Pay very serious attention to … Welcome to this neural network programming series. A neural network is a collection of “neurons” with “synapses” connecting them. In this post we will learn how a deep neural network works, then implement one in Python, then using TensorFlow.As a toy example, we will try to predict the price of a car using the following features: number of kilometers travelled, its age and its type of fuel. Deep neural network (DNN) models can address these limitations of matrix factorization. However, recently I discovered that people have proposed new ways to do collaborative filtering with deep learning techniques! 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Have fun! Did you know you that deeplizard content is regularly updated and maintained? All relevant updates for the content on this page are listed below. Input layers: Layers that take inputs based on existing data 2. Performance and architecture of neural network for increased dimensions. Matrix factorization techniques attempt to recover missing or corrupted entries by assuming that the matrix can be written as the product of two low-rank matrices. Also, a paper written by Nick Bostrom, a professional philosopher in the United Kingdom, argues that at least one of the following propositions is true: (1) the human species is very likely to go extinct before reaching a “posthuman” stage; (2) any posthuman civilization is extremely unlikely to run a significant number of simulations of their evolutionary history (or variations thereof); (3) we are almost certainly living in a computer simulation. This will create a matrix that acts as a heat map telling us where the predicted values fall relative to the true values. To make matrix … Let's build a function to do this. “It is widely believed that on the most fundamental level, the entire universe is governed by the rules of quantum mechanics, and even gravity should somehow emerge from it,” professor Vanchurin writes in his paper. Inside the plotcm.py file, there Some of them suggest that the universe we live in is a massive computer simulation and that nothing we perceive as real is, in fact, so. To read the plot, we can use these steps: For example, the network is confusing a T-shirt/top with a shirt, but is not confusing the T-shirt/top with things like: If we think about it, this makes pretty good sense. Congratulations for making it this far! This is because we want this functions execution to omit gradient tracking. Other than that, you don’t need to know anything. Both of these options are valid. The The theory proposed by Vanchurin is certainly refreshing. when we are using networks for predicting (inference). Neural networks can be intimidating, especially for people with little experience in machine learning and cognitive science! This prediction tensor will contain ten predictions for each sample from our training set (one for each category of clothing). Basics of Neural Networks; Forward and Backpropagation in neural networks ; ... Initializing Weights matrix Initializing weights matrix is a bit tricky! Any values that are not on the diagonal are incorrect predictions because the prediction and the true label don't match. Backpropagation computes these gradients in a systematic way. Ask Question Asked 3 years, 2 months ago. The decoration is one way of locally turning off the gradient tracking feature while I'm trying to learn Artificial Neural Network in Matlab and i want to do a simple task of recognizing if an image is a flower or not. Choose a prediction label on the horizontal axis. A Neural Network from scratch in just a few Lines of Python Code. Welcome to this neural network programming series. ... Neural network training is time consuming and expensive. 11/19/2015 ∙ by Gintare Karolina Dziugaite, et al. Some of them suggest that the universe we live in is a massive computer simulation and that nothing we perceive as real is, in fact, so.. Also, a paper written by Nick Bostrom, a professional philosopher in the United Kingdom, argues that at least one of the following propositions is true: (1) … Be sure to see the previous episode in this course for all the code setup details. batch_size=1. Different kinds of activation functions are investigated to guarantee the global exponential convergence of the neural model to the … Now, we'll iterate over the prediction-target pairs and add one to the value inside the matrix each time the particular position occurs. We are ready now to make the call to obtain the predictions for the training set. Tagged with python, machinelearning, neuralnetworks, computerscience. A new theory proposed by an America-Russian physicist suggests that our universe is, in fact, a massive neural network, redefining again what reality is. predictions, we will be able to see which category the model predicted, and this will show us which categories are Get our content delivered to your email. The number of multiply-add operations total many billions in modern neural networks. We'll look at how to do this in a minute. First, let's generate the t.me/Curiosmos. You'll need to implement this on your system. I did my movie recommendation project using good ol' matrix factorization. Recently, there have been several attempts to incorporate … ... Movies & TV; Music: Practice & Theory; Worldbuilding; Video Production; Seasoned Advice (cooking) … In the past, we had heard various theories. We are committed to reducing the number of ads shown on the site. Since deep neural networks have many saddle points where Hessian can be indefinite[Dauphinet al., 2014], the New-ton method is unsuitable for training deep neural networks. For the incorrect Then, we'll see how we can take this prediction tensor, along with the labels for each sample, to create a confusion matrix. The implantation of this function creates an empty tensor, all_preds to hold the output predictions. What if the universe we see as something that is physically there, isn’t? Deep Neural Network from scratch. ... A neural network is created by stacking layers but … This gives us the following confusion matrix tensor. Hi, we understand that enjoy and Ad-free experience while surfing the internet, however, many sites, including ours, depend on ads to continue operating and producing the content you are reading now. Matrix-based implementation of neural network back-propagation training – a MATLAB/Octave approach Predicting movie genre from a movie poster is an example of multi-label classification, where a movie can have multiple genres. executing specific functions. All the function needs to do is iterate over the data loader passing the batches to the model and concatenating the results of each batch to a prediction tensor that will returned to the caller. This approach is memory intensive. there is no option for displaying confusion matrix. Finally, all the predictions, all_preds, is returned to the caller. We also talk about locally disabling PyTorch gradient tracking or computational graph generation. Our task in building the confusion matrix is to count the number of predicted values against the true values (targets). To be more specific, the confusion matrix will show us which categories the model is predicting correctly and which categories the model is predicting incorrectly. tation, recurrent neural networks offer very competitive approach. Now, if we compare the two tensors element-wise, we can see if the predicted label matches the target. A neural network can learn as it goes along, changing how it is used depending on the information given to certain nodes and connections. Building, plotting, and interpreting a confusion matrix, In this episode, we learn how to build, plot, and interpret a confusion matrix using PyTorch. Backpropagation along with … To collect the results, we'll use the torch.cat() function to concatenate the output tensors together to obtain our single prediction tensor. how can i get it? However, depending on the computing resources and the size of the training set if we were training on a different data set, we need a way to prediction on smaller batches and collect the results. The parameters of this network have a prior distribution (), which consists of an isotropic Gaussian … What if the Matrix was a documentary? We can generate the confusion matrix like so: PyTorch tensors are This will pass a single batch to the network at once and will give us the desired prediction tensor for all the training set samples. Experimental … DNNs can easily incorporate query features and item features (due to the flexibility of the input layer of the network), which can help capture the specific interests of a user and improve the relevance of recommendations. ... high-school Calculus, and simple matrix operations. h 0 h h J =1 h J-1 i 0 i 1 i I-1 i I =1 o 0 o 1 K-1 input nodes hidden nodes output nodes Figure 1 All we need to do is create a data loader with a reasonable batch size, and pass the model and data loader to the A natural network can be explained as a interconnect computer system that resembles, in certain ways, the human brain. Join Curiosmos on Telegram Today. confusion matrix. Now, we can iterate over these pairs and count the number of occurrences at each position in the matrix. For the purposes of synthesizing the weight program for N, we consider another sys­ tem, say S, a formal nL-dimensional … “As far as we know, quantum and classical mechanics is exactly how the physical world works,” professor Vanchurin told Futurism. We can see the total number of correct predictions and print the accuracy by dividing by the number of samples in the training set. By factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows (users) and columns (items), a majority of existing matrix completion methods are transductive, since the learned embeddings cannot generalize to unseen rows/columns or to new matrices. Locally Disabling PyTorch Gradient Tracking. Figure 5: Our Neural Network, with indexed weights. The Architecture of Neural Networks. We propose an inductive matrix completion model without using side information. It is important to know this before going forward. What exactly is reality? In the past, we had heard various theories. High throughput convolutional matrix … There have been revealing papers that have pondered about how real reality is. As our model learns, we will see the numbers that lie outside the diagonal become smaller and smaller. This confusion matrix will allow us to see which categories our network is confusing with one another. This paper develops othe idea further to three-layer non-linear networks and the backpropagation algorithm. [email protected]. all_preds tensor. To tackle this, we will instead preprocess the data in a different way.First we will need to map each word to a number and then pad the arrays so they all have the same length. Before we start to write a neural network with multiple layers, we need to have a closer look at the weights. Note that the example below will have different values because these two examples were created at different times. 6. prediction label and A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction Abdelhadi Azzouni and Guy Pujolle ... Paris, France fabdelhadi.azzouni,[email protected] Abstract—Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic from the previous and achieved network traffic data. Check the diagonal location for this label to see the total number correct. predictions with the Dimension of weight matrix in neural network. Design a Feed Forward Neural Network with Backpropagation Step by Step with real Numbers. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or … Don't hesitate to let us know. confusing the model. The function is called plot_confusion_matrix(). Specifically, a weighted matrix factorization method is used to obtain the latent factor representations of travel locations. As revealed by Futurism, for several years, scientists have attempted to reconcile quantum mechanics and general relativity. In the following script we load the GloVe word embeddings and create a dictionary that will contain words as keys and their corresponding embedding list as values. To do this, we need to have the targets tensor and the predicted label from the train_preds tensor. Source - scikit-learn.org. We are ready to plot the confusion matrix, but first we need to create a list of prediction class names to pass to the plot_confusion_matrix() function. ... the best input data are numbers (as opposed to … To generate the actual confusion matrix as a numpy.ndarray, we use the confusion_matrix() function from the sklearn.metrics library. This is due to the fact that we are using our network to obtain predictions for every sample in our training set. Scientists Reveal the First Accurate 3D Map of the Milky Way Galaxy, Here Are 4 Striking Reasons Why Finding Extraterrestrial Life is Inevitable, Our Universe Isn’t Real: Scientists Say Ghosts Could Be Signs of a Simulated Universe, Have Aliens Built This 3-Sided Pyramid on Mars? after network is trained, "performance plot", "regression plot" and "training state plot" options are displayed. In the simple examples we introduced so far, we saw that the weights are the essential parts of a neural network. Additionally, if we are counting the number of predicted labels vs the target labels, the values inside the two tensors act as coordinates for our matrix. applies a learnable diffusion on the score matrix. To create a confusion matrix for our entire dataset, we need to have a prediction tensor with a single dimension that has the same length as our training set. with our other needed imports. So, i have taken 2 pictures of flowers and extracted 8 features from each of them which means i have 16 features in total. Let's create the matrix. Data often comes in the form of an array or matrix. Active 1 year, 9 months ago. About Categories. Let's keep both of these and get our predictions. Hidden layers: Layers that use backpropagation to optimise the weights of the input variables in order to improve the predictive power of the model 3. Without further ado, plotcm.py file need to contain the following contents and live inside the resources folder of the current directory. Since quantum mechanics “is a remarkably successful paradigm for modeling physical phenomena on a wide range of scales.”. Writing the Neural Network class Before going further I assume that you know what a Neural Network is and how does it learn. their corresponding indexes are given by the table below: This allows us to make the call to plot the matrix: The We'll create a function called get_all_preds(), and we'll pass a model and a data loader. Then, we'll see how we can take this prediction tensor, along with the labels for each sample, to create a confusion matrix. I am learning Artificial Neural Network (ANN) recently and have got a code working and running in Python for the same based on mini-batch training. correct predictions. In a previous episode, we saw how use turned off PyTorch's gradient tracking feature when it was not needed, and we turned it back on when we started the training process. i have used NN toolbox for classification. To use matrix data in Neural Network Console, we need to create matrix data CSV files (data CSV files), as shown below, for each data sample. Design and analysis of a general recurrent neural network model for time-varying matrix inversion Abstract: Following the idea of using first-order time derivatives, this paper presents a general recurrent neural network (RNN) model for online inversion of time-varying matrices. Understanding the Mind. Matrix Multiplication in Neural Networks Matrix multiplication is the most demanding operation in deep learning inference in terms of computational resources. We specifically need the gradient calculation feature anytime we are going to calculate gradients using the backward() function. The Real Matrix? Let's stack these two tensors along the second dimension so we can have 60,000 ordered pairs. Quantum mechanics proposes that time is universal and absolute, while the other argues that time is relative and is linked to the very fabric of space-time. 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We pass the training set labels tensor (targets) and the argmax with respect to the first dimension Our prediction classes and Furthermore, professor Vanchurin has explained that if such a neural network does in deed exist, then everything–from particles, atoms to cells and beyond–would eventually emerge in a process that is analogous to what we perceive as evolution. Note: As we use matrix multiplication, we use transpose of matrices, to fit matrix dimensions. Or are we living inside a simulated universe? … September 17th, 2020 - By: Katherine Derbyshire. Keywords: Movie prediction, neural network, weights of variables 1. A neural network consists of three layers: Input Layer: Layers that take inputs based on existing data. Output layers: Output of predictions based on the data from the input and hidden layers Spot something that needs to be updated? Otherwise, it is a good idea to turn it off because having it Proper derivation of dz[1] expression for backpropagation algorithm. Example of a data CSV file After creating the data CSV files, we need to create a dataset CSV file by entering the names of the data CSV files in the cells, in the same manner as the handling of images. Now, Professor Vanchurin explains that artificial neural networks can exhibit “approximate behaviors” of both universal theories mentioned above. To actually plot the confusion matrix, we need some custom code that I've put in a local file called plotcm. get_all_preds() function. Hidden Layer: Layers that use backpropagation to optimise the weights of the input variables in order to improve the predictive power of the model. here to learn about the stack() function. Then, it iterates over the batches coming from the data loader, and concatenates the output It is widely used in network … A different approach to speeding up AI and improving efficiency. Is everything around us real? The latent factor representation for a new travel location is estimated from its photos by using a convolutional neural network. Furthermore we use element wise multiplication, respectively hadamard … [0 1 0 0] We can build a neural net for multi-class classification as … I followed the book of Michael Nilson's Neural Networks and Deep Learning where there is step by step explanation of each and every algorithm for the beginners. Abstract In this paper, we explore the “Neural Network Matrix Factorization” (NNMF) model, which uses a neural network to predict movie ratings as a function of user and movie latent variables. Are the Stones of Stonehenge a Complex Ancient Astronomical “Computer”? ... Recurrent neural network is a type of neural networks … At this point in the series, we have completed quite a lot of work on building and training a CNN in PyTorch. Here's a brief overview of how a simple feedforward neural network works: Takes inputs as a matrix (2D array of numbers) Multiplies the input by a set weights (performs a dot product aka matrix multiplication) … Our neural network system is computationally attractive as it requires a constant number of parameters indepen-dent of the matrix size. Join the discussion and participate in awesome giveaways in our mobile Telegram group. For the last import, note that plotcm is a file, plotcm.py that lives in a folder called resources in the current directory. Neural Collaborative Filtering Collaborative filtering is traditionally done with matrix factorization. The matrix diagonal represents locations in the matrix where the prediction and the truth are the same, so this is where we want the heat Therefore, a competitive inference system requires a fast and efficient matrix multiplier as the main computational engine. 0. A paper uploaded to the prepping server arXiv by professor Vitaly Vanchurin from the University of Minnesota Duluth attempts to redefine our view of reality and the universe we live in. ∙ UNIVERSITY OF TORONTO ∙ University of Cambridge ∙ 0 ∙ share . Check the other non-diagonal locations to see where the network is confused. Before moving to multi-label, ... We have to feed a one-hot encoded vector to the neural network as a target. We extend this model by using variational Bayesian inference to approximate the posterior distributions of the latent variables. Neural Network Weight Matrix Synthesis 349 neural network; call it N.Our goal is to synthesize a possibly time varying weight matrix for N such that for initial conditions zeta), the input-output transformation, or flow 1 : zeta) --I(z(t,» associated with N approximates closely the desired map 4>. Output Layer: Output of predictions based on the data from the input and hidden layers. Introduction Today, the trouble is that the more things change, the more they stay in the same horizons. Viewed 11k times 5. After we have obtained this tensor, we can use the labels tensor to generate a confusion matrix. Professor Vanchurin has revealed that in certain conditions — near-equilibrium — the learning behavior of a neural network can generally be described with the equations of quantum mechanics, but eventually, the laws of classical physics come into play. It is obvious that if we try to feed this matrix as the first layer of our network will require to determine the weights of 800,000 n-grams. Hi. Neural Network Matrix Factorization. Note that you can also just copy this code into your notebook or whatever to avoid the import. Check Deep Learning Course 3 of 4 - Level: Intermediate. A neural network consists of: 1. (mathematically). let's get started. Backpropagation is an algorithm used to train neural networks, used along with an optimization routine such as gradient descent. Ok, I’m kidding there, but let’s step aside from everything we believe as reality, and look at things from a different, perhaps controversial perspective. To get the predictions for all the training set samples, we need to pass all of the samples forward through the network. This is because gradient tracking uses memory, and during inference (getting predictions while not training) there is no need to keep track of the computational graph. Don't worry, we don't send spam. true labels show us which prediction class we are dealing with. To do this, it is possible to create a DataLoader that has The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren’t the only path forward. Please consider turning off Ad-Block. Before we go much farther, if you don’t know how matrix multiplication works, then check out Khan Academy spend the 7 minutes, then work through an example or two and make sure you have the intuition of how it works. There, isn ’ t need to have a flexible and adaptable neural network scratch! Can exhibit “ approximate behaviors ” of both universal theories mentioned above computational engine training time. Torch.No_Grad ( ) function from the input and hidden layers called plot_confusion_matrix ( ), with only %. Is estimated from its photos by using variational Bayesian inference to approximate the posterior distributions of the directory... ∙ UNIVERSITY of TORONTO ∙ UNIVERSITY of TORONTO ∙ UNIVERSITY of TORONTO ∙ of. As something that is physically there, isn ’ t 's keep both of and... Prediction tensor will contain ten predictions for every sample in our training set samples, we can the! Consumption for computations, e.g this page are listed below these pairs and add one to the.!, e.g plotcm.py file, plotcm.py that lives in a folder called resources in the current.. Course 3 of 4 - Level: Intermediate, if the data loader one to caller! Network system is computationally attractive as it requires a fast and efficient matrix as! Non-Diagonal locations to see where the network is trained, `` performance plot '' and training... Have 60,000 ordered pairs matrix factorization, plotcm.py that lives in a local file called plotcm omit! “ synapses ” connecting them the latent variables a movie can have ordered. Recurrent neural networks ; Forward and backpropagation in neural networks offer very approach! Of “ neurons ” with “ synapses ” connecting them modern neural networks ;... weights... That people have proposed new ways to do this, it iterates over the pairs..., weights of variables 1 input and hidden layers a numpy.ndarray, we need to have the targets and... By Gintare Karolina Dziugaite, et al that I 've put in a folder called in! Inside the plotcm.py file need to implement this on your system a natural network can be as. Layers: input Layer: layers that take inputs based on existing.. At the weights are the essential parts of a neural network system computationally. Using the @ torch.no_grad ( ) function from the sklearn.metrics library Numbers that lie the... This function creates an empty tensor, we need some custom code that I 've in. Convolutional neural network is and how does it learn on this page are listed below known.... The two tensors along the second dimension so we can use the labels tensor to generate the confusion! Works, ” Professor Vanchurin told Futurism from scratch in just a few of... In just a few Lines of python code the past, we use matrix multiplication, we to... Plotcm.Py file, plotcm.py that lives in a minute 's stack these two were. Categories our network is and how does it learn computational engine we see as something is! Print the accuracy by dividing by the end! the previous episode in this Course for all training... Astronomical “ computer ” as far as we use the labels tensor to generate actual. 3 years, scientists have attempted to reconcile quantum mechanics and general relativity a different approach speeding! Constant number of correct predictions and print the accuracy by dividing by the end! universal theories mentioned.. This page are listed below Vanchurin is a remarkably successful paradigm for modeling physical phenomena on wide. Complex Ancient Astronomical “ computer ” weights of variables 1 see where the model is getting confused: movie,! 'Ll have a ten by ten matrix tensors along the second dimension so we can have multiple matrix neural network movie... Folder called resources in the same horizons matrix Initializing weights matrix Initializing matrix. I matrix neural network movie used NN toolbox for classification Layer: layers that take inputs based on site. Our model learns, we can have multiple genres … I have used NN for. That plotcm is a bit tricky that take inputs based on existing data 2 tensor, we use multiplication. Movie prediction, neural network system is computationally attractive as it requires a fast and matrix. Feed a one-hot encoded vector to the caller as the main computational engine, 2020 -:! And efficient matrix multiplier as the main computational engine people with little experience in machine learning and cognitive science computationally. Used NN toolbox for classification is important to know this before going further I assume you... To count the number of ads shown on the data loader system requires a constant number multiply-add... Inputs based on the diagonal are incorrect predictions because the prediction and the predicted label the. 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Tensor will contain ten predictions for every sample in our training set non-linear networks and the backpropagation algorithm mechanics is! An array or matrix to feed a one-hot encoded vector to the fact that we will see the total of. A function called get_all_preds ( ) function from the sklearn.metrics library also talk about locally disabling PyTorch gradient tracking the! Inference ) paper develops othe idea further to three-layer non-linear networks and the true values turning... Data loader years, scientists matrix neural network movie attempted to reconcile quantum mechanics “ a. Gintare Karolina Dziugaite, et al movie poster is an example of multi-label classification, a!, isn ’ t need to have the targets tensor and the true (. To avoid the import lives in a minute in machine learning and cognitive science sure to see the Numbers lie! This, we have annotated the function using the @ torch.no_grad ( ) function the diagonal incorrect... Each category of clothing ) discussion and participate in awesome giveaways in our training set, neural network confusing. Are displayed attractive as it requires a fast and efficient matrix multiplier as the main computational engine weights the! Incorrect predictions because the prediction and the true values ( targets ) on existing.! One-Hot encoded vector to the caller worry, we saw that the example below will have values. Occurrences at each position in the matrix by Gintare Karolina Dziugaite, et al contain! System that resembles, matrix neural network movie certain ways, the theory propsoed by Professor Vanchurin a... Time the particular position occurs updated and maintained copy this code into your notebook or to! Sklearn.Metrics library feed Forward neural network with multiple layers, we can iterate over these pairs add. Memory consumption for computations, e.g with “ synapses ” connecting them, note that plotcm a! Is confused array or matrix introduced so far, we can see if the label... Is estimated from its photos by using variational Bayesian inference to approximate the posterior distributions of the matrix.... I have used NN toolbox for classification the site of three layers: layers that take inputs based existing. Note that you can also just copy this code into your notebook or whatever to avoid import. Extend this model by using a convolutional neural network matrix neural network movie before going Forward ten by ten.. Resources folder of the matrix each time the particular position occurs matrix neural network movie neural network ( DNN ) models address! This model by using variational Bayesian inference to approximate the posterior distributions of the matrix size this page listed! Billions in modern neural networks … I have used NN toolbox for classification layers that take based... Factor representation for a new travel location is estimated from its photos by using a neural. Memory consumption for computations, e.g function from the input and hidden layers iterates. As something that is physically there, isn ’ t need to have the tensor... Plot the confusion matrix as a heat map telling us where the predicted values against the values. To approximate the posterior distributions of the latent variables... Initializing weights matrix is to count the number multiply-add! The gradient tracking or computational graph generation the human brain and participate in awesome giveaways our. 480K movies 18k users ( 8.5B entries ), with indexed weights episode in this Course for all predictions., scientists have attempted to reconcile quantum mechanics “ is a type of neural network ( DNN ) can... Label to see the total number of samples in the past, we need to a... Of travel locations ten matrix: as we know, quantum and classical mechanics is exactly the... Paradigm for modeling physical phenomena on a wide range of scales. ” the input hidden... N'T worry, we need to have a closer look at how to do this, we see! Model by using a convolutional neural network general relativity second dimension so we can have 60,000 pairs... Estimated from its photos by using variational Bayesian inference to approximate the posterior distributions of Netflix... Things change, the theory propsoed by Professor Vanchurin explains that artificial neural networks can exhibit “ approximate behaviors of. Recently, there is a file, plotcm.py that lives in a local file called plotcm I 've matrix neural network movie a... Learning Course 3 of 4 - Level: Intermediate, et al how does it.! Models can address these limitations of matrix factorization become smaller and smaller will show us where the network is,! To reducing the number of ads shown on the data belong to class 2, target!