【深度学习】【python】下载和读取MINIS数据集 中文注释版

注释版"/>

【深度学习】【python】下载和读取MINIS数据集 中文注释版

【深度学习】【python】下载和读取MINIS数据集 中文注释版

环境要求
- python3.5
- tensorflow 1.4
- pytorch 0.2.0

这次的只需要python3.5即可

程序如下:

#!/usr/bin/env python
# -*- coding: utf-8 -*-"""下载和读取 MNIST 数据集."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import numpy
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
SOURCE_URL = '/'def maybe_download(filename, work_directory):"""检查是否下载数据集;否则下载;"""#如果没有这个文件夹则创建;if not os.path.exists(work_directory):os.mkdir(work_directory)#将这个文件夹名加上数据集文件名:如'./MINIST/minist-1.gz';filepath = os.path.join(work_directory, filename)#如果不存在则用urllib下载;if not os.path.exists(filepath):filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)statinfo = os.stat(filepath)print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')return filepathdef _read32(bytestream):"""将读入的字节流转化为numpy格式;"""#设置dtype型变量dt;dt = numpy.dtype(numpy.uint32).newbyteorder('>')#按数据格式dt转换数据;注意read方式会“取出”bytestream内的数据;return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]def extract_images(filename):"""将所有图片转化为一个 4D uint8 的 numpy 矩阵[index, y, x, depth]."""#转化gzip格式的文件print('Extracting', filename)with gzip.open(filename) as bytestream:#magic变量存储了图像头信息;magic = _read32(bytestream)#校验图像数据是否符合要求;if magic != 2051:raise ValueError('Invalid magic number %d in MNIST image file: %s' %(magic, filename))#获取图像数目;  num_images = _read32(bytestream)#获取图像行宽;rows = _read32(bytestream)#获取图像列宽;cols = _read32(bytestream)#全读入缓存区buf;buf = bytestream.read(rows * cols * num_images)#转化为numpy格式;data = numpy.frombuffer(buf, dtype=numpy.uint8)#rashape为4D uint8 的 numpy 矩阵;data = data.reshape(num_images, rows, cols, 1)return datadef dense_to_one_hot(labels_dense, num_classes=10):"""将类别标签从标量转化为one-hot向量."""#此乃label数目;num_labels = labels_dense.shape[0]#生成一个数组,等价于2D one-hot矩阵展开为1D后1的位置;index_offset = numpy.arange(num_labels) * num_classes#生成一个存储了num_labels个one-hot向量(num_classes维)的2D零矩阵;labels_one_hot = numpy.zeros((num_labels, num_classes))#将对应类别的位置的0设为1;labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1return labels_one_hotdef extract_labels(filename, one_hot=False):"""将标签输出为 1D uint8 的 numpy 数组 [index]."""#转化gzip格式的文件print('Extracting', filename)with gzip.open(filename) as bytestream:#magic变量存储了图像头信息;magic = _read32(bytestream)#校验图像数据是否符合要求;if magic != 2049:raise ValueError('Invalid magic number %d in MNIST label file: %s' %(magic, filename))#标签数目;num_items = _read32(bytestream)#读取标签到缓存区;buf = bytestream.read(num_items)#转化为numpy格式;labels = numpy.frombuffer(buf, dtype=numpy.uint8)#如果要求转换为one-hot则调用dense_to_one_hot()转换;if one_hot:return dense_to_one_hot(labels)return labelsclass DataSet(object):"""封装一个DataSet类方便管理数据"""def __init__(self, images, labels, fake_data=False):if fake_data:self._num_examples = 10000else:#断言;要求images数目等于labels数目;assert images.shape[0] == labels.shape[0], ("images.shape: %s labels.shape: %s" % (images.shape,labels.shape))#设置图像数目;self._num_examples = images.shape[0]# reshape：将[num examples, rows, columns, depth] 转化为 [num examples, rows*columns] (assuming depth == 1)assert images.shape[3] == 1images = images.reshape(images.shape[0],images.shape[1] * images.shape[2])# 归一化: 转换 [0, 255] -> [0.0, 1.0].images = images.astype(numpy.float32)images = numpy.multiply(images, 1.0 / 255.0)self._images = imagesself._labels = labelsself._epochs_completed = 0self._index_in_epoch = 0@propertydef images(self):return self._images@propertydef labels(self):return self._labels@propertydef num_examples(self):return self._num_examples@propertydef epochs_completed(self):return self._epochs_completeddef next_batch(self, batch_size, fake_data=False):"""从当前数据集返回下一批次`batch_size`的样本."""#生成伪数据if fake_data:#伪图像fake_image_1*784;fake_image = [1.0 for _ in xrange(784)]fake_label = 0#伪图像+伪标签;return [fake_image for _ in xrange(batch_size)], [fake_label for _ in xrange(batch_size)]#当前epoch返回样本的开始位置;          start = self._index_in_epoch#设置下一次epoch返回样本的开始位置;self._index_in_epoch += batch_size#本次epoch返回样本的结束位置超出了当前样本数;解决方式:又回到开始;if self._index_in_epoch > self._num_examples:#结束epochself._epochs_completed += 1#先打乱整个数据perm = numpy.arange(self._num_examples)numpy.random.shuffle(perm)self._images = self._images[perm]self._labels = self._labels[perm]#从头又来一次;start = 0self._index_in_epoch = batch_size#断言,batch_size不超出样本数;assert batch_size <= self._num_examples#设置结束位置;end = self._index_in_epoch#返回start:end位置的images+labels;return self._images[start:end], self._labels[start:end]def read_data_sets(train_dir, fake_data=False, one_hot=False):"""相当于本文件的主函数"""#最终要返回的多个DataSet的抽象DataSets;class DataSets(object):pass#data_sets = DataSets()#只需要伪数据;if fake_data:data_sets.train = DataSet([], [], fake_data=True)data_sets.validation = DataSet([], [], fake_data=True)data_sets.test = DataSet([], [], fake_data=True)return data_sets#需要的文件;TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'TEST_IMAGES = 't10k-images-idx3-ubyte.gz'TEST_LABELS = 't10k-labels-idx1-ubyte.gz'VALIDATION_SIZE = 5000#完成下载;完成数据读取;local_file = maybe_download(TRAIN_IMAGES, train_dir)train_images = extract_images(local_file)local_file = maybe_download(TRAIN_LABELS, train_dir)train_labels = extract_labels(local_file, one_hot=one_hot)local_file = maybe_download(TEST_IMAGES, train_dir)test_images = extract_images(local_file)local_file = maybe_download(TEST_LABELS, train_dir)test_labels = extract_labels(local_file, one_hot=one_hot)validation_images = train_images[:VALIDATION_SIZE]validation_labels = train_labels[:VALIDATION_SIZE]train_images = train_images[VALIDATION_SIZE:]train_labels = train_labels[VALIDATION_SIZE:]#训练数据data_sets.train = DataSet(train_images, train_labels)data_sets.validation = DataSet(validation_images, validation_labels)#测试数据data_sets.test = DataSet(test_images, test_labels)return data_sets