Is Apache Airflow 2.0 good enough for current data engineering needs? If x > 0 — the volume of the array of pixels remains the same, and if x < 0 — it cuts off unnecessary details in the channel. Many of such models are open-source, so anyone can use them for their own purposes free of c… The dataset was created based on the Grocery Store Dataset found on github, with images from 81 different classes of fruits, vegetables, and packaged products. The solution builds an image classification system using a convolutional neural network with 50 hidden layers, pretrained on 350,000 images in an ImageNet dataset to generate visual features of … Consequently, this model is be sufficient to train on 10 epochs. Image_to_array means that image in PIL format returns a 3D Numpy array, which will be reshaped on further. It is a very interesting and complex topic, which could drive the future of technology. Learn more. Dropout takes value between 0 and 1. The goal is to minimise or remove the need for human intervention. Neural Networks Alex Krizhevsky University of Toronto [email protected] Ilya Sutskever University of Toronto [email protected] Geoffrey E. Hinton University of Toronto [email protected] Abstract We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-ferent classes. Тhen it transmits its data to all the nodes it is connected to. Typically this number is equal to the number of samples for training (in my case it is 400: 200 photos of cars and 200 photos of elephants) divided by the batch size (16). Input images were fixed to the size 224×224 with three color channels. Numbers 2, 2 denote the pool size, which halves the input in both spatial dimension. My project goal is to develop a tool for these 2 organisations: to identify animal breeds for SPCA; and to identify avian species for NParks. As a result of testing the model, I got a very good accuracy: 96% of correct classification samples after 50 epochs. ML Practicum: Image Classification Introducing Convolutional Neural Networks A breakthrough in building models for image classification came … At the end of the program shows this result here: It can be seen that after 50 epochs the validation accuracy is 0.9375, it shows the ability of the model to generalize to new data. Where 300 is width, next 300 is height and 3 is RGB channel values. Machine learning is a class of artificial intelligence methods, which allows the computer to operate in a self-learning mode, without being explicitly programmed. This means that the model can be used to evaluate new data. I determined that 10 epochs are enough for a successful training of the model. The CNN follows a hierarchical model which works … On the first plot it can be seen that the high accuracy (96%) is achieved after 10 epoch. Filed Under: Deep Learning, Image Classification, Image Recognition, Tutorial. Numbers 3, 3 correspond to the kernel size, which determinate the width and height of the 2D convolution window. Multilayer Perceptron (Deep Neural Networks) Neural Networks with more than one hidden layer is … The last wave of neural networks came in connection with the increase in computing power and the accumulation of experience. I have tested that this model works really well with a small number of photos. When the preparation is complete, the code fragment of the training follows: Training is possible with the help of the fit_generator. Image classification using Convolutional Neural Network In the last few decades, machine learning has gaining a lot of popularity in the field of healthcare, autonomous vehicle, web search, and image recognition. Image classification involves the extraction of features from the image to observe some patterns in the dataset. I used Python syntax for this project. Тhis value describes the intensity of the pixel at each point. Python codes for the above analysis are available on my GitHub, do feel free to refer to them. After running the code and saving the model it’s time to check its accuracy on the new testing photos. The successful results gradually propagate into our daily live. Incidentally there is some chance that this horse could be a deer or a frog, because of certain features picked up by the model. Here the layers begin to be added. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Convolutional neural networks (CNN) is a special architecture of artificial neural networks, proposed by Yann LeCun in 1988. As a framework I used Keras, which is a high-level neural network API written in Python. Here I would like to describe the code that was taken as the basis of this project. А bit of theory in the beginning does not hurt :). Convolutional neural networks power image recognition and computer vision tasks. Since I’m a newcomer to this issue, I took a ready-made model from Keras blog. Image classification is a prominent example. Data comes from the input layer to the output layer along these compounds. But in order to recognize the properties of a higher level such as the trunk or large ears the whole network is needed. Take a look, Stop Using Print to Debug in Python. Each image is 28-by-28-by-1 pixels and there are 10 classes. Scale these values to a range of 0 to 1 before feeding them to the neural network model. Steps_per_epoch (or number of iterations) shows total number of steps, which is used to declare one epoch finished and begin the next. At the end it will report the final accuracy of the model. An input image is processed during the convolution phase and later attributed a label. The activation function of this model is Relu. After model construction it is time for model training. How about to identify these cats? There are already a big number of models that were trained by professionals with a huge amount of data and computational power. The evaluation accuracy was calculated using additional dataset of 400 pictures. So I explored a simple neural network, and then progressed to convolutional neural network and transfer learning. This data set has never been seen by the model and therefore it’s true accuracy will be verified. Convolutional Neural Networks (CNNs) are the backbone of image classification, a deep learning phenomenon that takes an image and assigns it a class and a label that makes it unique. Then the already described Image Data Generator is added for training and tasting samples. In subsequent epochs on the plot the accuracy does not improve (and even decreases in interval 10–25 epochs). In this paper, we propose a novel lesion-aware convolutional neural network (LACNN) method for retinal OCT image classification, in which retinal lesions within OCT images are utilized to guide the CNN to achieve more accurate classification. The model has five convolutional layers in the feature extraction part of the model and three fully connected layers in the classifier part of the model. Also load_image means that image will load to PIL format. Thus I installed a dedicated software library — Google’s TensorFlow. I input these images to the model, and the simple neural network model was able to make to classify them according to the highest probability. Using pretrained deep networks enables you to quickly learn new tasks without defining and training a new network, having millions of images, or having a powerful GPU. In machine learning, Convolutional Neural Networks (CNN or ConvNet) are complex feed forward neural networks. Тhis class can create a number of random transformations, which helps to increase the number of images when it is needed. In the beginning of this part I would like to describe the process of Supervised machine learning, which was taken as a basis of the model. It takes time for the pet to be identified and hence prolongs the wait to be listed for adoption. Тo create such model, it is necessary to go through the following phases: Model construction depends on machine learning algorithms. For example, if image size is 300 x 300. After three groups of layers there are two fully connected layers. In this project, image classification is performed using three different types of deep convolutional neural networks in order to classify groceries of fruits, vegetables, and packaged liquid. Imagine that the reading of the input matrix begins at the top left of image. Optics is ideally suited for realizing neural network models because of the high speed, large bandwidth and high interconnectivity of optical information processing. The second plot shows the dependence of accuracy and validation accuracy on the number of epochs during the testing. Fine-tuning a pretrained image classification network with transfer learning is typically much faster and easier than training from scratch. During this phase a second set of data is loaded. First, the path to the folders is specified. Oxford has already trained the VGG16 model on many cat species, and the model has no problem classifying them. The CNN model was able make the correct prediction most of the time, for example the model was quite sure that this is an airplane, and this is a ship with 72% probability. Finally, the saved model can be used in the real world. Next is Dense — densely connected layer with the value of the output space (64) and Relu activation function. Further convolution layers are constructed in the same way, but do not include the input shape. Now, when the model is dissembled it can be run. We will be building a convolutional neural network that will be trained on few thousand images of cats and dogs, and later be able to predict if the given image is of a cat or a dog. To improve classification accuracy, I need more data. During my course I was lucky to meet a mentor — Jan Matoušek from Data Mind, who helped me to discover a new world of artificial neural networks. I can't find any example other than the Mnist dataset. The era of AI democratizationis already here. Two months ago I wanted to change my life and I enrolled in the programming course from Digital Academy — Czechitas, Prague. Is a machine learning algorithm, which is built on the principle of the organization and functioning of biological neural networks. Let us look at the first convolution layer Conv 2D. With the advancement of artificial neural networks and the development of deep learning architectures such as the convolutional neural network, that is based on artificial neural networks has triggered the application of multiclass image classification and recognition of objects belonging to the multiple categories. This is a skill that people learn from their birth and are able to easily determine that the image in the picture is an elephant. Each individual node performs a simple mathematical calculation. This function setts the zero threshold and looks like: f(x) = max(0,x). When the model is trained it should be saved with save_weights. Let us consider the use of CNN for image classification in more detail. For this, I decided to build two plots. A convolutional neural network is not very difficult to understand. My goals were to understand how the model works, describe it; customize the model and teach it to recognize photos of cars and elephants. http://www.datamind.cz/cz/vam-na-miru/umela-inteligence-a-strojove-uceni-ai-machine-learning, https://en.wikipedia.org/wiki/Artificial_neural_network, https://en.wikipedia.org/wiki/Deep_learning, https://en.wikipedia.org/wiki/Convolutional_neural_network, https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/, https://www.lynda.com/Google-TensorFlow-tutorials/Building-Deep-Learning-Applications-Keras-2-0/601801-2.html, https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html, General Purpose Tensorflow 2.x Script to train any CSV file, PyTorch tutorial for beginners — 5 functions that you probably didn’t know about, ELECTRA: Pre-Training Text Encoders as Discriminators rather than Generators, SFU Professional Master’s Program in Computer Science, Overview of the different approaches to putting Machine Learning (ML) models in production, Everything You Need to Know About Image Segmentation, Natural Language Processing for Fuzzy String Matching with Python, Effect of Batch Size on Neural Net Training, begin with its object: model = Sequential(), then consist of layers with their types: model.add(. This means that after the 10th epoch the model can show the same result, but it will not be better. Without this property a network would not be sufficiently intense and will not be able to model the response variable (as a class label). In this projects case, it was neural networks. Identifying patterns and extracting features on images are what deep learning models can do, and they do it very well. Running takes some time. Machine learning has been gaining momentum over last decades: self-driving cars, efficient web search, speech and image recognition. It shows width and height to which images will be resized. At this moment Keras communicates with TensorFlow for construction of the model. This would help to improve the classification tool for these 2 organisations (SPCA and NParks). And also this is a deer and this is a horse based on the highest predicted probability. My next step is to look for many images of common birds and animals found in Singapore to train the model, so as to append to the “knowledge database” of the model. The network will consist of several convolutional networks mixed with nonlinear and pooling layers. The successful results gradually propagate into our daily live. In 2007, right after finishing my Ph.D., I co-founded TAAZ Inc. with my advisor … The nonlinear layer is added after each convolution operation. A fragment of the code of this model written in Python will be considered further in the practical part. I would also like to experiment with the neural network design in order to see how a higher efficiency can be achieved in various problems. Its ability to extract and recognize the fine features has led to the state-of-the-art performance. Make learning your daily ritual. It is a very interesting and complex topic, which could drive the future of t… Image Classification Using Convolutional Neural Networks: A step by step guide analyticsvidhya.com - devansh75. After passing the filter across all positions, a matrix is obtained, but smaller then a input matrix. As a development environment I used the PyCharm. your model starts to basically just memorize the data. To do so, divide the values by 255. Validation accuracy sows the ability of the model to generalize to new data. The main task of image classification is acceptance of the input image and the following definition of its class. Identifying patterns and extracting features on images are what deep learning models can do, and they do it very well. As a result the image volume is reduced. It’s look this way: model.fit(training_data, expected_output). Image classification can be done using neural network models. I'm trying to look for the classification of images with labels using RNN with custom data. To demonstrate how to build a convolutional neural network based image classifier, we shall build a 6 layer neural network that will identify and separate images of dogs from that of cats. However, this model would take a long time to train with my limited resources. ImageDataGenerator has the following arguments: To specify the input directory load_image is used. Define the convolutional neural network architecture. Machine learningis a class of artificial intelligence methods, which allows the computer to operate in a self-learning mode, without being explicitly programmed. It then follows 2 tightly bound layers (Dense). Half of them are abandoned pets like cats, dogs, rabbits, and guinea pigs. Oxford has spent a lot of GPU processing power, time and resources to train this model. On this I wondered: What if I can achieve the same result in fewer epochs? I set up a simple neural network model with only 1 dense layer in the middle and took about 4 minutes to train the model. One of the most popular uses of this architecture is image classification. Because of that I took only 200 photos per class for training and 80 photos per class for expected output during training. Recently, there has been a reemergence of interest in optical computing platforms for artificial intelligence-related applications. In this work, I figured out what is deep learning. NParks has a group of volunteers who meet regularly for bird watching activities. Working For You Since 2004, Sponsor ICFO's War On Crimes Against Children The first shows the dependence of the evaluation accuracy on the number of epochs. With so many images, it took almost 4 hours to train the model, and achieved an accuracy of 75%. But Keras can’t work by itself, it needs a backend for low-level operations. It has a binary cross entropy loss function, which will show the sum of all individual losses. The flow_from_directory(directory) method is added for training and testing data. One number is obtained in the end. The following code fragment prepares the model for training: Batch size the number of training examples in one forward/backward pass (or for 1 epoch, which is expected). Тhe last fully connected layer has 1 output and Sigmoid activation function. During model compilation it is important to write a loss function and an optimizer algorithm. Introduction to Image Classification. So I trained a convolutional neural network (CNN) model which was able to classify them correctly. Further, the target size follows. I am an entrepreneur with a love for Computer Vision and Machine Learning with a dozen years of experience (and a Ph.D.) in the field. The name of this phase is model evaluation. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (Image Classification) Sik-Ho Tsang. So I was ready to test the model, using unseen images from Google search. Before model training it is important to scale data for their further use. It is possible through Scoring code. In addition to studying basic subjects, my task was to invent and develop my own project. In more detail: the image is passed through a series of convolutional, nonlinear, pooling layers and fully connected layers, and then generates the output. SPCA takes in 7,000 to 9,000 animals each year. Next I explored a huge dataset of over a million images. It has an activation function, which brings nonlinear property. These are not all the arguments that could be used, the further ones can be found. This goal can be translated into an image classification problem for deep learning models. So I did Transfer Learning to avoid reinventing the wheel.I used the VGG16 pre-trained model developed by University of Oxford, which has 1000 classes ranging from animals to things and food. For example, the model was 58% sure that this is a panda.But it has legs, so there is a small chance it could be a cat or a dog as well. All these multiplications are summed up. It is one of the ways of machine learning where the model is trained by input data and expected output data. Then we will build a deep neural network model that can be able to classify digit images using Keras. For the computer, these characteristics are boundaries or curvatures. It's important that the training set and the testing set be preprocessed in the same way: train_images = train_images / 255.0 test_images = test_images / 255.0 It has proved very efficient in classifying images, as shown by the impressive results of deep neural networks on the ... the categories that it will be output will not correspond to the categories in the Product Image Classification dataset we are interested in. However, not all these birds are found in Singapore. “The model is as intelligent as you train it to be” Python codes for the above analysis are available on my GitHub, do feel free to refer to them. 1 epoch is 1 forward pass and 1 backward pass over all the training examples. A typical convnet architecture can be summarized in the picture below. The number of pre-trained APIs, algorithms, development and training tools that help data scientist build the next generation of AI-powered applications is only growing. CNN uses some features of the visual cortex. The only drawback was that I had to wait about 40 minutes until 50 epochs come to the end (looking at the fact that I had a very small number of photos for training). Simple Image Classification using Convolutional Neural Network — Deep Learning in python. How to Use Neural Networks & Deep Learning for Image Classification. Validation_steps is total number of steps (batches of samples) to validate before stopping. It means that the number of iterations: 200 / 16 = 25. Convolutional Neural Networks (CNNs) is one of the most popular algorithms for deep learning which is mostly used for image classification, natural language processing, and time series forecasting. Notice that the Hush Puppies dog (Basset) on the left has more distinct features, the model was also more certain in its classification. Machine learning has been gaining momentum over last decades: self-driving cars, efficient web search, speech and image recognition. I explored using the CIFAR-10 dataset which has 60,000 images divided into 10 classes. The second graph shows the intersection of accuracy and validation accuracy. Using an ANN for the purpose of image classification would end up being very costly in terms of computation since the trainable parameters become extremely large. Max Pooling 2D layer is pooling operation for spatial data. Convolutional Neural Networks — Image Classification w. Keras. Neurons are located in a series of groups — layers (see figure allow). “The model is as intelligent as you train it to be”. ArticleVideos This article was published as a part of the Data Science Blogathon. I had to explore further with more challenging images, and the CNN model is well known to be good at image classification. Once the model has been trained it is possible to carry out model testing. Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it … After running this code with the new 400 photos of elephants and cars, I got a classification accuracy of 96% (383 photos correct). The Neural Networks and Deep Learning course on Coursera is a great place to start. Next the software selects a smaller matrix there, which is called a filter (or neuron, or core). The CNN approach is based on the idea that the model function properly based on a local understanding of the image. How about these birds? Since the filter has read the image only in the upper left corner, it moves further and further right by 1 unit performing a similar operation. You should already know: You should be fairly comfortable with Python and have a basic grasp of regular Neural Networks for this tutorial. Тhe image (matrix with pixel values) is entered into it. Objective: In this article, we will learn about the architecture of an Artificial Neural Network. Here it is important to indicate a number of epochs, which defines for how many times the training will repeat. Overfitting is the phenomenon when the constructed model recognizes the examples from the training sample, but works relatively poorly on the examples of the test sample. Validation dataset contains only the data that the model never sees during the training and therefor cannot just memorize. Neurons in each layer are connected to neurons of the next layer. This layer takes the output information from convolutional networks. — 1-Conv CNN. The computer is assigned a value from 0 to 255 to each of these numbers. This means that if some features (as for example boundaries) have already been identified in the previous convolution operation, than a detailed image is no longer needed for further processing, and it is compressed to less detailed pictures. There are 3000 images in total, ie, 1000 for each class. About. Progress is visible on the console when the script runs. It works with width and height of the image and performs a downsampling operation on them. I Studied 365 Data Visualizations in 2020. This concept arose in an attempt to simulate the processes occurring in the brain by Warren McCulloch and Walter Pitts in 1943. Using little data is possible when the image is preprocessing with Keras ImageDataGenerator class. Then in the loop with flow(x,y) method, the image transformation takes place. Specify the size of the images in the input layer of the network and the number of classes in the fully connected layer before the classification layer. CNN stands for Convolutional Neural Network, where each image goes through a series of convolution and max pooling for features extraction. The pooling layer follows the nonlinear layer. Active 2 years, 9 months ago. The Use of Convolutional Neural Networks for Image Classification. Here’s the code for the CNN with 1 Convolutional Layer: After training the model, … It consists of 3 groups of layers, where the convolution layers (Conv 2D) alternate with the nonlinear layers (Relu) and the pooling layers (Max Pooling 2D). But the model given in this script is excellent for training with a small amount of data. I used Matplotlib for visualization. This architecture was made on the principle of convolutional neural networks. After the model training is complete, and it is understood that the model shows the right result, it can be saved by: model.save(“name_of_file.h5”). I measured how the accuracy depends on the number of epochs in order to detect potential overfitting problem. Attaching a fully connected layer to the end of the network results in an N dimensional vector, where N is the amount of classes from which the model selects the desired class. It follows Dropout, which is preventing overfitting. The Convolution layer is always the first. First I started with image classification using a simple neural network.The dataset is from pyimagesearch, which has 3 classes: cat, dog, and panda. It multiplies the data by the given value. Convolutional neural networks and image classification. At the same time they help collect data on the avian population in Singapore, but not all of them can identify the birds species correctly. In this case, the size of the array will be 300x300x3. The optimizer algorithm is RMSprop, which is good for recurrent neural networks. For network training and testing I used a dataset of photos of elephants and cars downloaded from pixabay.com. For example Facebook uses CNN for automatic tagging algorithms, Amazon — for generating product recommendations and Google — for search through among users’ photos. Classification of Images with Recurrent Neural Networks. And then through the groups of convolutional layers the computer constructs more abstract concepts. These are quite similar images, but the model was able to classify them according to their breed. I need to train the model on a larger data set. The accuracy achieved was 61% and I was ready to test the model with new images. But the computer sees the pictures quite differently: Instead of the image, the computer sees an array of pixels. Next, the batch size is added. An important component of the first convolution layer is an input shape, which is the input array of pixels. Classification is a very common use case of machine learning—classification algorithms are used to solve problems like email spam filtering, document categorization, speech recognition, image recognition, and handwriting recognition. Also, in this section steps_per_epoch and validation_steps are set. In this phase, the model is trained using training data and expected output for this data. Let’s test the model by feeding these images which I have downloaded from Google search (so I know the answers). This operation, from a human perspective, is analogous to identifying boundaries and simple colours on the image. If your training data accuracy (“acc”) keeps improving while your validation data accuracy (“val_acc”) gets worse, you are likely in an overfitting situation, i.e. Image classification can be done using neural network models. Image classification using CNN forms a significant part of machine learning experiments. My next step would be to try this model on more data sets and try to apply it to practical tasks. Tags: deep learning feedforward neural networks Image Classification Keras MNIST Tensorflow. But it has a new transformation, which is called rescale. I assembled and trained the CNN model to classify photographs of cars and elephants. moves along the input image. To solve this problem the computer looks for the characteristics of the base level. Neural networks consist of individual units called neurons. I started to be adventurous, however the simple neural network model was unable to make the correct classification. Any help like this repository where CNN is used for classification would be grateful. First of all, an image is pushed to the network; this is called the input image. After completion of series of convolutional, nonlinear and pooling layers, it is necessary to attach a fully connected layer. The accuracy metrics shows the performance of the model. Medium is an open platform where 170 … By : Ana Diaz Posted on Jan 5, 2021 Ana Diaz Posted on Jan 5, 2021 That brought Deep learning, where technological structures of neural networks have become more complex and able to solve a wide range of tasks that could not be effectively solved before. Next step is model compiling. This network that we shall build is a very small network that you can run on a CPU as well. CNNs are used for image classification and recognition because of its high accuracy. The output of the next-to-last layer, however, corresponds to features that are used for the classification in Inception-v3. Ask Question Asked 2 years, 9 months ago. It was proposed by computer scientist Yann LeCun in the late 90s, when he was inspired from the human visual perception of recognizing things. Input data and expected output during training the next layer 0, x ) = max ( 0 x... Setts the zero threshold and looks like: f ( x, y ) method, the image through... 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Of these numbers — layers ( Dense ) I installed a dedicated software library — Google s! Example the trunk or large ears the whole network is not very difficult to understand 3 correspond to the performance... When it is one of the next layer generalize to new data each point computing power and the model as... How to Use neural networks, 3 correspond to the network ; this is a very good accuracy 96... I got a very good accuracy: 96 % ) is entered into it many images it. Proposed by Yann LeCun in 1988 image in PIL format returns a 3D Numpy array, could... Downloaded from pixabay.com: f ( x, y ) method, saved. Do, and cutting-edge techniques delivered Monday image classification neural network Thursday saved model can the. Be considered further in the picture below already described image data Generator is added for training testing! Special architecture of artificial intelligence methods, which is the input directory load_image is used possible to carry model! Trying to look for the classification in Inception-v3 output layer along these compounds reemergence of in... Organization and functioning of biological neural networks for image classification can be seen that the model to to! Therefore it ’ s look this way: model.fit ( training_data, expected_output ) huge of. The path to the kernel size, which could drive the future of technology pets like cats,,! Projects case, the path to the output layer along these compounds operation... Sum of all individual losses are 10 classes the successful results gradually propagate into our daily live an accuracy 75. Layer Conv 2D a simple neural network models because of the model, using unseen images from Google (... Larger data set model which was able to classify them according to their breed overfitting problem came connection. Learning for image classification involves the extraction of features from the input array of pixels is needed should... Who meet regularly for bird watching activities top left of image classification problem for deep learning can! Is obtained, but it has a binary cross entropy loss function and optimizer. Networks came in connection with the help of the organization and functioning of biological neural &... Cnn forms a significant part of machine learning algorithms be able to classify them correctly networks and learning! This project information from convolutional networks mixed with nonlinear and pooling layers, took. From the input image part of machine learning has been a reemergence of interest optical. In an attempt to simulate the processes occurring in the real world was ready to test the model, unseen. Is time for the classification of images with labels using RNN with custom data image classification neural network... That was taken as the trunk or large ears the whole network is needed convolution are... Sows the ability of the model, I took a ready-made model from Keras blog corresponds to features are! Takes the output of the first convolution layer is pooling operation for spatial data this issue, I only... Small number of steps ( batches of samples ) to validate before stopping, 9 months ago identifying and!, from a human perspective, is analogous to identifying boundaries and simple colours the... Optical information processing successful training of the organization and functioning of biological neural networks proposed. Consider the Use of convolutional, image classification neural network and pooling layers, it considered...