tensorflow MNIST数据集

____tz_zs

关于MNIST数据集的下载和更多介绍：
http://yann.lecun.com/exdb/mnist/
详解 MNIST 数据集

MNIST数据集的操作

这里介绍的是tensorflow提供的一个用来处理MNIST数据集的类，它将会自动的下载并转化MNIST数据的格式，便于初学者学习和练习。# -*- coding: utf-8 -*-
"""
@author: tz_zs

MNIST数据集的操作
"""

from tensorflow.examples.tutorials.mnist import input_data

# 载入MNIST数据集
'''
第一次运行时，会先下载数据集
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting /path/to/MNIST_data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting /path/to/MNIST_data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting /path/to/MNIST_data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting /path/to/MNIST_data/t10k-labels-idx1-ubyte.gz
之后在运行则直接从文件夹提取
Extracting /path/to/MNIST_data/train-images-idx3-ubyte.gz
Extracting /path/to/MNIST_data/train-labels-idx1-ubyte.gz
Extracting /path/to/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting /path/to/MNIST_data/t10k-labels-idx1-ubyte.gz
'''
mnist = input_data.read_data_sets("/path/to/MNIST_data/", one_hot=True)

print("Training data size: ", mnist.train.num_examples) # Training data size: 55000
print("Validating data size: ", mnist.validation.num_examples) # Validating data size: 5000
print("Testing data size: ", mnist.test.num_examples) # Testing data size: 10000

print("Example training data: \n", mnist.train.images[0])
'''
Example training data:
[ 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
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0. 0. 0. 0. 0. 0. 0.
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0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.38039219 0.37647063
0.3019608 0.46274513 0.2392157 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.35294119 0.5411765
0.92156869 0.92156869 0.92156869 0.92156869 0.92156869 0.92156869
0.98431379 0.98431379 0.97254908 0.99607849 0.96078438 0.92156869
0.74509805 0.08235294 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.54901963 0.98431379 0.99607849 0.99607849 0.99607849 0.99607849
0.99607849 0.99607849 0.99607849 0.99607849 0.99607849 0.99607849
0.99607849 0.99607849 0.99607849 0.99607849 0.74117649 0.09019608
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.88627458 0.99607849 0.81568635
0.78039223 0.78039223 0.78039223 0.78039223 0.54509807 0.2392157
0.2392157 0.2392157 0.2392157 0.2392157 0.50196081 0.8705883
0.99607849 0.99607849 0.74117649 0.08235294 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.14901961 0.32156864 0.0509804 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.13333334 0.83529419 0.99607849 0.99607849 0.45098042 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.32941177 0.99607849 0.99607849 0.91764712 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.32941177 0.99607849 0.99607849 0.91764712 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.41568631 0.6156863 0.99607849 0.99607849 0.95294124 0.20000002
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.09803922 0.45882356 0.89411771
0.89411771 0.89411771 0.99215692 0.99607849 0.99607849 0.99607849
0.99607849 0.94117653 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.26666668 0.4666667 0.86274517
0.99607849 0.99607849 0.99607849 0.99607849 0.99607849 0.99607849
0.99607849 0.99607849 0.99607849 0.55686277 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.14509805 0.73333335 0.99215692
0.99607849 0.99607849 0.99607849 0.87450987 0.80784321 0.80784321
0.29411766 0.26666668 0.84313732 0.99607849 0.99607849 0.45882356
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.44313729
0.8588236 0.99607849 0.94901967 0.89019614 0.45098042 0.34901962
0.12156864 0. 0. 0. 0. 0.7843138
0.99607849 0.9450981 0.16078432 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0.66274512 0.99607849 0.6901961 0.24313727 0. 0.
0. 0. 0. 0. 0. 0.18823531
0.90588242 0.99607849 0.91764712 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0.07058824 0.
4000
48627454 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.32941177 0.99607849 0.99607849 0.65098041 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.54509807 0.99607849 0.9333334 0.22352943 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.82352948 0.98039222 0.99607849 0.65882355 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.94901967 0.99607849 0.93725497 0.22352943 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.34901962 0.98431379 0.9450981 0.33725491 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.01960784 0.80784321 0.96470594 0.6156863 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.01568628 0.45882356 0.27058825 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. ]
'''

print("Example training data label: ",
mnist.train.labels[0]) # Example training data label: [ 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]

# mnist.train.next_batch可以取出一部分数据作为一个batch
batch_size = 100
xs, ys = mnist.train.next_batch(batch_size)
print("X shape：", xs.shape) # X shape： (100, 784)
print("Y shape: ", ys.shape) # Y shape: (100, 10)