This tutorial is targeted to individuals who are new to CNTK and to machine learning. We assume you have completed or are familiar with CNTK 101 and 102. In this tutorial, we will download and pre-process the MNIST digit images to be used for building different models to recognize handwritten digits. We will extend CNTK 101 and 102 to be applied to this data set. Additionally we will introduce a convolutional network to achieve superior performance. This is the first example, where we will train and evaluate a neural network based model on read real world data.
CNTK 103 tutorial is divided into multiple parts:
In [1]:
# Import the relevant modules to be used later
from __future__ import print_function
import gzip
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import os
import shutil
import struct
import sys
try:
from urllib.request import urlretrieve
except ImportError:
from urllib import urlretrieve
# Config matplotlib for inline plotting
%matplotlib inline
We will download the data into local machine. The MNIST database is a standard handwritten digits that has been widely used for training and testing of machine learning algorithms. It has a training set of 60,000 images and a test set of 10,000 images with each image being 28 x 28 pixels. This set is easy to use visualize and train on any computer.
In [2]:
# Functions to load MNIST images and unpack into train and test set.
# - loadData reads image data and formats into a 28x28 long array
# - loadLabels reads the corresponding labels data, 1 for each image
# - load packs the downloaded image and labels data into a combined format to be read later by
# CNTK text reader
def loadData(src, cimg):
print ('Downloading ' + src)
gzfname, h = urlretrieve(src, './delete.me')
print ('Done.')
try:
with gzip.open(gzfname) as gz:
n = struct.unpack('I', gz.read(4))
# Read magic number.
if n[0] != 0x3080000:
raise Exception('Invalid file: unexpected magic number.')
# Read number of entries.
n = struct.unpack('>I', gz.read(4))[0]
if n != cimg:
raise Exception('Invalid file: expected {0} entries.'.format(cimg))
crow = struct.unpack('>I', gz.read(4))[0]
ccol = struct.unpack('>I', gz.read(4))[0]
if crow != 28 or ccol != 28:
raise Exception('Invalid file: expected 28 rows/cols per image.')
# Read data.
res = np.fromstring(gz.read(cimg * crow * ccol), dtype = np.uint8)
finally:
os.remove(gzfname)
return res.reshape((cimg, crow * ccol))
def loadLabels(src, cimg):
print ('Downloading ' + src)
gzfname, h = urlretrieve(src, './delete.me')
print ('Done.')
try:
with gzip.open(gzfname) as gz:
n = struct.unpack('I', gz.read(4))
# Read magic number.
if n[0] != 0x1080000:
raise Exception('Invalid file: unexpected magic number.')
# Read number of entries.
n = struct.unpack('>I', gz.read(4))
if n[0] != cimg:
raise Exception('Invalid file: expected {0} rows.'.format(cimg))
# Read labels.
res = np.fromstring(gz.read(cimg), dtype = np.uint8)
finally:
os.remove(gzfname)
return res.reshape((cimg, 1))
def try_download(dataSrc, labelsSrc, cimg):
data = loadData(dataSrc, cimg)
labels = loadLabels(labelsSrc, cimg)
return np.hstack((data, labels))
In [3]:
# URLs for the train image and labels data
url_train_image = 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz'
url_train_labels = 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz'
num_train_samples = 60000
print("Downloading train data")
train = try_download(url_train_image, url_train_labels, num_train_samples)
url_test_image = 'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz'
url_test_labels = 'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'
num_test_samples = 10000
print("Downloading test data")
test = try_download(url_test_image, url_test_labels, num_test_samples)
In [4]:
# Plot a random image
sample_number = 5001
plt.imshow(train[sample_number,:-1].reshape(28,28), cmap="gray_r")
plt.axis('off')
print("Image Label: ", train[sample_number,-1])
Save the images in a local directory. While saving the data we flatten the images to a vector (28x28 image pixels becomes an array of length 784 data points).
The labels are encoded as 1-hot encoding (label of 3 with 10 digits becomes 0001000000, where the first index corresponds to digit 0 and the last one corresponds to digit 9.
In [5]:
# Save the data files into a format compatible with CNTK text reader
def savetxt(filename, ndarray):
dir = os.path.dirname(filename)
if not os.path.exists(dir):
os.makedirs(dir)
if not os.path.isfile(filename):
print("Saving", filename )
with open(filename, 'w') as f:
labels = list(map(' '.join, np.eye(10, dtype=np.uint).astype(str)))
for row in ndarray:
row_str = row.astype(str)
label_str = labels[row[-1]]
feature_str = ' '.join(row_str[:-1])
f.write('|labels {} |features {}\n'.format(label_str, feature_str))
else:
print("File already exists", filename)
In [6]:
# Save the train and test files (prefer our default path for the data)
data_dir = os.path.join("..", "Examples", "Image", "DataSets", "MNIST")
if not os.path.exists(data_dir):
data_dir = os.path.join("data", "MNIST")
print ('Writing train text file...')
savetxt(os.path.join(data_dir, "Train-28x28_cntk_text.txt"), train)
print ('Writing test text file...')
savetxt(os.path.join(data_dir, "Test-28x28_cntk_text.txt"), test)
print('Done')
Suggested Explorations
One can do data manipulations to improve the performance of a machine learning system. I suggest you first use the data generated so far and run the classifier in subsequent parts of the CNTK 103 tutorial series. Once you have a baseline with classifying the data in its original form, now use the different data manipulation techniques to further improve the model.
There are several ways data alterations can be performed. CNTK readers automate a lot of these actions for you. However, to get a feel for how these transforms can impact training and test accuracies, I strongly encourage individuals to try one or more of data perturbation.
permute_indices = np.random.permutation(train.shape[0]). Then run Part B of the tutorial with this newly permuted data.numpy.random) to the training images.
In [ ]: