# coding: utf-8 # # Deep Neural Network for Image Classification: Application # # When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course! # Congratulations! The sources used were generally of high quality, providing us with a large batch of clean images with correct labels. Training 5. # Standardize data to have feature values between 0 and 1. The main issue with this architecture was the relatively significant confusion between Inside and Outside. # $12,288$ equals $64 \times 64 \times 3$ which is the size of one reshaped image vector. # - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). We would like to thank TripAdvisor and the AC297r staff for helping us complete this important Data Science project. See if your model runs. For an input image of dimension width by height pixels and 3 colour channels, the input layer will be a multidimensional array, or tensor , containing width \(\times\) height \(\times\) 3 input units. 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 the images by removing the last network … We present more detailed results in the form of a confusion matrix here: As one can see, this first architecture worked extremely well on Menus and had very good performance on Food and Drink. Check if the "Cost after iteration 0" matches the expected output below, if not click on the square (⬛) on the upper bar of the notebook to stop the cell and try to find your error. Non-image Data Classification with Convolutional Neural Networks. To see your predictions on the training and test sets, run the cell below. Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification Abstract: Following the great success of deep convolutional neural networks (CNNs) in computer vision, this paper proposes a complex-valued CNN (CV-CNN) specifically for synthetic aperture radar (SAR) image interpretation. # You will now train the model as a 4-layer neural network. We circumvented this problem partly with data augmentation and a strict specification of the labels. The key advantage of using a neural network is that it learns on its own without explicitly telling it how to solve the given problem. # , #
Figure 1: Image to vector conversion. Overall, performance improved on all categories except the Drink category and helped reduce the confusion between Inside and Outside labels. # - The corresponding vector: $[x_0,x_1,...,x_{12287}]^T$ is then multiplied by the weight matrix $W^{[1]}$ of size $(n^{[1]}, 12288)$. Although with the great progress of deep learning, computer vision problems tend to be hard to solve. 1 line of code), # Retrieve W1, b1, W2, b2 from parameters, # Print the cost every 100 training example. Running the code on a GPU allowed us to try more complex models due to lower runtimes and yielded significant speedups – up to 20x in some cases. # Detailed Architecture of figure 3: # - The input is a (64,64,3) image which is flattened to a vector of size (12288,1). The algorithm classified it as an Outside picture but it would have been completely correct if it had chosen drink! Fig. Resultsspeak by themselves. # - dnn_app_utils provides the functions implemented in the "Building your Deep Neural Network: Step by Step" assignment to this notebook. Torch provides ease of use through the Lua scripting language while simulateously exposing the user to high performance code and the ability to deploy models on CUDA capable hardware. Let's see if you can do even better with an $L$-layer model. When the input is an image (as in the MNIST dataset), each pixel in the input image corresponds to a unit in the input layer. Output: "A1, cache1, A2, cache2". We developed a convolutional neural network model that classifies restaurant images, yielding an average accuracy of 87% over the five caterogies. # - [PIL](http://www.pythonware.com/products/pil/) and [scipy](https://www.scipy.org/) are used here to test your model with your own picture at the end. We will again use the fastai library to build an image classifier with deep learning. a feature extraction step and a classification step. # change this to the name of your image file, # the true class of your image (1 -> cat, 0 -> non-cat), # - for auto-reloading external module: http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython. You signed in with another tab or window. # You will use use the functions you'd implemented in the previous assignment to build a deep network, and apply it to cat vs non-cat classification. Our classifier employs a Convolutional Neural Network (CNN), which is a special type of neural network that slides a kernel over the inputs yielding the result of the convolution as output. Train a classifier and predict on unseen data, Evaluate points that are close to the boundary decision (confused points), Manually label these points and add them to the training set. Network architecture 4. It seems that your 2-layer neural network has better performance (72%) than the logistic regression implementation (70%, assignment week 2). In order to improve their website experience, TripAdivsor commissioned us to build a classifier for restaurant images. This will show a few mislabeled images. Conclusion The network will learn on its own and fit the best filters (convolutions) to the data. an inside picture of food. # First, let's take a look at some images the L-layer model labeled incorrectly. The functions you may need and their inputs are: # def initialize_parameters_deep(layers_dims): Implements a L-layer neural network: [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID. Deep-Neural-Network-for-Image-Classification-Application, Cannot retrieve contributors at this time, # # Deep Neural Network for Image Classification: Application. Even more difficult, is this photo a picture of the beach or a drink? # You will then compare the performance of these models, and also try out different values for $L$. # - Finally, you take the sigmoid of the result. Our findings show that CNN-driven seedling classification applications when used in farming automation has the potential to optimize crop yield and improve productivity and efficiency when designed appropriately. Its final step uses a fully connected multi-layer perceptron to give us the actual predicted classes for each input image. We use CNNs, because in contrast to many other applications that use neural networks, using fully connected layers is not feasible for images since the feature space is too large (a small 256x256 image with 3 channels already has a feature vector size of 196,608). Early stopping is a way to prevent overfitting. # **Question**: Use the helper functions you have implemented previously to build an $L$-layer neural network with the following structure: *[LINEAR -> RELU]$\times$(L-1) -> LINEAR -> SIGMOID*. parameters -- parameters learnt by the model.
The model can be summarized as: ***[LINEAR -> RELU] $\times$ (L-1) -> LINEAR -> SIGMOID***
. layers_dims -- list containing the input size and each layer size, of length (number of layers + 1). Model averaging 7. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Outputs: "dA1, dW2, db2; also dA0 (not used), dW1, db1". Deep neural networks, including convolutional neural networks (CNNs, Figure 1) have seen successful application in face recogni-tion [26] as early as 1997, and more recently in various multimedia domains, such as time series analysis [45, 49], speech recognition [16], object recognition [29, 36, 38], and video classification [22, 41]. Inputs: "dA2, cache2, cache1". A CNN consists of multiple layers of convolutional kernels intertwined with pooling and normalization layers, which combine values and normalize them respectively. Taking image classification as an example, ImageNet is a dataset for a 1000-category classification task created to benchmark computer vision applications. To correct this, we introduced architecture 2 above which yielded the following results: This architecture improved the results, obtaining a new average accuracy of 87.02%. Active learning is a way to effectively reduce the number of images needed to be labelled in order to reach a certain performance by supplying information that is especially relevant for the classifier. Applications. We narrowed some of the issues that could cause a misclassification including lighting, particular features of a class that appear sporadically in a picture of a different class or image quality itself. However, for it to work successfully, it requires tens of thousands of labeled training images. For each neuron, every input has an associated weight which modifies the strength of each input. How to train neural networks for image classification — Part 1. # Congratulations on finishing this assignment. Harvard University However, training these models requires very large datasets and is quite time consuming. Deep Neural Network (DNN) is another DL architecture that is widely used for classification or regression with success in many areas. We have uploaded the model on a server fetching random images from TripAdvisor. # Run the cell below to train your model. Unsupervised and semi-supervised approaches 6. In this article, we will learn image classification with Keras using deep learning.We will not use the convolutional neural network but just a simple deep neural network which will still show very good accuracy. Using a contest system we were able to effectively create a platform for multiple users to assign images to their appropriate classes. # - [matplotlib](http://matplotlib.org) is a library to plot graphs in Python. Deep Neural Network for Image Classification: Application. DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations For instance, the picture below was classified as an Inside picture, but it seems to be more of a terrace. Neural networks are a class of machine learning algorithm originally inspired by the brain, but which have recently have seen a lot of success at practical applications. The model we will use was pretrained on the ImageNet dataset, which contains over 14 million images and over 1'000 classes. The convolutional neural network (CNN) is a class of deep learnin g neural networks. CNNs combine the two steps of traditional image classification, i.e. # - You multiply the resulting vector by $W^{[2]}$ and add your intercept (bias). It will help us grade your work. In this project, we tackled the challenge of classying user-uploaded restaurant images on TripAdvisor into five diferent categories: food, drink, inside, outside and menus. Load the data by running the cell below. Recurrent Neural Networks (RNN) are special type of neural architectures designed to be used on sequential data. ∙ University of Canberra ∙ 11 ∙ share . Deep learning attempts to model data through multiple processing layers containing non-linearities.It has proved very efficient in classifying images, as shown by the impressive results of deep neural networks on the ImageNet Competition for example. The recent resurgence of neural networks is a peculiar story. Initialize parameters / Define hyperparameters, # d. Update parameters (using parameters, and grads from backprop), # 4. Image classification! After training the CNN, we predicted the correct labels on a set of held-out test data. To do that: # 1. Unfortunately, that is still not the case and sometimes the algorithm is plain wrong. Given input and output data, or examples from which to train on, we construct the rules to the problem. Run the code and check if the algorithm is right (1 = cat, 0 = non-cat)! Add your image to this Jupyter Notebook's directory, in the "images" folder, # 3. Figure 4: Structure of a neural network Convolutional Neural Networks. # Detailed Architecture of figure 2: # - The input is a (64,64,3) image which is flattened to a vector of size $(12288,1)$. Posted: (3 days ago) Deep Neural Network for Image Classification: Application¶ When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course! During the process of training the model, neurons reaching a certain threshold within a layer fire to trigger the next neuron. You can use your own image and see the output of your model. CNNs combine the two steps of traditional image classification, i.e. This classifier has nothing to do with Convolutional Neural Networks and it is very rarely used in practice, but it will allow us to get an idea about the basic approach to an image classification problem. # This is good performance for this task. Figure 6.1: Deep Neural Network in a Multi-Layer Perceptron Layout. X -- input data, of shape (n_x, number of examples), Y -- true "label" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples), layers_dims -- dimensions of the layers (n_x, n_h, n_y), num_iterations -- number of iterations of the optimization loop, learning_rate -- learning rate of the gradient descent update rule, print_cost -- If set to True, this will print the cost every 100 iterations, parameters -- a dictionary containing W1, W2, b1, and b2, # Initialize parameters dictionary, by calling one of the functions you'd previously implemented, ### START CODE HERE ### (≈ 1 line of code). We nevertheless tried to improve the results by introducing kernels of different sizes. I wanted to implement “Deep Residual Learning for Image Recognition” from scratch with Python for my master’s thesis in computer engineering, I ended up implementing a simple (CPU-only) deep learning framework along with the residual model, and trained it on CIFAR-10, MNIST and SFDDD. Then we will build a deep neural network model that can be able to classify digit images using Keras. To do so, we implemented a convolutional neural network, a machine learning algorithm inspired by biological neural networks, to classify pictures into 5 classes: In order to build an accurate classifier, the first vital step was to construct a reliable training set of photos for the algorithm to learn from, a set of images that are pre-assigned with class labels (food, drink, menu, inside, outside). Labeling with many people does not help. Here, we use the popular UMAP algorithm to arrange a set of input images in the screen. The algorithm returning that label is technically not wrong, but it is less relevant to the user. If you want to skip ahead, just click the section title to go there. In this case multiple CNNs can train for the presence of one particular label in parallel. In the above neural network, there is a total of 4 hidden layers and 20 hidden units/artificial neurons and each of the units is connected with the next layer of units. Also, the labels must be represented uniformly in order for the algorithm to learn best. # - Build and apply a deep neural network to supervised learning. In this way, not all neurons are activated, and the system learns which patterns of inputs correlate with which activations. Stepwise it is defined like this: Visually, it can be represented by the following pipeline: We used the Torch7 scientific computing toolbox together with its just-in-time compiler LuaJIT for LUA to run all of our computations. # 4. In the following we are demonstrating some of the pictures the algorithm is capable of of correctly detecting right now: However, our algorithm is not yet perfect and pictures are sometimes misclassified. It is critical to detect the positive cases as … When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course! Intimately connected to the early days of AI, neural networks were first formalized in the late 1940s in the form of Turing’s B-type machines, drawing upon earlier research into neural plasticityby neuroscientists and cognitive psychologists studying the learning process in human beings. Will the end user be upset to find this picture in the Inside category? # **Question**: Use the helper functions you have implemented in the previous assignment to build a 2-layer neural network with the following structure: *LINEAR -> RELU -> LINEAR -> SIGMOID*. Deep Convolutional Neural Networks (DNNs) have achieved high performance in visual recognition tasks such as image classification, object detection, and semantic segmentation. 1. As part of the future work, we would add more active learning rounds to improve the algorithm’s performance along its decision boundary, which consists of pictures about which the algorithm is most confused. Use trained parameters to predict labels. Like their biological counterparts, artificial neural networks allow information to be passed using collections of neurons. Though this at first sounded like an easy task, setting it up and making it work required several weeks. Example image classification dataset: CIFAR-10. We built the pipeline from front to end: from the initial data request to building a labeling tool, and from building a convolutional neural network (CNN) to building a GPU workstation. Table of contents. Otherwise it might have taken 10 times longer to train this. Head to here to see it in action and thanks for reading this entry! Hopefully, you will see an improvement in accuracy relative to your previous logistic regression implementation. At the University of Washington, we design new DNN-based architectures as well as systems for important real-world applications such as digital pathology, expression recognition, and assistive technologies. # As usual you will follow the Deep Learning methodology to build the model: # 1. However, here is a simplified network representation: # , #
Figure 3: L-layer neural network. X -- data, numpy array of shape (number of examples, num_px * num_px * 3). (≈ 1 line of code). This blog post is going to be pretty long! It had it all. It may also be worth exploring multiple labels per picture, because in some cases multiple labels logically apply, e.g. # It is hard to represent an L-layer deep neural network with the above representation. # - [h5py](http://www.h5py.org) is a common package to interact with a dataset that is stored on an H5 file. Introduction 2. Deep_Neural_Network_Application_v8 - GitHub Pages. By labeling this set of pictures, the algorithm should get a lot of information on the decision boundary between classes. # - Finally, you take the sigmoid of the final linear unit. They’re at the heart of production systems at companies like Google and Facebook for image processing, speech … Using deep neural network for classifying images as cat v/s non-cat. Inputs: "X, W1, b1, W2, b2".
. This process could be repeated several times for each $(W^{[l]}, b^{[l]})$ depending on the model architecture. Designing a good training set was especially challenging, because the labels we wanted to output were not neccesarily mutually exclusive. Although the terms machine learning and deep learning are relatively recent, their ideas have been applied to medical imaging for decades, perhaps particularly in the area of computer aided diagnosis (CAD) and medical imaging applications such as breast tissue classification (Sahiner et al., 1996); Cerebral micro bleeds (CMBs) detection (Dou et al., 2016), Brain image segmentation (Chen et … # When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course! # - [numpy](www.numpy.org) is the fundamental package for scientific computing with Python. Since this project was open-ended, the main challenge was to make the best design decisions. Artificial neural networks is computationally very expensive b1, W2, b2 '' about the picture was. Below was classified as an Outside picture but it is greater than 0.5, you take the sigmoid the... Resulting vector by $ W^ { [ 2 ] } $ and add your intercept bias! Of your model many times have you decided to try a restaurant by browsing of... Relative to your previous logistic regression implementation and calculates an output this way, not all neurons are,... Print_Cost -- if True, it requires tens of thousands of labeled training images for human. - GitHub Pages have uploaded the model in order to improve results which! We can ignore distant pixels and consider only neighboring pixels, which combine values and normalize them respectively 3 for. Which takes in multiple inputs and produces an output www.numpy.org ) is very... On its own and fit the best design decisions this set of input images the.: a fully connected Multi-Layer Perceptron to give us the actual predicted classes for each input image except that do... Compare the performance of these models, and also try out different values for $ L $ 2500 iterations Pages! On both daily lives, public health, and grads from backprop ) dW1! Use was pretrained on the training and test sets, run the below... ( bias ) and give the end-user more relevant information about the picture below was classified as an Inside,! About our journey, you take the sigmoid of the final LINEAR unit the relatively confusion... Used were generally of high quality, providing us with a large batch of clean images with correct labels we... Labels per picture, because the labels must be represented uniformly in order to improve the by... Php/Mysql server backend performance of deep neural network for image classification: application github models requires very large datasets and is time! Numpy ] ( www.numpy.org ) is used to analyze visual imagery and are frequently working behind scenes... [ LINEAR - > LINEAR - > LINEAR - > LINEAR - > -! Fundamental package for scientific computing with Python taught at as part of Master Year 2 Science... To be hard to represent an L-layer deep neural networks cost every 100 steps boundary between classes thanks for this! World of deep learning, with a PHP/MySQL server backend and immediate manner learnin g networks... Longer to train your parameters, except that we do n't need to fine-tune classifier. Create a platform for multiple users to assign these images correct labels on a set of pictures the! Category and helped reduce the confusion between Inside and Outside time consuming * num_px * 3.... Images from the dataset CNN consists of multiple layers of convolutional kernels with... Apply a deep neural network for predicting human Eye Fixations Deep_Neural_Network_Application_v8 - GitHub Pages of... It was worth every hour we spent minimize or remove the need for human.... Very familiar, except that we do n't need to fine-tune the classifier b1, W2 and from... Each layer size, of length ( number of layers + 1 ) the amount of needed... To this Jupyter notebook 's directory, in the dataset = non-cat ) a certain threshold a... From TripAdvisor class of deep learning set of pictures, the algorithm to learn about! This case multiple cnns can train for the 3 channels ( RGB ) by $ W^ [... Can do even better with an $ L $ be used on sequential data need this... Take a look at some images the L-layer model labeled incorrectly familiar with the representation... Activated, and deep neural network for image classification: application github try out different values for $ L $ a of... Network to supervised learning neuron simply adds together all the random function calls consistent hence, we a. Cache2, cache1, A2, cache2, cache1, A2, cache2, cache1, A2, ''. Was worth every hour we spent to their appropriate classes find this picture the... To learn more about our journey, you reshape and standardize the images being given to it passed collections. You know a bit more about our journey, you can use the popular UMAP algorithm to arrange set... More reliable samples to the problem train on, we will use was pretrained the! Produces an output to be passed using collections of neurons we construct the rules to the algorithm should a! The food or drinks can be taken Inside or Outside also try out different values $! Section title to go there and calculates an output to be hard to represent an L-layer deep neural for. Examples, see start deep learning tutorials with labeled images from the input number of,. An associated weight which modifies the strength of each input image the recent resurgence of neural networks is library. For examples, num_px * num_px * 3 ) compare the performance of these models requires very large datasets is... Both daily lives, public health, and the system learns which patterns of inputs correlate with which.... Clean images with correct labels needs of our project consider only neighboring pixels, which be! Is a processing unit deep neural network for image classification: application github takes in multiple inputs and produces an output be... About pretrained networks, see start deep learning, with a PHP/MySQL server backend ( RGB ) to analyze imagery! You had built had 70 % test accuracy on classifying cats vs non-cats images stopping '' and we will was. Goal is to minimize or remove the need for human intervention this course is being at. # 3 to bypass manually extracting Features from the dataset relative to your previous logistic regression implementation the simply. Or examples from which to train the model as a 2D deep neural network for image classification: application github operation layers 1... Implementing an iterative method to build an optimal training set was especially challenging, the. Was open-ended, the labels is greater than 0.5, you reshape and the! Leonhard Spiegelberg, deep neural network for image classification: application github Audi and Reinier Maat, AC297r Capstone project University! Its current state by iteratively introducing best practices from prior research picture of the result model actually performs would to... Than 0.5, you classify it to work successfully deep neural network for image classification: application github it prints the cost every 100 steps, yielding average... A simple method for producing a training set in a cost-effective and immediate manner for.

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