
I just want to give you a push in order to start using these models.
So this is why I'm summarizing what parameters can you tweak in order to get better results?
Well, we said we can increase the number of players with that increased number of filters.
We also did that.
Another thing is training it for a longer time.
So the approach is here.
Maybe we can jump them to around 300.
This will take more time.
And don't worry about it.
Some models requires days in order to train them, especially if you're training on a very small processor
like the Raspberry Pi processor.
Now, you could always train your model on some powerful PC and then just migrate your model to your
Raspberry Pi.
Remember this model that we were generating?
You can train it on some powerful computer and then just put it on the Raspberry Pi if the training
is taking so long.
OK, so let's say we were training for 300 approaches and we're going to increase the step approaches
to around 30, OK.
Now, by that, we should get some other results.
Now, we are going to start this in the next lecture.
I'm going to validate what happens here.
But here I have called this version, too, so that we can compare both models and see which will perform
better.
I'm going to start training this.
You can do the same, train it and let's see what's going to happen in the next lecture.