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block which lets get this one running so the simple part is just go over there and then | |
you have the run selected cell so we select that one and run it so while it runs you would | |
so this is just a comment so if you can choose to run it but it doesnt actually do anything | |
you would be make basically classifying them into these different kinds of classes over | |
images itself and these are all color rgb color images so thats available directly within | |
the torch vision data sets so now we had imported torch vision data set over here | |
now i can go into data sets and then from there i input the cifar ten data set now the | |
point is when it imports locally so its its either imported somewhere earlier and then | |
is basically another folder which is created within my local directory so you see your | |
directory anyways because we did not upload the data set thats a huge bulky file to be | |
so if you have it already downloaded | |
purpose otherwise you need to download it from scratch so here like what it would do | |
is it just goes over there and sees that files are already downloaded and they are perfectly | |
and then within cifar ten batches it will be creating my training and test batches over | |
here ok so now once thats done so what i can do is i move back on to my main directory | |
over here and lets go to the next part of it so here what i am trying to do is get into | |
what is the length over there and then it just converts it to a string and prints it | |
training and testing data set is of ten thousand images now once thats done the next part is | |
we come down over here which is feature extraction on a single image so initially what we will | |
be doing is lets lets see what these images look like so what i am doing is i take down | |
one of these images which is at the zero , zero location so this is the first image present | |
format so that will typically be coming down as some sort of a container with me now that | |
its its really fuzzy to understand but this is basically if you like really go far off | |
what you are going to do is you would need the main image array so thats present over | |
is basically the number of points you would be taking around the central point | |
so you remember clearly from our earlier discussions on from in the last class on lbp where you | |
you would be getting eight such neighbors along that point which are at a distance separation | |
you would do now what it allows within these functions is that you can choose down any | |
number of neighbors you can choose four five six seven typically for the three cross three | |
that that would not be a uniform pixel kind of a distribution but you can interpolate | |
and go down to those kind of forms so what we choose to do is we take a circular neighborhood | |
this lbp feature on a point to point basis looks like so we compute this one and this | |
hard to actually find out whether there is a frog or something or not from so many points | |
there for for this from this histogram then that would help you to get down the energy | |
and entropy as well | |
now once you have all of these you can basically use energy and entropy as two different distinct | |
whole image needs to be represented in terms of one single scalar value and a set of those | |
multiple number of scalar values which will be your features which describe this image | |
so for that what we do is we just evaluate this part over here and i get down that lbp | |
energy of this much and lbp entropy of this much is what defines all of this together | |
present in this image ok now once that goes down the next part is to find it out on the | |
co occurrence matrix ok so in a co occurrence matrix what i need to do is i need to get | |
there is what is the orientation of your vector whether its at zero degrees forty five degree | |
number two fifty six is basically the number of gray levels you have in your gray level | |
are basically to show down how to handle down the boundary conditions present over there | |
one the first scalar value is basically to get done contrast second scalar value is to | |
in getting and this are the different measures for that one particular image now from there | |
the next one is to get into wavelets and do it so for we choose to do it with gabor filters | |
now as you remember from your gabor filtered equations in the last class so there would | |
down over there as well as what is your frequency at which you would like to operate | |
now the other part is what is the angle at which it is located and what are the variables | |
also choose to give them so you can read down with within the details more over there now | |
given that at any point you will be getting down to components of your wavelet decomposition | |
imaginary part and this is basically the consolidated magnitude response over there the next part | |
than these kind of matrix representation and they are basically your probability energy | |
now this is till now what we have done was just for one of these images which was at | |
the first location within my training data set now in order to do it for training i would | |
define some sort of a matrix which is called as the training features matrix so this is | |
a two d matrix which is the number of rows in this matrix is equal to the length of the | |
training data set the number of columns is equal to the length of features now how many | |
features we found out was basically two plus five plus two and that makes it nine features | |
which we are going to have over here now for this part what we do is we write down first | |
over the whole length of the training data set once you get over the whole length of | |
the training data set you need to find out one feature at a time now once you have one | |
feature at a time coming down you need to calculate all of these features one sorry | |
filters now once you have all of them you need to concatenate that into one row matrix | |
and then you keep on concatenating one below the other and you get your two d matrix coming | |
down so if we run this part you see this verbose commenting coming down and then it keeps on | |
running so together that would finish it off there might be certain warnings at positions | |
it over fifty thousand of those but if you look through it so its its pretty much fast | |
so tidy slow as well in the duration of where we are speaking you can already see this quite | |
going on so we just have a verbose ,nd given down over there so if you would like | |
to get rid of this part then the simple task is that you dont keep one printing this part | |
to show down how many of them are done and and then you just just need to wait till its | |
out on your test set as well | |
do a basic revision in that case so what i did was i have my pre defined precursor coming | |
the type of the data set or not but say if you are writing a full fledged code over there | |
all images in your data set now if you dont want to look into whats getting extracted | |
still keeps on running over here so lets see how far yeah it should be quite close to finishing | |
time now once your features are extracted the next part of your code is basically to | |
are extracted the next part is to go down on your test data set and also extract out | |
features and completely show it and and then eventually you can go and basically save down | |
yeah so now this is over and the next part of it is basically to get down your testing | |
out all the features is basically to get down get each feature dynamically varying within | |
to be applied within your testing set otherwise the nature of normalizations are going to | |
file and then just print it all so once this part is complete you need to get down extract | |
features for your training one and for your testing set then run the feature normalization | |
on images some basic operations using the classical way so as you start with any kind | |
you have in that big corpus of pixel space available to you now from that when we eventually | |
go down as you have seen that there are features which you have extracted out the next question | |
as what we had defined in the first few lectures was that you need to be able to relate certain | |
called as a classification problem ok | |
now in order to make it even simpler so what it would essentially mean is that if i have | |
these are all may be scalar parameters now if i arrange these scalar parameters into | |
sort of a matrix thats what we would call down as a vector or in the standard parlance | |
of our definitions we would also be calling this as a feature vector now once you have | |
that feature vector given to you how do i associate a feature vector to one single categorical | |
itself and now from that perspective here is where we start down so what todays lecture | |
neuron model and from there we will go down to ah the neural network formulation and then | |
would define what a neuron is so as in a neural network you would always have a neuron |
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