Hey there. If you've been following along with
some of my articles, you may have seen that
I really enjoyed playing around with machine
learning and deep learning. What brought me
into initially was all the fun things that
I wanted to use for my GPU and maybe,
like, push harder a little bit further, have
some more fun discussions about that. But
what kept me in here and kept me interested
in the entire thing is all the things I was
able to rather easily do that I thought were
well beyond my resources and my means.
So I was very excited when Fast.ai a group
I've been working with for this had put out
a new course that starting in October, well,
I kind of fell far behind because my little
boy was born. Finally, though, I've been able
to set some sent time aside and been able
to work forward to some of the coursework.
And already I have some really interesting
results within the five or six hours that
I've been playing around with it. Let's go
take a look at these notebooks that I will be sharing
the fast.ai part that shows different
links to everything you can learn, the data sets,
and everything along those lines. A
part of lesson one is they going out choosing
data sets. So I chose that Kvasir. I
don't know if I said that right. That is that
where it kind of goes through some diseases
in the G.I. tract and gives images for this.
So if you scroll down, you could see the different
landmarks about it and everything along those
lines and go all the way to the bottom. You
start seeing some of these downloads, so the
different versions have different number of
images in it. But it's exactly what I'm looking
for. So in this case, I start off with the
four thousand image set has eight different
things you're looking for and five hundred
images and each of these classes, and then
you can go up to the next set, which is eight
thousand set. And then there is nothing with
the folds where twenty thousand image set,
which it gets kind of crazy out there. So
so how would we go about it with Fast.ai. So hopefully you are able to get to the
point with the different videos where you're
able to set up a Jupyter notebook and be able
to see the different set set ups fpr ot. Here's
my set up. I just basically took the
fast.ai one and then added in my own code right
there. So looking into the data set, what
did we see from this? After setting all the
data up the correct way, it did take me a
little bit of time to pull in the right places
because they had it set up slightly differently
and then different phrases on it. I went and
did first one, and then I went almost immediately
into the second one. Looking through this,
you see the eight different classes where
yeah, that dyed left polyps, the Normal
cecum, um, the normal Z line. All those everything's
were able to put out some these images showing
that it's actually working and everything
is doing exactly what's supposed to be doing
and farther on down. We actually get to some
of the training since we're looking at images
were going to be using a CNN, our convolutional
neural network. So these are particularly
good for images and especially the dataset
we are looking at right here. So in this, because
fast.ai is simple. We are able to take
our data, the model we're going to using his
resident, which is a very fantastic one that
works really quickly, and that's why we want
to use it. And then the metrics. We're just
going for the error rate and could see from this
initial look, we get to about thirteen percent
before we start doing some of this some more
fine to me tuning. So we're able to look down
and we could see what we saw wrong. Where
there's like some, this is normalcy come.
But there is what I predicted with my model,
but it's actually popped a name. You can actually
see someone in there and then you can see
that similar thing like normals. Pylorus
When I really love about this is you have
this confusion matrix. So you're able to see
exactly where some of these things fit in
so we can see where the trouble spot are,
where there are some problems with that. So
I look in particular up here where the dyed
images. Obviously that makes sense because
I can't tell with the human eye differently
on. And then the other one is the ah esophagitis versus the normal scene lines. So
normal verse that so these are the two little
problems spots. All the other things are fairly
easy and were caught. We were able to do after
another, another set of iterations we were able
to actually bring this down a lot lower. So
we're at where was I? About ten percent. So
he pulled off another three percent. Ninety
percent of these images are correct, um, and
what they're predictions are toe what it actually
is going down farther. So we kind of reached
the end Resnet 34 were with
up to Resnet 50 which is a more robust
model to be looking at it. But it also takes
longer. So we know since we know thirty four
works, let's spend some time on fifty. So
we do the saying a lot of the same things
except for there's ah, lot of transforms going
on. So I'm moving the image around a lot,
and it basically chose just about everything
especially since we an endoscope. Um, the
rotation has a full three sixty. I was able
to rotate the image almost any way you look
at it so that you can get some good stuff
out of it, and they're still going to be useful
and still what you see in the real world,
I'm going to clean some of that code up. But
on So you could see what here, Now we're getting
down to a like a nine percent like that. That's
pretty impressive on after some of these rounds
and then even down to a seven percent. I think
I'm over fitting at that point in particular
the problems we're having. So we've almost
solved the dyed aspect of it, so we don't
have nearly as many false is on there. However,
the actually, this esophagitis versus the
normals in line is getting worse. So additional
things to think about. So I tried doing even
more iterations to make it even better and
it doesn't really help any. We've kind
of reached the max of that, So I need to take
a look back on how I could look. Especially
so for the esophagitis versus the normals
in line. If there's some interesting things
with that. The other thing I started to realize
when looking through this is I'm not doing
a multiple classifier. I'm saying there is
this or not, so there's probably some of these
where they actually have two or three of the
same ones. That's why it's confusing
the model and the different predictions. So
now, being that my next step with him, I did
try to take this a little bit further with
the and it's still running. So it's why everything
is running slowly with the eighty thousand
images. So that was just the twenty thousand
set we walk through and it's still working
on it. But you can see with Resnet34 up here, supposedly, I had one hundred
percent accuracy that can't happen. There's
something drastically wrong if I'm getting
one hundred percent of it right. So that's
why I think there's something wrong with how
I interpreted the data. And so I need to go
back and take a look at that. I'm probably
going to wait until I do another few courses
with fast. Hopefully, that's a great little
explanation of what I was able to do in my
first week of working on these different data
sets. I am excited because they did find a
fraud data set that I wanna take a look at
We're looking to NLP and some of those
things and hopefully we'll be able to get
that out on just a delayed schedule for you.
Thanks for washing. Have a good one.
