Building an image classifier using a single-layer neural network

Let's see how to create a single-layer neural network using TensorFlow and use it to build an image classifier. We will be using the MNIST image dataset to build our system. It is a dataset containing images of handwritten digits. Our goal is to build a classifier that can correctly identify the digit in each image.

Create a new Python file and import the following packages:

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

Extract the MNIST image data. The one_hot flag specifies that we will be using one-hot encoding in our labels. It means that if we have n classes, then the label for a given data point will be an array of length n. Each element in this array corresponds to a given class. To specify a class, the value at the corresponding index will be set to 1 and everything else will be 0:

# Get the MNIST data
mnist = input_data.read_data_sets("./mnist_data...