minst的数据集介绍

MINST数据库是由是一个手写数字的数据集,官方网址:http://yann.lecun.com/exdb/mnist/

 > MNIST 数据集来自美国国家标准与技术研究所, National Institute of Standards and Technology (NIST). 训练集 (training set) 由来自 250 个不同人手写的数字构成, 其中 50% 是高中学生, 50% 来自人口普查局 (the Census Bureau) 的工作人员. 测试集(test set) 也是同样比例的手写数字数据.

MINST数据总共有4个包,解压出来的数据如下:

tensorflow{:height=”600px” width=”600px”}

  1. train-images-idx3-ubyte.gz,train-labels-idx1-ubyte.gz:提供了60000张,28*28像素的黑底白字图片用来训练
  2. t10k-images-idx3-ubyte.gz,t10k-labels-idx1-ubyte.gz:提供了10000张,28*28像素的黑底白字图片用来测试

mnist提供的图片有784(28*28)个像素点,把每个像素点的值,组织成一个一维的数组,作为输入参数。形式如下:

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 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.386 0.379 0....... 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]
```

图片的标签以一维数组给出,每个元素代表出现的概率,形式如下:

`[0. 0. 0. 0. 0. 0. 1. 0. 0. 0. ]` 代表数字6


`tensorflow`官方支持数据集的读取,使用代码如下:

``` python
# coding:utf-8
from tensorflow.examples.tutorials.mnist import input_data

minst=input_data.read_data_sets('./data/',one_hot=True)
#打印数据数量
print( "train data size:",minst.train.num_examples)
print( "validation data size:",minst.validation.num_examples)
print( "test data size:",minst.test.num_examples)

#打印数据
print(minst.train.labels[0])
print(minst.train.images[0])

#打印前200行数据
BATCH_SIZE=200
xs,ys=minst.train.next_batch(BATCH_SIZE)
print("xs shape:",xs.shape)
print("ys shape:",ys.shape)

```

输出到控制台结果:

``` cte
train data size: 55000
validation data size: 5000
test data size: 10000
[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
[ 0. 0.....0. 0.3529412 0.5411765 0.9215687 0.9215687 0.9215687 0.9215687 0.9215687 0.9215687 0.9843138 0.9843138 0.9725491 0.9960785 0.9607844 0.9215687 0.74509805 0.08235294 0. 0. 0.....]
xs: [[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.]]
xs shape: (200, 784)
ys: [[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 1. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 1. ... 0. 0. 0.]
[1. 0. 0. ... 0. 0. 0.]]
ys shape: (200, 10)

既然是图片数据,我们就能把他们都还原过去,代码如下:

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#coding:utf-8
import numpy as np
import struct

from PIL import Image
import os

data_file = 'train-images-idx3-ubyte' #需要修改的路径

# It's 47040016B, but we should set to 47040000B
data_file_size = 47040016
data_file_size = str(data_file_size - 16) + 'B'

data_buf = open(data_file, 'rb').read()

magic, numImages, numRows, numColumns = struct.unpack_from(
'>IIII', data_buf, 0)
datas = struct.unpack_from(
'>' + data_file_size, data_buf, struct.calcsize('>IIII'))
datas = np.array(datas).astype(np.uint8).reshape(
numImages, 1, numRows, numColumns)

label_file = 'train-labels-idx1-ubyte' #需要修改的路径

# It's 60008B, but we should set to 60000B
label_file_size = 60008
label_file_size = str(label_file_size - 8) + 'B'

label_buf = open(label_file, 'rb').read()

magic, numLabels = struct.unpack_from('>II', label_buf, 0)
labels = struct.unpack_from(
'>' + label_file_size, label_buf, struct.calcsize('>II'))
labels = np.array(labels).astype(np.int64)

datas_root = './pic/' #需要修改的路径
if not os.path.exists(datas_root):
os.mkdir(datas_root)

for i in range(10):
file_name = datas_root + os.sep + str(i)
if not os.path.exists(file_name):
os.mkdir(file_name)

for ii in range(numLabels):
img = Image.fromarray(datas[ii, 0, 0:28, 0:28])
label = labels[ii]
#不足5位补0版本
file_name = datas_root + os.sep + str(label) + os.sep + \
str(ii).zfill(5) + '.png'
img.save(file_name)

tensorflow

tensorflow

作者

付威

发布于

2019-04-15

更新于

2019-04-15

许可协议

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