In this section, we will do a complete example of implementing a CNN for digit classification using the MNIST dataset. We will build a simple model of two convolution layers and fully connected layers.

Let's start off by importing the libraries that will be needed for this implementation:

%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn.metrics import confusion_matrix
import math

Next, we will use TensorFlow helper functions to download and preprocess the MNIST dataset as follows:

from tensorflow.examples.tutorials.mnist import input_data
mnist_data = input_data.read_data_sets('data/MNIST/', one_hot=True)

Output:
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting data/MNIST/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting data/MNIST/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting data/MNIST/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data/MNIST/t10k-labels-idx1-ubyte.gz

print("- Number of images in the training set:		{}".format(len(mnist_data.train.labels)))
print("- Number of images in the test set:		{}".format(len(mnist_data.test.labels)))
print("- Number of images in the validation set:	{}".format(len(mnist_data.validation.labels)))

- Number of images in the training set: 55000
- Number of images in the test set: 10000
- Number of images in the validation set: 5000

mnist_data.test.cls_integer = np.argmax(mnist_data.test.labels, axis=1)

# Default size for the input monocrome images of MNIST
image_size = 28

# Each image is stored as vector of this size.
image_size_flat = image_size * image_size

# The shape of each image
image_shape = (image_size, image_size)

# All the images in the mnist dataset are stored as a monocrome with only 1 channel
num_channels = 1

# Number of classes in the MNIST dataset from 0 till 9 which is 10
num_classes = 10

Next, we need to define a helper function to plot some images from the dataset. This helper function will plot the images in a grid of nine subplots:

def plot_imgs(imgs, cls_actual, cls_predicted=None):
    assert len(imgs) == len(cls_actual) == 9
    
    # create a figure with 9 subplots to plot the images.
    fig, axes = plt.subplots(3, 3)
    fig.subplots_adjust(hspace=0.3, wspace=0.3)

    for i, ax in enumerate(axes.flat):
        # plot the image at the ith index
        ax.imshow(imgs[i].reshape(image_shape), cmap='binary')

        # labeling the images with the actual and predicted classes.
        if cls_predicted is None:
            xlabel = "True: {0}".format(cls_actual[i])
        else:
            xlabel = "True: {0}, Pred: {1}".format(cls_actual[i], cls_predicted[i])

        # Remove ticks from the plot.
        ax.set_xticks([])
        ax.set_yticks([])
        
        # Show the classes as the label on the x-axis.
        ax.set_xlabel(xlabel)
        
    
    plt.show()

Let's plot some images from the test set and see what it looks like:

# Visualizing 9 images form the test set.
imgs = mnist_data.test.images[0:9]

# getting the actual classes of these 9 images
cls_actual = mnist_data.test.cls_integer[0:9]

#plotting the images
plot_imgs(imgs=imgs, cls_actual=cls_actual)

Here is the output: