{"id":1604,"date":"2019-03-19T07:42:29","date_gmt":"2019-03-19T07:42:29","guid":{"rendered":"https:\/\/cloudxlab.com\/blog\/?p=1604"},"modified":"2019-04-16T10:52:05","modified_gmt":"2019-04-16T10:52:05","slug":"fashion-mnist-using-machine-learning","status":"publish","type":"post","link":"https:\/\/cloudxlab.com\/blog\/fashion-mnist-using-machine-learning\/","title":{"rendered":"Fashion-MNIST using Machine Learning"},"content":{"rendered":"\n

One of the classic problem that has been used in the Machine Learning world for quite sometime is the MNIST problem. The objective is to identify the digit based on image. But MNIST is not very great problem because we come up with great accuracy even if we are looking at few pixels in the image. So, another common example problem against which we test algorithms is Fashion-MNIST.<\/p>\n\n\n\n

The complete code for this project you can find here : https:\/\/github.com\/cloudxlab\/ml\/tree\/master\/projects\/Fashion-MNIST<\/a><\/p>\n\n\n\n

Fashion-MNIST is a dataset of Zalando’s fashion article images \u2014consisting of a training set of 60,000<\/strong> examples and a test set of 10,000<\/strong> examples. Each instance is a 28×28 grayscale image, associated with a label.<\/p>\n\n\n\n\n\n\n\n

\"Fashion
Fashion-MNIST dataset images<\/figcaption><\/figure>\n\n\n

Objective<\/strong><\/h2>\n\n\n

This work is part of my experiments with Fashion-MNIST dataset using various Machine Learning algorithms\/models. The objective is to identify (predict) different fashion products from the given images using various best possible Machine Learning Models (Algorithms) and compare their results (performance measures\/scores) to arrive at the best ML model. I have also experimented with \u2018dimensionality reduction\u2019 technique for this problem.
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Acknowledgements<\/h2>\n\n\n

I have used Fashion-MNIST dataset for this experiment with Machine Learning. Fashion-MNIST dataset is a collection of fashion articles images provided by Zalando<\/a> . Thanks to Zalando Research for hosting the dataset<\/a>.
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Understanding and Analysing the dataset<\/strong><\/h2>\n\n\n

Fashion MNIST Training dataset consists of 60,000 images and each image has 784 features (i.e. 28×28 pixels). Each pixel is a value from 0 to 255, describing the pixel intensity. 0 for white and 255 for black.<\/p>\n\n\n\n

The class labels for Fashion MNIST are: <\/p>\n\n\n\n
Label\ufeff<\/strong><\/td>Description\ufeff<\/strong><\/td><\/tr>
0<\/td>T-shirt\/top<\/td><\/tr>
1<\/td>Trouser<\/td><\/tr>
2<\/td>Pullover<\/td><\/tr>
3<\/td>Dress<\/td><\/tr>
4<\/td>Coat<\/td><\/tr>
5<\/td>Sandal<\/td><\/tr>
6<\/td>Shirt<\/td><\/tr>
7<\/td>Sneaker<\/td><\/tr>
8<\/td>Bag<\/td><\/tr>
9<\/td>Ankle boot<\/td><\/tr><\/tbody><\/table>\n\n\n\n

Let us have a look at one instance (an article image) of the training dataset.<\/p>\n\n\n\n

\"A
sample image Fashion-MNIST<\/figcaption><\/figure>\n\n\n

Problem Definition<\/strong><\/h2>\n\n\n

The \u2018target\u2019 dataset has 10 class labels, as we can see from above (0 – T-shirt\/top, 1 \u2013 Trouser,,\u2026.9 \u2013 Ankle Boot). <\/p>\n\n\n\n

Given the images of the articles, we need to classify them into one of these classes, hence, it is essentially a \u2018Multi-class Classification\u2019<\/strong> problem.
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We will be using various Classifiers and comparing their results\/scores. <\/p>\n\n\n

Preparing the Data<\/strong><\/h2>\n\n\n

We already have the splitted dataset (training and test) available in ratio 85:15 (60,000:10,000) from Zalando Research, we will use the same for this experiment.<\/p>\n\n\n\n

X_train, X_test, y_train, y_test = trainSet, testSet, trainLabel, testLabel\n\nprint(X_train.shape)\nprint(y_train.shape)\nprint(X_test.shape)\nprint(y_test.shape)\n\n# Output\n(60000, 784)\n(60000,)\n(10000, 784)\n(10000,)<\/code><\/pre>\n\n\n\n
As part of data preparation, following techniques were applied on the dataset:<\/p>\n\n\n