TensorFlow in an Hour: Handwriting Recognition using Computer Vision

1. Welcome to this Course: Hi, welcome to this course on TensorFlow. Tensorflow is a program that helps engineers build and train machine-learning models. In this course, you will learn about tensors and how to work with dancers using TensorFlow. We will start by looking at what tensors are. We will then learn how to build tensors using data. We will then see how to perform basic and intermediate math operations using TensorFlow. Tensorflow can also work with GPUs and TPUs, which are types of computer chips bill to extend dense and loose capacities. These chips make TensorFlow run faster, which is helpful when you have a lot of data to work with. At the end of the course, you will have a good understanding of what TensorFlow is and how we use it to build deep learning models. We will also be building a computer vision project where we will create a simple TensorFlow model to recognize handwritten images. This is the beginner level course, but I'm assuming some basic knowledge of python and machine learning. You don't have to be an expert in machine learning. But if you understand how data is used to train models for prediction, you'll be able to understand this course. If not, please find links to a couple of intro videos in the course description. If you're stuck at any point of the course, send me an email at admonition shiva.com, and I'll get back to you as soon as again. So let's get started. 2. Tensors and Tensorflow: In this lesson, we will look at what a tensor is, followed by the popular deep learning library tensorflow. Let's first look at what a tensor is. A simple explanation would be that a tensor is a multi-dimensional array. E.g. we have scalars, which is just a single number. Then we have a vector also called as an array. Then we have a matrix which will be a two-dimensional array. Finally, we have an answer which is an n-dimensional array, which means it can have any number of dimensions. In TensorFlow, everything can be considered a tensor, including a scalar. A scalar will be a tensor of dimension zero, a vector of dimension one, and a matrix of dimension two. Now this is useful because we're not limited to working with complex data sets in TensorFlow. Tensorflow can handle any type of data and feed it to machine learning models. Tensorflow is an open source software library for building deep neural networks. Google Brain team was the one who built it, and it is now the most popular deep-learning library in the market today. You can use TensorFlow to build AI models including image and speech recognition, natural language processing, and predictive modelling. Tensorflow uses a dataflow graph to represent computations. To put it simply, TensorFlow has made it easy to build a complex machine learning models and so-called takes care of a lot of work behind the scenes, which makes it useful while building and training any type of a deep learning model. Diencephalon also manages the computation, including parallelization and optimization on the user's behalf. And Tableau has a high level API called Keras. Get us was initially a standalone project which is now available within the TensorFlow library. Get us makes it easy to define and train models. While TensorFlow provides more control over the computation, TensorFlow supports a wide range of hardware, including CPUs, GPUs and TPUs. Tpus are tensor processing units built specifically to what the dense layers and TensorFlow. You can also run TensorFlow on mobile devices and IoT devices using TensorFlow Lite principle also has a large community of developers and it is updated with new features and gave him the obese almost on a monthly basis. Hope this video helped you to understand tensors and TensorFlow in detail. Next week we'll start working with TensorFlow on a Google Colab notebook. 3. Working with Tensorflow: Let's start writing some code. I'll be using a Google Colab notebook and you can find the link for that completed notebook in the course description. Alphas connect this notebook with CPU. Let's wait for a minute. Now it's initializing and grid. It is connected. If you don't know. Google Colab Notebooks help us to run Python and then submit code on the web. It's much easier to work with rather than setting up a local development environment. Now let's start by importing TensorFlow and printing out the Washington. You can press Command Enter or abdomen to run the code block. Great, we're using quotient to 0.9, 0.2. If you have a different version, don't worry about it. They won't be much difference. Let's start by creating a scalar using tf.constant. Tf.constant is a function that we will be using to load the course. But in real-world scenarios, we won't be using it that much because TensorFlow will handle a lot of tensile creation for you. But for now, let's create a scalar. It could be encoded as seven. And let's print it. You can see that we have created a scale-out with the language seven. He doesn't have a shape because it's just a single value. And the data type is integer 32. Now let's create a vector of the input will be an array with two values. And now let's print it. Greg, we can see that we have created a vector of shape to it when I was in digitally do. Now let's try creating a matrix. The input will be a two-dimensional array. Then we'll have 12, 30. Let's print it. We'll create the matrix which has shape to gamma2. It's a two-dimensional array and the data vectors integer 32. Now let's create a denser, call it densa of understanding. Bake all be based on language. And let's print it. You can see that we have created an actual tensor, which is also a three-dimensional array, and it has a shape of three by one by three. This is how we create an actual denser using tf.constant. We can see that the datatype is integer 32. What if we wanted to use a different datatype, let's say float 32s. We can use this data type as an argument and password when creating a bedsore. We'll just copy the symptoms are returned. I would call it. So one same values, but I'll specify the data type is float render. You can see that now the data that was filtered into, this is something you can change the data type when working with TensorFlow. In case you run into issues when you're working with large models and principle of science, it can handle it by changing the database. In real-world scenarios, we'll be dealing with tensors of higher dimensions and even bigger shapes. In the following lessons, I'll also show you how to convert a real-world data set like a group of images into a tensor. We have seen tf.constant, which is used to create constant and sauce. This is what you'll be using throughout the course. But if you want to create a variable denser, you can use tf dot variable. The difference between constants and variables that we can change the values in a variable denser but in a constant. And so you can't change the varnish. Let's create a variable dancer. I'll use the same pterosaur and call it denser than use the F dot variable. Now I'll print it. You can see that we have created a variable tensor with the same shape. And they're, they both into your W2. One of the most important attributes of a tensor is its dimension. Let's look at what the dimension of each of these varnish or the staff at the vector. Let's print the dimension using the ending property. In them. The virulent. We can see that it's dimensionless one, what's expected, and you can look at the scale-out. It's dimensionally be zero because it's just a single value. And for matrix AB dimension tool, there we go. And for denser, the diamond show would it be three? Think of dimensions as the number of columns, e.g. if you're using a dataset to calculate housing prices, you will be inputting the sky, feed, the location, and maybe a few other inputs. Each of these inputs will be called as dimensions. There can also be called as features. So I hope this lesson helped you to understand how to create tensors and find basic attributes like shapes and dimensions. In the next lesson, we will see how to generate dancers and also we'll see how the load tends us from NumPy arrays. 4. Generating and Loading Tensors: Now let's look at how to generate answers. In most cases, you won't be creating tensors from scratch. You will need to load a dataset, convert other datasets like NumPy arrays to tensors or generate answers. Let's see how we can generate some tensors. We will first create a tensor with random values. There are two common ways you can do this. You can jump into normal distribution or you can generate a uniform distribution of data. The normal distribution is a bell-shaped curve. So this represents the distribution of data. This means that most of the data will be close to the average and fewer data is away from the average. It basically means that the probability of getting a value near the average is higher. The uniform distribution, however, is a straight line that represents the distribution of data. So all the values can a uniform distribution will have an equal probability of occurring within the given range? That is one more thing you need to know before we start generating random values. That is a concept called seed. Seed is just a value. And if we use a seed value, we can regenerate the same set of data multiple times. So you know that we are going to generate random values, right? So if we use a seed, that same random values will be generated again and again. This is really useful when you're working with a machine learning model and you want to test that model against the same set of data. So let me create the seed l, call it seed db dot random dog. Alright, dove from seed. That will set the value, Let's say 42. Now, I'm going to create a set of random values based on the normal distribution. Or mother ends up is seed dot normal. And I'll put a shape. Let's say p by two. It has been created, let's print it. We have a tensor with the shape of three by two. And if you plot all these values, you can see that it deform a bell curve. So all these belong to the normal distribution. Now let's create another random tensor with the uniform distribution. Uniform Tarasoff. C dot uniform. I'll give it the same shape, three by two. And let me print it. But come into sharp formula. Now this is a uniform tensor with the shape of three by two. So this is how we can generate a set of random dead cells using normal and uniform distributions. Now let's see how to create tensors with zeros and ones. You might be wondering why we need this in TensorFlow. Tensors filled with zeros and ones are often used as a starting point for creating other dancers. E.g. they can also be used as placeholders for inputs in a computational graph. So let's first create a denser. Would see those. For that, we'd be using the df1 zeros function. Let's call it zeros. And we'll give it a shape. You can see that we have created a three by two tensor fluid syllabus. Now let's create the same tensor with one. The hub dot once, ship and ship equal to two. Let's print it. Here we go. We have a tensor of shape three by two for the dwarfs that are more different types of freedom pencils that you can create using TensorFlow, but these are the common ones that you will be using. Now let's look at how to convert a NumPy array into a tensor. If you don't know what NumPy is, it is a Python library for numerical computing. It helps us handle large data sets and perform a variety of computations on there. And before we had tensorflow, Numpy was all we had when we were working with machine learning models. So let's first import numpy and create an antibiotic. I'll create a NumPy array, followed by underscore a odd-odd. And I'll be using the range function, which tells NumPy to generate a list of values, let's say 1-25. And the data byte bleep be numb by Bob int. Let's printed. There we go. We have an array with 24 values ranging 1-25. Now we're going to convert this into a TensorFlow has made to support for NumPy. So you can easily convert you an unbiased into answer object. Let's call them by then. So this tf dot constant and my input will be the non-binary I just created. And the shape will be, I can set the custom shape for I equal to say, two by 34 by three. And let's print it. There we go. We have converted a one-dimensional NumPy array into a two-by-four by three tensor. You'll be doing this often when you're working with real-world machine learning problems. Because oftentimes there'll be other machine learning algorithms or other frameworks which people will be used. And you will get the final dataset in a numpy array. So it's very important to understand how we got to what NumPy arrays of objects. I hope this lesson helped you to understand how the genotype tensors and how to convert NumPy arrays into tensors. So in the next lesson, we will see how to perform some basic calculations, aggregations and matrix multiplication using TensorFlow. 5. Basic Operations using Tensorflow: Let's look at some basic operations using tensors. We will start by looking at how to get some information from our existing tensors. Let me create a new 40 denser. Use the df.columns function to generate a 4D tensor. Let's go with trying for tensor. And the shapely be two comma three color fulcrum of fire that's printed. So we have created a 40 denser, but the shape of two comma three comma four, comma five. Now let's get some information about this tensor. First print the size of the tensor. So for that we use tf dot size. Yeah, let me also print out the shape for that called the tensor dot shape. Then let's print the dimension b or b. Seen this flipped and Sadat and grit. Let's see what comes up. There we go. The size of what tensors 120 because there are 12d values. We have the shape and we have the dimension. This will be useful when you're working with complicated pencils. And if you want to equip you to get some information about the shape and size and dimensions. Now let's perform some basic operations on tensors. So let me define our simple dense or what we call it basic denser. Df, dy, constant, uncreated beauty denser, let's say 101112 grid. Let's try addition. I just wanted to add a number to each value in the tensor. So you can simply say, basic denser blows stem. This will add ten to all the numbers in the denser and we print out the result. Let's try some fraction. May also include multiplication and division. Multiplied by ten, comparing it to add it back down, Let's print up to it. But there, there we go. The first is the Edison. Second is subtraction. That is all the values multiplied by then and the full dish, all the values divided with them. The solution is a bit important because we'd be using it in our project. So there is a concept called normalization. That'll be fine converters that are running into zero to one. I'll explain that in detail when we come to that lesson, but please keep this in mind. Now let's look at matrix multiplication. This is also something that we'll be doing often when we are working on machine learning projects. Let me quickly create two pencils. We call it zero doesn't one, constant, two comma two. Forgone or for Ben, I'll create a new denser called filler, want to say 234 and acquired. Before we go into matrix multiplication, please keep in mind that the inner dimensions of the tensor that you're trying to multiply should match. E.g. let's assume you have two tensors of shape three by five. This multiplication won't work. But if you have two tens us with shape three by phi n phi by three, that will work because the inner dimensions match. The final result of the multiplication will be off the shape of the outer dimensions. So if you have two tensors, each with five by 3.3 by phi at the shapes, the final result will be five-by-five. Let's try multiplying these two tensors will be using the df.count malfunction meet printed tf dot html. And so the zero double one, and so 012. There we go. We have the product of these two matrices. Let's look at Domo matrix operations. So reshape and transpose. We will often use a V-shape to change your matrix structure. Then cleaning neural networks, e.g. an image pixel of Netflix 28 by 28 will be converted into a one-dimensional array of 7-day default values. You will see this in our upcoming project and I'll explain this in detail. But for now, understand that reshaping is a very important concept in TensorFlow. And use the sentence or that be created in the previous code blocks. Denser zero double one left me with reshape it into four by one. For that I'll be using df dot reshape. And my first argument will be the actual tensor, will just answered that one. And the second argument will be the shape that they say four by one. So if you see this, I'm trying to convert a two-by-two Tensor into fall by one tensor. There we go. So the value is two comma 2.4 comma four are converted into a four by one tensor. This is all we reshape a tensor. Now let's see how the transpose and then soft baby using the df dot transpose function. And then you can keep it the same, denser than, sorry, zero to one. Straight. There we go. You can see that the values have been transposed, e.g. the values two-by-two and four-by-four are now 24.2 for, if you don't know what transposing a matrix, this is just converting into rows and two columns and column to the horse. Now let's look at performing some aggregations using dense us. In often cases you would want to find the sum, mean, median, and standard deviation of a pencil. Let me create a symbol dancer, DFP, constant. We'll just do a simple array, 1-9, pre band six, settling a date, work. I'll set the data diapers for. Now. Let me print out some values. First, I want to find the minimum value within this tensor. For that, I'll be using the reduced Min function f dot with her, but abuse and then give the denser lead us input on three. Let's print it. You can see that the minimum value is one. Let's do a few more aggregations like this. Reduce. Let's copy this. Then. Now to find the sun. Let's try them English. So the minimum value is one, the maximum value is nine and the sun was 45 grid. Now let's open a new code block and dry somewhat aggregations. I wondered then the standard deviation of this tensor for that, I'll be using tf dot map, reduce, STD, and the denser lastly in bold there. And I want to print the variance, which is also in P of Dartmouth. Let's try this. There we go. We have the standard deviation and the variance. Now let's try a few more simple aggregation is we're inclined to find this quiet is quiet volt and the log of all the ladies of the tensor. First to the squash ruled, which is T of dark SQRT, denser than disquiet will just tf dot squared denser. And I wanted to print the log, which is in math. So df.net dot log of the densa, let's print it. You can see that DFS quite robust phone disquiet or for each value of the tensor. And D are both quiet squares, all the numbers in the denser. And finally, Math.min log finds the log value of all the bodies of the dancer. These are the basic operations that you need to know for now. In the next video, we will look at an important concept called one-hot encoding. 6. One-hot Encoding: In this lesson, we will look at an important concept that you will come across in deep learning, one-hot encoding. One-hot encoding is a process used to represent a set of values as category based binary data. This encoding creates a new binary column for each unique category. Each row in the dataset is then assigned either a one or a zero. So e.g. consider a dataset with a variable color having three unique categories, red, green, and blue. If we use one-hot encoding, this variable can be represented as three new binary columns. Each row in the dataset will have a one in the column corresponding to assign the color. E.g. if you look at row one, which is thread, the first value is one and the other two values are zero. And in green, the first and the last value is just zero and the middle is one. And in blue the last value is one. Deep learning algorithms, particularly neural networks, work with numerical data. One-hot encoding provides a convenient way for us to convert these type of categorical variables into simple numerical representations. So these can then be used as inputs or even outputs for deep learning models. Using one-hot encoding, we can handle many type of category variables. This is usually difficult for other encoding models to handle. And finally, one-hot encoding provides a clear mapping between the categories and their corresponding numerical representations. So this makes it easier for the model to interpret and analyze the results of deep learning models, e.g. if we tried to build an algorithm to classify between cats and dogs, a one-hot encoded output will be much easier for us to convert into the final result. I hope this lesson helps you to understand how one-hot encoding works. In the next lesson, we will see how TensorFlow can work with GPUs and TPUs. We won't be working with GPUs and TPUs and this project, but it is great for you to understand how you can make use of GPUs or TPUs if you have them available. 7. Working with GPUs and TPUs: So let's see how to work with GPUs and TPUs using TensorFlow won't be seen what GPUs and TPUs are, but let's look at them in more detail on how we can use them. But TensorFlow, GPU or graphics processing units, and TPUs are tensor processing units or special hardware design for speeding up machine learning process. They have many goals and can process data much faster than traditional CPUs. Let's first start by looking at what devices we have available in this Colab notebook. For that, we will use the F dot config list. Physical devices. You can see that B, we only have a CPU allocated for us. Now, let's use our runtime and allocate a GPU. Could change from time to time and convert it to a GPU. I wouldn't be showing you how to connect to a TPU because CPU will always be available. So let's just look at the GPU and how we can work with that. Save it. So very important once you change the runtime, please make sure that your Colab notebook is collected and run TensorFlow. You just have to read on the import buck. You don't have to worry about the rest. Let's feed on it. Great. Now let's see what devices we have access to. There we go. We can see that we have a GPU or by good for us. As far as TensorFlow is concerned, you don't have to switch between CPUs and GPUs because TensorFlow automatically takes care of it for you. If there are TPUs, the code will be slightly different, but you won't be using TPUs unless you're working with extremely large deep learning models. If you want to specify a device that you want to use for your code, what you can do is you can use the tf dot device function and give it the device name. It would say GPU Cielo. And then you can write the rest of your code in this code block. So all the gold that is in this code block will run using the GPU. So I hope this lesson helps you to understand what GPUs and TPUs are and how you can actually make use of them in your code. In the next lesson, we'll start with our actual project, which is a handwriting recognition neural network using TensorFlow and get us. 8. Preparing the Model: Now that we have learned and slow basics, let's start building our project. We will use the MNIST dataset of handwritten digits to train our model. Mnist is the image dataset with 60,000 training images and 10,000 test images. So we can use this 60,000 training images for training our model. And we can use the 10,000 test images to see how well our model performs. These digits range 0-9. Each image is of size 28 by 28 and in grayscale. So if you look at this block, the x-axis is 28 and the y-axis is 028. Each pixel will have a value ranging 0-255, e.g. the empty blocks will be zero and the darkest blocks will have the value of to 55. So this is how each image is represented in this dataset. We will have a total of 784 pixel values per image. If this is clear to you, Let's start by adding the code. It will first import the MNIST data set. I'll create a variable called MNIST. And we can get the dataset from Geddes, DFL, get us data sets. Now I'm going to create four variables. Extreme, white train and x test and bike test. So the x train and y train will be used for training while the x test and y desk will be used for testing. And I'll call the load data function. And we should add the date downloaded into these variables. So let's test it. And one value from x train. There we go. We have a 28 by 28. And so a wide range will add the value of five. So x has images and Y has the actual value of the signatures. That is an important step we need to do, which is called normalization. So each pixel in this image will links 0-255 and we're going to come with their traits to zero to one. So this is to make the data-set simple and easy for the model to understand. In order to normalize these, I'm just going to divide all the image values by two. So x train and x test would be divided by width divide. Let's double-check this. There we go. Now our image values range 0-1 and 002, 55. X test dataset is ready. Now let's start building our model. We will build a sequential model using getters, as we saw before. Get us used to be an individual library for building deep learning models, but it has been integrated with TensorFlow. Keras we used to code all the layers herself. It was quite complicated and made TensorFlow hub to work with. But with Geddes, It's much easier for us to stack a bunch of layers together and build a network of gold is modern. I'll call the sequential model. And my first layer, it will be a flattening layer. So Kayla's stopped. But it still flatten. The input shape is 28 by 28. So what this layer will do is it will take our 28 by 28 tensor and convert that into a single-dimensional array. So our input layer won't be a 28 by 28 layer, it will be 0784 layer one-dimensional array. So that will be passed as an input to our neural network. I'll show an image at the end of this model, so it will be much more clear to you. The next layer would be a dense layer. And this will have 128 neurons with an activation function called relu. Activation functions help us to capture patterns and relationships. And this ReLu activation is a commonly used activation function. So it will take the output of the first layer and only they're done a positive value. If the output is negative, it returns zero. So this helps us to avoid negative values and speed-up training the model. Next layer will be a dropout layer. And we'll set the drop off rate of 0.2. So the dropout layer prevents overfitting. Overfitting happens when our model starts to lean on a specific output, e.g. if we train our model with 10,000 documenters and thousand images, the model will automatically have a bias towards dogs and we'll classify more cat pictures of stocks. So this troubled layer helps us to reduce that. The 0.2 arguments specifies that the rapid rate is 20%, meaning that 20% of the neurons in the previous list will be dropped during training. Finally, we will construct another dense layer which will be our output. This will have 10-year-olds there and let's just print it out. Model. Made a mistake of Messina common. So our model is ready. This is what we have constructed. If you look at this model, the first layer has so many different neurons, which will be the input layer. And this layer flattens out 28 by 28 denser image into a salmon AT for one-dimensional array. So that will be passed as an input. Then we have the activation layer, then we have the dropout layer, and finally, we have the output layer. We will need one more layer later in the project, which will be a softmax layer. So all of these outputs will be predictions and they won't be probabilities. So there'll be scores. And we need a way to convert the scores into actual probability so that we know what the model is trying to predict. So we have just constructed a model. It is not trained yet, but we will just pass it some data and see how well it is working. Create a variable called predictions, and I'll call the model and pass a value from x-ray that's passed the first value. The reason I'm using this slice operator is because the slice operator returns an array. Input has to be an array of tensors. So even if it's a single value does to me. But then another way, That's why I'm using the slice operator. It returns an array of values. And let me print predictions. You'll see that it will print out a bunch of scores here. So there are positive and negative values. Let's see called softmax works. So softmax is basically a layer that converts a discourse into actual bumblebees. So create a layer called softmax and pass it the predictions. Let's see what it comes up like. So now we have probabilities. So this means that the model is trying to tell us what is the probability that the input belongs to any of these then glasses? So the first one means zero. Second one is one minus two, and the last one is tonight. So we have probabilities for each numbers in the dataset. So let's make this even simpler and see what the model is trained to predict. For that, we'll just convert these predictions in do a simple array. I use NumPy to make it simpler and I'll get the first learning. Because as you can see, this is within another, I'm just grabbing this first value and I'm going to convert it into a Python list. And I'm going to print the maximum value, the index of the maximum value for simple array index match value of sub below it. So now we can see what the model is trying to predict. Debbie go, it's trying to say it's seven. Let's see what the actual value was. So print x train of one, so white train of one. So the actual value spike, but our model says it's seven. That's because our model is trained yet. So once you have trained our model, you will see how much the spiritual impulse. So with the code block B ever done via constructed a fully-connected layer deep neural network. So the next chapter we'll be looking at optimizers and loss function and we'll see why they're important for us to train our model. 9. Optimizer and Loss Function : In this lesson, we will discuss the use of loss functions and how they help in training deep neural networks. We will also talk about optimizers and see how we can use them to minimize the loss function. We finish the lesson by looking at the popular Adam optimizer, which we will be using for our project. I lost function is also known as a cost function. It is a mathematical function that measures the difference between the predicted output of a model and the actual target value. So when we train a neural network, but inputs and outputs, the neural network will generate its own output. It then compares that output with the actual output that we have given in the training set. So this is how the neural network learns. The goal of the training process is to minimize the value of these loss functions. The closer the predicted values are to the actual values, the lower the value of the loss function will be. In deep learning, loss functions help us to improve the accuracy of the model. The model parameters are adjusted in such a way that the loss function is minimized, giving us better predictions. The choice of loss function will depend on the type of problem you'll find yourself. So there are a lot of loss functions like cross-entropy loss function. Or if you are working on other collision problem, you'll be using something like a mean squared error, loss function. Aggressive means, stock market price prediction, housing prices prediction and other similar problems. You can find more resources on loss function, the course description, I'll add a link for you. So now let's talk about optimizes. Optimizers are algorithms that help us to minimize the loss function. So they work by updating the model parameters in such a way that it keeps bringing down the value of the loss function. And as we saw before, the lesser the loss function is, the better the model gets trained. So there are many different optimal choices available, each with its own strength and weakness. One bubbled optimizer is called Adam optimizer. Adam optimizer is very effective in a wide range of deep learning tasks and it's a great choice for us to use for this project. Adam optimizer combines the advantages of gradient descent, which is an older algorithm. If you have studied at initial learning, you would have heard about gradient descent. So Adam optimizer is an implement on gradient descent. And also it's computationally very efficient. Even though to understand the logic behind these cost functions and optimize it for now, TensorFlow will handle all there for you. For now. Just understand that loss functions will help us reduce the endosome predictions and optimizes help minimize the loss function. So I hope you understand how lost one chosen optimizers work. In the next lesson, we'll construct a loss function and you have an optimized it and we start compiling and training our model. 10. Compiling and Training the Model: So now let's create the loss function. I'm going to use the sparse categorical cross entropy function, which is commonly used for classification models. Then go to goal loss function in Keras. And I'm going to save from the logits is true. I'm calling the logits because the actual predictions or goddess logics. So that's why I'm saying we're going to calculate losses from logits. Now let's compile our model. And I'm going to specify the optimizer, which will be Adam. And my loss function is in-laws for sure I just created and I'm going to print out some accuracy metrics. For now. I just want him to see the accuracy. So this will tell us how good our model is getting during training. We have, because I've made a mistake here. So let me run this and make sure the stroke is properly. That is one word, dipole. It just doesn't now a hedge, so sparse scattered across interfaith, that stuff there. And now let's compile the money. There we go. Part of modulus Compiled. Now let's start training. Our model will be called model dot fit, which is fitting the data into the neural network. And I'm going to pass it extreme and white train values. I'll also specify a box feeble to fight. So epoch means an iteration. So epochs tell the model how many times this model should train with this dataset. So if we specify the e-book as 5k, the deep learning model, we go through this dataset phi times. This is for the model to understand any patterns that it has missed during the first or second titrations. So you can improve the trading by increasing the box. But after a certain point of time, the accuracy will start to be constant. So that means the model as the lungs as much as it can from the given data. So let's start with the box fight and let me train them on. You can see that the training has started and the first titration is going on. And you can see our accuracy is now 91%. It goes to rent high percent in the second iteration, and it keeps getting better than the loss function gets reduced. Now we added the 97% accuracy. Let's increase the box to ten so that you can understand what is exactly does. And when I increase the box, this model is already trained. So it can be training. So if you continue training it from this point, so you'll see that the starting accuracy ranges. I do seven per cent. Yeah, we have my good person, but it goes a little down. So you can see that the accuracy is now all 98%. It doesn't get better than that. So this is a good point for us to stop with our inbox count. So that is only a certain number of vibrations that you can do with a machine learning model. Put it back to five. So you can see that the training is now complete and we have, we are at 98% accuracy in real-world scenarios. Even if you have an accuracy of more than 80%, you should have a decent deep-learning model. Our model is trained and ready to predict. In the next lesson, we'll play with some sample digit values and see how well our model performs. 11. Predicting Handwritten Digits: So we have built-in trained our model. Now, let's see how good our model is. First, we will use the inbuilt evaluate function and pass the testing data and see how good the model performs. Let's try that. Modern birth weight, x-test and white dust. Lots of verbosity so that we know what's happening in the background. So we have an accuracy of around 97%. The loss is 0.7. Great. This means that we have a legal mortgage. We need to add one more step before we start looking at the prediction values. As we saw before, this model prints out the scores for the predictions. It doesn't give us the probabilities. So we will add a softmax layer and then we will look at the problem it is. Now I'm going to create another sequence layer. And clubs are existing model and a softmax layer together. Let's do that. We'll call this the probability model. So this model is the same as our existing model, but we're just going to add a softmax layer. So let's say the Kayla's dot sequential. And we're going to add our model and a softmax layer grid. Now our model is ready. So we will take some values from the testing set, which is x test and y test and see if I'm only gets the value is correct. This is something we did earlier, but our model was predicting everything wrong. Now let's see how it's doing. After I've training, I'm going to add another code block where first I'm going to print the original. Call it original. And it'll be a white dust value. Let's stop at zero. Let's look at what way does this. It's seven. Great. Now we will bust the x test value in the mix and give it to our model and ask it to predict it. So if you get the same value, it means that model is working as expected. So let's say output will be probability model. And I'm going to Bus it. X test value will be zero. So this is the first value. So the zeroth element, That's why I'm saying it's called The one. You can also say zero golden one. Now, I'm going to use the same logic which I did before, but just to convert this into among byte array and get the index of the maximum probability. Let's say output is output dot non by grabbed the first element and convert it to a list. And I'll turn predicted value of the index of the maximum valid when the output rate. So therefore it's printing out the original value than we are passing the x value that belongs to this output and we're setting it or modern to ask the model what it thinks the given input S. So if we have our original and productive equal, that means our model is doing well. Let's try this grid or more. You got it, right? Let's try another one called dual 1200. Do the same job. There we go. Religion is five and the predicted value is five. Let me try one more. Go for 50. Melody used to seeing value. Show. There we go. It's 4.4. You can play with different inputs and you will see that 97 out of 100 times our model will predict the values correctly. So great job. We have built a working deep learning model using computer vision that predicts handwritten digits. You can only imagine how other classification problems like Datadog predictions and other gentlemen image classification problems work. And I'm sure you have a few questions after going through this course. So please don't hesitate to get in touch with me. You can reach out to me if you're stuck at any point and the cosine, I'll be happy to help you up. So let's do a quick summary of what we've seen so far. 12. Conclusion: Again, great job at finishing the course. You have learned a lot. We started by looking at tensors and how TensorFlow helps us to build and work with tensors. We then saw how to perform operations on tensors and generally tends us using NumPy arrays. We also looked at loss functions and optimizes and how they help to improve our neural network. We then learn how to load the MNIST dataset and build a model using Keras. Finally, we compile and train our model to predict handwritten digits. Hope this course helps you to understand how to work with TensorFlow. If you have any questions, please don't hesitate to get in touch with week. You're going to reach out to me at help admonition shiva.com. Thank you for taking this course. I would love to hear your feedback to make the next course even better. So thank you again for taking this course. See you soon with a new topic.