这篇文章主要介绍了python中MNIST手写识别数据调用API的示例分析，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。
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下载地址：yann.lecun.com/exdb/mnist/
有4个有用的文件：
train-images-idx3-ubyte: training set images
train-labels-idx1-ubyte: training set labels
t10k-images-idx3-ubyte: test set images
t10k-labels-idx1-ubyte: test set labels
The training set contains 60000 examples, and the test set 10000 examples. 数据集存储是用binary file存储的，黑白图片。
下面给出load数据集的代码：
import os
import struct
import numpy as np
import matplotlib.pyplot as plt
def load_mnist():
'''
Load mnist data
http://yann.lecun.com/exdb/mnist/
60000 training examples
10000 test sets
Arguments:
kind: 'train' or 'test', string charater input with a default value 'train'
Return:
xxx_images: n*m array, n is the sample count, m is the feature number which is 28*28
xxx_labels: class labels for each image, (0-9)
'''
root_path = '/home/cc/deep_learning/data_sets/mnist'
train_labels_path = os.path.join(root_path, 'train-labels.idx1-ubyte')
train_images_path = os.path.join(root_path, 'train-images.idx3-ubyte')
test_labels_path = os.path.join(root_path, 't10k-labels.idx1-ubyte')
test_images_path = os.path.join(root_path, 't10k-images.idx3-ubyte')
with open(train_labels_path, 'rb') as lpath:
# '>' denotes bigedian
# 'I' denotes unsigned char
magic, n = struct.unpack('>II', lpath.read(8))
#loaded = np.fromfile(lpath, dtype = np.uint8)
train_labels = np.fromfile(lpath, dtype = np.uint8).astype(np.float)
with open(train_images_path, 'rb') as ipath:
magic, num, rows, cols = struct.unpack('>IIII', ipath.read(16))
loaded = np.fromfile(train_images_path, dtype = np.uint8)
# images start from the 16th bytes
train_images = loaded[16:].reshape(len(train_labels), 784).astype(np.float)
with open(test_labels_path, 'rb') as lpath:
# '>' denotes bigedian
# 'I' denotes unsigned char
magic, n = struct.unpack('>II', lpath.read(8))
#loaded = np.fromfile(lpath, dtype = np.uint8)
test_labels = np.fromfile(lpath, dtype = np.uint8).astype(np.float)
with open(test_images_path, 'rb') as ipath:
magic, num, rows, cols = struct.unpack('>IIII', ipath.read(16))
loaded = np.fromfile(test_images_path, dtype = np.uint8)
# images start from the 16th bytes
test_images = loaded[16:].reshape(len(test_labels), 784)
return train_images, train_labels, test_images, test_labels再看看图片集是什么样的：
def test_mnist_data():
'''
Just to check the data
Argument:
none
Return:
none
'''
train_images, train_labels, test_images, test_labels = load_mnist()
fig, ax = plt.subplots(nrows = 2, ncols = 5, sharex = True, sharey = True)
ax =ax.flatten()
for i in range(10):
img = train_images[i][:].reshape(28, 28)
ax[i].imshow(img, cmap = 'Greys', interpolation = 'nearest')
print('corresponding labels = %d' %train_labels[i])
if __name__ == '__main__':
test_mnist_data()跑出的结果如下：
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