Hoping you are having a great day. If not lets make it worse, I am here with a new post.
As we talked about before, machine-learning uses input data called the
Features and output data called the
Labels to learn the model algorithm from.
So, first of all, we need lots of examples to train a neural network to recognize articles of clothing. Remember, an
example is a feature label pair that we feed to the training loop.
In this case, the feature would be the input image and the label would be the correct output that specifies the piece of clothing the image depicts. Fortunately, such a dataset already exists. It's called the
Fashion MNIST dataset.
Given an input image, these are our possible label outputs.
In total, the Fashion-MNIST dataset contains
70,000 images which is plenty for us to get started with.
Remember, each image is
28 by 28 gray-scale pixels, so each image is 784 bytes.
So our job is to create a neural network that takes the 784 bytes as input, and then identifies which of the 10 different items of clothing the image represents.
Lets make our neural network . Jump in.