In this project we use convolution neural network to predict fashion classes from the fashion mnist dataset
Fashion training set consists of 70,000 images divided into 60,000 training and 10,000 testing samples. Dataset sample consists of 28x28 grayscale image, associated with a label from 10 classes.

The 10 classes are as follows:

0 => T-shirt/top
1 => Trouser
2 => Pullover
3 => Dress
4 => Coat
5 => Sandal
6 => Shirt
7 => Sneaker
8 => Bag
9 => Ankle boot
Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255.