We load the MNIST data into our session from  Keras module with mnist.load_data(). After doing so, we will print the shape, size of the dataset as well as the number of classes and unique labels in the dataset.

(X_train, y_train), (X_test, y_test) =  mnist.load_data()

print('Size of the training_set: ', X_train.shape)
print('Size of the test_set: ', X_test.shape)
print('Shape of each image: ', X_train[0].shape)
print('Total number of classes: ', len(np.unique(y_train)))
print('Unique class labels: ', np.unique(y_train))

We have a dataset with 10 different classes and 60000 images with each image having a shape of 28*28 and each class having 6000 images.

Let's plot and see what the handwritten images look like...

# Plot of 9 random images
for i in range(0, 9):
    plt.subplot(331+i) # plot of 3 rows and 3 columns
    plt.axis('off') # turn off axis
    plt.imshow(X_train[i], cmap='gray') # gray scale

Let's plot a handwritten digit from each class...

# plotting image from each class
fig=plt.figure(figsize=(8, 4))
columns = 5
rows = 2
for i in range(0, rows*columns):
    fig.add_subplot(rows, columns, i+1)
    plt.title(str(i)) # label 
    plt.axis('off') # turn off axis
    plt.imshow(X_train[np.where(y_train==i)][0], cmap='gray') # gray scale
plt.show()

Look at the maximum and the minimum pixel value in the dataset...

print('Maximum pixel value in the training_set: ', np.max(X_train))
print('Minimum pixel value in the training_set: ', np.min(X_train))

We see that the maximum pixel value in the dataset is 255 and the minimum is 0.