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 laterfrom __future__ import print_functionimport gzipimport matplotlib.image as mpimgimport matplotlib.pyplot as pltimport numpy as npimport osimport shutilimport structimport systry:     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 dataurl_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 = 60000print("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 = 10000print("Downloading test data")test = try_download(url_test_image, url_test_labels, num_test_samples)

• 查看/可视化

# Plot a random imagesample_number = 5001plt.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 readerdef 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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