Chapter 4: Classification

Activity 11: Linear Regression Classifier – Two-Class Classifier

Solution

1. Import the required dependencies:

   import struct
   import numpy as np
   import gzip
   import urllib.request
   import matplotlib.pyplot as plt
   from array import array
   from sklearn.linear_model import LinearRegression

2. Load the MNIST data into memory:

   with gzip.open('train-images-idx3-ubyte.gz', 'rb') as f:
       magic, size, rows, cols = struct.unpack(">IIII", f.read(16))
   
       img = np.array(array("B", f.read())).reshape((size, rows, cols))
   
   
   with gzip.open('train-labels-idx1-ubyte.gz', 'rb') as f:
       magic, size = struct.unpack(">II", f.read(8))
       labels = np.array(array("B", f.read()))
   
   
   with gzip.open('t10k-images-idx3-ubyte.gz', 'rb') as f:
       magic, size, rows, cols = struct.unpack(">IIII", f.read(16))
   
       img_test = np.array(array("B", f.read())).reshape((size, rows, cols))
   
   
   with gzip.open('t10k-labels-idx1-ubyte.gz', 'rb') as f:
       magic, size = struct.unpack(">II", f.read(8))
      ...