The image classifier in the previous section didn't perform well. Getting 92.1% on MNIST dataset is relatively easy. Let's see how we can use Convolutional Neural Networks (CNNs) to achieve a much higher accuracy. We will build an image classifier using the same dataset, but with a CNN instead of a single layer neural network.

Create a new python and import the following packages:

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

Define a function to parse the input arguments:

def build_arg_parser(): 
    parser = argparse.ArgumentParser(description='Build a CNN classifier \ 
            using MNIST data') 
    parser.add_argument('--input-dir', dest='input_dir', type=str,  
            default='./mnist_data', help='Directory for storing data') 
    return parser 

Define a function to create values for weights in each layer...