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Tensorflow Input Pipeline | tf Dataset | Deep Learning Tutorial 44 (Tensorflow, Keras & Python) | codebasics | YouTubeToText
YouTube Transcript: Tensorflow Input Pipeline | tf Dataset | Deep Learning Tutorial 44 (Tensorflow, Keras & Python)
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Core Theme
The TensorFlow input pipeline, built using the tf.data API, is a crucial tool for efficiently handling large datasets and applying necessary transformations for deep learning model training.
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What's up boys and girls? Are you using tensorflow for your deep learning
project? Do you know about tensorflow input pipeline?
Well tensorflow input pipeline is very important it offers so many
benefits. So in this video we are going to look at some of the benefits of
tensorflow input pipeline will do coding and then in the end we'll have an
exercise.
Let's get started! Let's say you are
building your typical cats and dogs
image classification model. These images
are obviously stored on hard disk and you
need to load these images into RAM into some kind of numpy array or pandas
data frame. You have to convert these images into
numbers because machine learning model understand numbers they don't understand
images. So now you have loaded them into lesson Numpy
x train y train and you give it to your model for training
things are looking fine when you have thousand images
what if you have 10 million images in in deep learning environment you know
typically you have a lot of data and when you're running this on your
computer which has only eight gigabyte of RAM,
When you try to load it you know what your computer is going to tell you?
It will be like too much data buddy, I cannot handle it!
please help me! Alright so one approach
of tackling this issue is how about we load these
images into batches this is called a streaming approach
so batch one is thousand images you load this into
some kind of spatial data structures by the way
this table is not your Numpy array or pandas
data frame it is some kind of spatial data structure
and we'll talk about what that data structure is you load thousand images
you give batch one for your model training then
you do batch two, batch three, batch four and so on and
things work perfectly okay so now you'll ask me what is that
spatial data structure? well that spatial data structure is
tf.data.dataset and this is what helps you build your
tensorflow input pipeline. Tensorflow input pipeline in order to
build this you need to use tf.data api framework and
tf.data.dataset is the main class in this framework
all right what if I have some blurry images, you know?
I don't want to directly load the images and do my model training because you all
know we have to do data cleaning data
transformation scaling, things like that. tf dataset
fortunately has a lot of good apis to support the transformation so for
example here the red row is that blurry image and you can
use filter function you can say dot filter
and this filter underscore func is a function
custom function defined by you where you will detect if the image is
blurry or not we are not going to go into details on
how exactly you detect the blurry image but you get the point you can have a
custom filter function which you can supply to tf data set
and it will filter it out you see the red row
is gone now in this particular instance of this data structure
and then you can do model training. You might want to do more transformation
where you all know typically when you are when
you're training your image data set you want to scale it.
So all these values by the way that you're seeing I don't know if you
noticed but there are three dimensional arrays you
know rgb so an image is presented by rgb
channels and these values are from 0 to 255
and it's a usual practice that we scale this by dividing it by 255
so now you can do dot map and then define a lambda function
if you are aware about python lambda function it's a
simple function which will do x divided by
255 on each of these values so you can see that
34 divided by 255 is 0.13 70 divided by 255 is 0.27
I took all the four to do this math so all these values are correct you can
verify, okay? Alright and then you can do your model
training so overall you can use tf dataset
to do filtering mapping shuffling lot of different transformations now
what if I can write all this transformation in a single line of code
yes single line of code you want to see it how it looks
this is how it looks so I'll explain you don't be afraid okay?
this is a single line of code this is forming your complete data input
pipeline so the first step list image list files
that will load the images from your hard disk
into memory. Okay? then you do dot map so dot map is like you know pandas
dot apply function where you want to run some transformation on
your images so I have just loaded these images from
hard disk I would probably want to convert it into
Numpy array and then do some transformation by the
way Numpy array is internally it's inside your tf data set so tf data
set is kind of you know providing
abstractions over it so you essentially your Numpy array is
converted to a tensor you know and the tensor is
an underlying data structure for tf data set.
now you converted these images into array extracted label from the folder
and then the next step would be filtering blurry images.
then you do mapping. So mapping is just
your scaling you know bringing values from zero to one
and that is your tf data set. So that first step is called building data
pipeline. In this pipeline you perform etl
extract transform load all kind of transformation. I just showed you few
transformation you can do repeat you can do batching you can do so
many transformation we'll look at some of those in our coding
which is part two of this video but
you get an idea that you build a data input pipeline
Look at the code look at the beauty single line of code
and then the second step would be training the model
where you supply tf dataset in your model.fit
until now if you've seen my previous videos we would use
either Numpy array or Pandas data frame as an
input of fit function but now we'll be using tf
dataset okay? You can load text files spreadsheet.
It's not just images you can load any kind of data you can
load images from s3 you know from cloud it doesn't have to be your local
hard disk and you can use this data input pipeline
for doing batch loading, shuffling, filtering
mapping and all of this is called etl extract
transform load and in the end what you get is your tf data
set which you can directly feed into your
tensorflow model so just to summarize the
tensorflow input pipeline offers two big benefits
first you can handle huge data sets easily
by streaming them from either disk or s3 or any other cloud
storage and second benefit is you can apply
various type of transformation which you typically need to train your deep
learning model all right so that was a theory let's
begin coding now. You should go through this page it has
useful information on tf.data api framework
little code snippets etc so we are going to practice all of this
in today's coding session. So I have imported tensorflow as
tf and I'm going to create now a simple tensorflow dataset object
okay let's say you have a daily sales number
something like this these are your daily sales number twenty one thousand dollar
twenty two thousand dollar you have some data errors as well see
negative. Daily sales number can't be negative so these are all data errors
and you want to build atf data set out of it.
So you can use this api see here in the documentation
they've shown this is how you build a simple tf data set from
a python list. Okay, so here I'm going to say that this
is my tf data set let me increase the font size a little bit
okay and I will print tf data set.
I need to execute yeah so you can see that it created
an object now if you want to know the content
you can just iterate through it so you can say for sales
in tf data set print sales you know an individual
element here is a tensor and if you want to convert
this tensor into a Numpy object you can do
it by calling a Numpy function. So see you got all your sales number here this
looks fairly simple. If you don't want to do
dot numpy in your for loop you can use as numpy iterator so as
Numpy iterator and that way you don't have to write
this you'll get the same output so here
it needs to be a function. Okay so you will
iterate tf data set either directly or using as Numpy iterator.
Let's say you have your data set is 10000 elements you want to just
look at first three elements so there is a
function called take you know so if you do
take three, let's say, I want to print only
first three elements so I can do it so by using this
take function now as I mentioned before the sales numbers
can't be negative so you have to filter those
numbers you know when you're building your data pipeline you will
get rid of invalid data points so how can we filter these
data points. So the way you can do that
is using filter function so you can do tf data set dot
filter and here supply your filter function. So your filter function could
be a simple lambda where you can say x has to be greater
than zero. Okay? And that will return another data
set so you can do data set is equal to this and
then once again I just iterate through it. So you see now I don't see any negative
values here. So this filter function is quite
convenient now these numbers are in us dollars
let's say i'm doing some data analysis in indian market I need to convert these
numbers into indian currency and one dollar is equal to 72 rupees
so I want to apply this transformation I want to
multiply all the elements in this data set
with 72 and the way you do that is using map function.
So map function will take each individual element
and it will apply that particular function so you can do
Lambda x. So x will be each individual element
and you are saying multiply by that by 72. And you can do.
Alright! I'll just save this here and then again i will print those
numbers
you see you multiplied everything by 72 so
you formed a pipeline where you filter invalid elements you did currency
conversion and so on. You can also
shuffle these elements you know sometimes especially when you are doing
like image data analysis etc you want to randomly shuffle these
elements. So I can say shuffle and shuffle expects an argument
called a buffer and if you want to first let me show you
how this works. So let's say I have a buffer of size 2.
I will show you how shuffle works and this shuffle will randomly shuffle the
elements. let's say I have shuffle of three. So you see
it just randomly rearrange all these elements now if you want to know
what is that argument? What is this three? Then
you need to look at this very useful stack overflow
post. So see when you have a buffer size of three
and let's say these this is your data set one to six.
What it will do is first it will create a window of three elements it will pick
a random element from it. So let's say random element is two
then in the remaining elements it will add one more so now
your remaining element is c2 is gone so now you have one three and now you add
four now from one three four you pick another
random element okay let's say it's one so you keep on
doing that so I will provide a link of this very useful
stack overflow post thank you vlad for posting this answer.
And you can clearly understand you know it's very simple so you are taking a
buffer and from that buffer you are taking a random element.
You can also do batching you know last video we talked about
batching the training samples and distributing them
on multi-gpu environment. So similarly in the data set you can create batches
like this you can do tf dataset dot batch. Let's say I want to
create a batch of size two. So now
see if I don't do batching okay see if I remove this
then it will I trade through these elements
one by one but when I do batch it will create a batch of size two see
if I do batch three batch of size three four batch of size
four and so on. This batching concept is useful
especially if you are running in multi gpu environment where you want to
distribute these batches on different gpus for your
training purpose. Now how can I do all of these operations
in one single line? In the presentation we
saw that it's possible to do everything in a one
single line. So let me just create my data set once
again so I'm creating the data set once again
and I will do tf.dataset dot okay dot what did we do first well first we
did filter. Okay? So you do filter
and you want to filter out any negative numbers
then you did dot map where you converted US dollar to Indian rupees and then
you did shuffle. Okay so you shuffled it. Let's say
using buffer two whatever this buffer is something you can it's a free
parameter and then you did batch of two
okay so see you can chain all these operations
and in the end you get a new data set and as usual when you iterate through
that same data set you will get the whole result in one shot.
Okay I'm getting some error because I think I have two lambda functions here
so I need to just replace this x with y let me do that
see so whatever I did previously like in probably a few steps right so
one two three four. I combine all of that in single line and this is
what your tensorflow input pipeline is so tensorflow input pipeline is
reading the data from your data source then you are doing filtering
mapping, shuffling, batching all kind of transformations
now we are going to load some images from hard disks you know
let's say I have this images directory where I have cats images and docs images
so I have downloaded a couple of cats
images using this useful extension called
Fatkun batch download. So if you add this to your chrome you can
do Google search on images and you can download all the images in bulk.
So I downloaded these images from Google so I have some cat images I have some
dog images and I'm going to show you how you
can use tensorflow input pipeline to read these
images and apply various transformations okay?
So let me just go into full screen mode here
and first thing is reading those images so
you can use this function called
tf.data.dataset.list files okay? So list files what it will do
is you can supply images so I have image star dot star
right? So see I have images and then images have a directory
and directory has those actual images so I'm listing all
these files here and I'm going to say shuffle is equal to
false you can say shuffle equal to true if you want to
randomly read them and this I will store into images
data set and then I will go through this data set.
I will just print maybe first three file paths and see how it looks
so when you run this, it's now reading those images. So basically it
actually stored the image paths it has not
yet read the images. So you got all these image paths okay here in your image data
set I printed only for three elements first
five you can print however many element that you want to
print. Now I want to shuffle this if I did software
equal to true here it would have shuffled it already but let's say if you
want to do it inside your tensorflow pipeline then you can just do
simple images.ds shuffle 200 is your buffer size okay and if you want to
again know more about buffer just read that stack overflow article
so you see like now I have dog, cat it's it's like randomly arranged.
okay now the class names that I have are cat and dog. So I'm just going to create
a list and create those class names and then I'm
going to divide these images into training and test images now.
If you have used sk learn you would use test trend split function
but in tensorflow the function to do this split
is take basically. Okay so I will say okay first of all let me know what is
my image count so image count let me do this so
image count is length of this images database okay and when I
print my image count comes to be 130 and my training size
is let's say image count into 0.8 and I want this to be a whole
number so ofcourse so 80 of samples are my training size so
then my training data set is nothing but images
ds dot take. So take function will take
the first eighty percent of images as your training data set.
Okay so train size and my test ds
is skip so when you do skip, skip is a an opposite function of take it will
skip first 80 percent samples so now you're
left with remaining 80 and images are shuffled already so you
don't have to worry about the order here. So now I got my training test data set
if I do length of training data set it is this
and length of test data set is this again the purpose of this whole tutorial
is to give you an idea of tensorflow input pipeline
you will be using that while training tensorflow
deep learning models so here we are not in this video we are not doing any
training we are just building the pipeline so
that you get an idea on the api now what I got was
image path okay from image path I need to retrieve the label so
in classification problem you will have the image
and then you have you will have corresponding label which is dog or a
cat so how can you retrieve the label from
the string so let's see that see if I have a string like this
and if I want to retrieve a liv this middle portion get a dog. How do I do
that? Well just think about it okay it's a simple python split
question so you can just say do split and
a split will give you this array okay and you can go
from backward you can say see this one is minus 1
and this one is -2 so if I do minus 2 I get the
label so we're going to write a function that can get
a label from your image so you have a file path
and then you will take uh that file path and you will split and
you will get this minus 2 right? Like something like
this.
So this is your label. correct? Okay now I'm going to
call this label on on what on my images data set, right?
So let's see where is my image data set
so I have trained yes you can use a function called map so
again if you go to the documentation here you can read
about all these functions like map and such
but I'll just quickly show you
so what map will do is it will apply this
get label function on all the elements in training ds
okay so if you look at training ds right so
for t in train ds dot take
let's say four element printing dot Numpy so training
trend ds has nothing but the set of image file paths.
Okay so now how do you retrieve this label from all these cool
file paths well you call map function you say dot get
label and get label will get you those file
paths so you can say for okay
label in this print label okay now I got an error why
the error is tensor object has no attribute split
so the the file path argument that I'm getting here is a
tensor object and for tensor object you need to apply
spatial functions okay so you need to here say that instead of
this split okay it's the same thing what I would
say is tf dot let me write it here tf
dot strings dot split
file path and we'll use os separator so if you import os utility and you can say
os dot path dot sep this means the os separator and once you
have that you are you want to get the last second
element okay? So when you do this
see I get cat and dog I got the actual label
so I want my map function to be such that it not only gets labeled
but it reads the contents of the file you know what is your x and what is your
y well your x is if you look at our
presentation here do we have a presentation yes.
So if you look at our presentation here our
x x c x strain where is my extreme my x strain
is the actual image data and my y train is cat and dog
so so far we got only y part we need to get the x part so for x
part i will define a new function called process image file path.
And in this I will get the label as well as the image so what is my label
okay my label is cat label file
part. I love this tabs okay so my i got my label, now how do I
read my file in tensorflow there is an api
called tf.io dot read file this will actually read
your file. Okay so let's say I store this in an image. Now my file
is a jpeg image. So jpeg image I need to decode it so there is a
function called decode jpeg image, okay?
and then I need to resize the image because the
image are of different dimensions so I need to resize that image you know
kind of make every image the same size and I
will say okay make it 128 by 128
cool and once you have that now I return my image and label
so what you got here is x string is your image
y train is your label okay so we'll run this and in this process function
you will get call here okay
and then let me just call it for only first three elements
because otherwise it's going to be too much.
And when I call it for first three functions
what's now gonna happen is since this function is returning a tuple
I need to return a tuple here so I will say
imagine label okay? Okay. Now let me print image as well so
print image see if I nt image is gonna be
too big okay so I will print maybe only first
few characters or let me just print it you see it's printing the whole like
three dimensional array okay
but you kind of get an idea that now my training data set has basically
all my images and labels so my training doesn't look so far so
good all right now the next step we are going to do is I hope you
guys are not tired if you practice along with me when I'm
coding it's going to be super useful so I
recommend you watch this video you practice you pause it then you play
it. I think that's the best way to learn something.
Okay, so now I got my numpy array, I need to now
scale it so if you look at again our presentation.
[Music] Okay so in the presentation we do this
map lambda 255 so you we want to convert these
numbers in a range 0 to 1 okay so let me write my scale
function so my scale function what is my scale function
so my scale function takes log both image and label and it will return image
dash 255 and label as it is okay
and then when you do training ds is equal to trained yes
dot map scale and then I will just
I threw it through this
okay error what is that ts skill missing one requirement position argument.
Alright! Let's see what's going on here.
Okay what happened was here I did not when I converted this I had to do
train ds is equal to this because if I don't do that it will
just keep that copy in memory okay? So trained here so this step was
not there and that's the reason we got this error
so now I'm going to do this and you can see that
scale down by the way I did not print the entire image I just printed like
first few elements here but you can see that the numbers which
was see these are rgb values okay and rgb
values you know they are between 0 to 255 so I divided that by 255 and I
now I get a value between 1 to 255. now sorry I get a value between
0 to 1 okay now I'm not going to write it here but you can of course
chain all these calls you know you can do scaling,
mapping, you're filtering everything in one
shot like like the way we did it here you know
so you can chain all these calls and make your code look
very compact that's all I had for the coding part
now comes the most interesting part of this video which
is an exercise if you don't work on exercise
you're wasting my time my friend you better watch a movie on netflix and
relax if you don't want to practice coding you
know so practicing this exercise is very important and what we are doing in
this exercise is i have provided a reviews folder so if
you look at this reviews folder that are positive
and negative obviously these are movie reviews I'm talking about you might have
made a guess and the way the data comes here
is each review is an individual text file
so see, there is this review and this is a negative review and there
is another review negative review. Third review now this is a blank review
by the way so there are data errors here which I have
introduced them purposefully similarly positive reviews okay?
You need to read all these reviews into your tensorflow data set
then you need to filter those blank text reviews
okay? And then also split that review into your text
text review as well as your label which is positive or negative
and then perform all those transformations and
once you individually perform this transformation try to do it in a single
line of code there is a solution link which I'm
pretty sure you guys are all sincere students you're not going to
look at solution link without trying it on your own so I hope you find
this video useful if you did please give it a thumbs up
share it with your friends who are confused about tensorflow data input
pipeline. And if you have any question post in a
comment below
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