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。
创新互联建站-专业网站定制、快速模板网站建设、高性价比新丰网站开发、企业建站全套包干低至880元,成熟完善的模板库,直接使用。一站式新丰网站制作公司更省心,省钱,快速模板网站建设找我们，业务覆盖新丰地区。费用合理售后完善，十年实体公司更值得信赖。MNIST数据集比较小，一般入门机器学习都会采用这个数据集来训练
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：
感谢你能够认真阅读完这篇文章，希望小编分享的“python中MNIST手写识别数据调用API的示例分析”这篇文章对大家有帮助，同时也希望大家多多支持创新互联，关注创新互联行业资讯频道，更多相关知识等着你来学习!