Tensorflow Input Pipeline | tf Dataset | Deep Learning Tutorial 44 (Tensorflow, Keras & Python)

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

Channel: codebasics

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Keywords: tf data pipeline, tf data shuffle, tf data api, tf.data.dataset tutorial, tensorflow input pipeline, tf input pipeline, tensorflow data shuffle, tensorflow data api, input pipeline tensorflow, input pipeline, tensorflow pipeline, tensorflow pipeline example, tensorflow dataset, tensorflow input pipeline tutorial, tensorflow data pipeline, tensorflow input pipeline performance, Input data pipeline, tf.data pipeline, tensorflow datasets, loading data tensorflow

Id: VFEOskzhhbc

Length: 33min 19sec (1999 seconds)

Published: Thu Jun 10 2021