CNTK API文档翻译(4)——MNIST数据加载

本教程针对机器学习和CNTK新手，本教程的前提是你已经完成了本系列的第一个案例和第二个案例。在本教程中，我们将下载和预处理MNIST图像，以便用于建立不同的手书数字图像识别模型。在之后的三期教程中，我们会把第一期和第二期的方法用于本数据集，还会引入卷积神经网络来获取更好的表现。这是我们使用真实的数据进行训练和评估的第一个例子。

本小系列被分成了四个部分：

第一部分熟悉本教程中会被用到的MNIST数据集（MNIST数据集详情请看我的Python与人工神经网络第三期）

后面三个部分会使用不同类型的神经网络来处理MNIST数据

# 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

数据下载

我们需要把MNIST数据下载到本机。MNIST数据集是一个标准的手书图片，他被广泛用于训练和测试机器学习算法。数据集中包含60000个训练图片和10000个测试图片，每个图片大小是28*28像素，这个数据集能够很方便的在各种电脑上查看和训练。

# 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))

下载

# 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)

查看/可视化

# 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])

保存图片

在本地文件夹中保存图片：保存图片时我们把图片降为成一个矢量（28*28的图片变成一个长784的数组）



标签使用一位有效编码（One-Hot Encoding，上图是3，会被标记成0001000000，守卫表示0，最后一位表示9）.

# 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)

# 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')

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