“““
mnist_loader
~~~~~~~~~~~~

A?library?to?load?the?MNIST?image?data.??For?details?of?the?data
structures?that?are?returned?see?the?doc?strings?for?‘‘load_data‘‘
and?‘‘load_data_wrapper‘‘.??In?practice?‘‘load_data_wrapper‘‘?is?the
function?usually?called?by?our?neural?network?code.
“““

####?Libraries
#?Standard?library
import?cPickle
import?gzip

#?Third-party?libraries
import?numpy?as?np

def?load_data（）:
????“““Return?the?MNIST?data?as?a?tuple?containing?the?training?data
????the?validation?data?and?the?test?data.

????The?‘‘training_data‘‘?is?returned?as?a?tuple?with?two?entries.
????The?first?entry?contains?the?actual?training?images.??This?is?a
????numpy?ndarray?with?50000?entries.??Each?entry?is?in?turn?a
????numpy?ndarray?with?784?values?representing?the?28?*?28?=?784
????pixels?in?a?single?MNIST?image.

????The?second?entry?in?the?‘‘training_data‘‘?tuple?is?a?numpy?ndarray
????containing?50000?entries.??Those?entries?are?just?the?digit
????values?（0...9）?for?the?corresponding?images?contained?in?the?first
????entry?of?the?tuple.

????The?‘‘validation_data‘‘?and?‘‘test_data‘‘?are?similar?except
????each?contains?only?10000?images.

????This?is?a?nice?data?format?but?for?use?in?neural?networks?it‘s
????helpful?to?modify?the?format?of?the?‘‘training_data‘‘?a?little.
????That‘s?done?in?the?wrapper?function?‘‘load_data_wrapper（）‘‘?see
????below.
????“““
????f?=?gzip.open（‘data/mnist.pkl.gz‘?‘rb‘）
????training_data?validation_data?test_data?=?cPickle.load（f）
????f.close（）
????return?（training_data?validation_data?test_data）

def?load_data_wrapper（）:
????“““Return?a?tuple?containing?‘‘（training_data?validation_data
????test_data）‘‘.?based?on?‘‘load_data‘‘?but?the?format?is?more
????convenient?for?use?in?our?implementation?of?neural?networks.

????In?particular?‘‘training_data‘‘?is?a?list?containing?50000
????2-tuples?‘‘（x?y）‘‘.??‘‘x‘‘?is?a?784-dimensional?numpy.ndarray
????containing?the?input?image.??‘‘y‘‘?is?a?10-dimensional
????numpy.ndarray?representing?the?unit?vector?corresponding?to?the
????correct?digit?for?‘‘x‘‘.

????‘‘validation_data‘‘?and?‘‘test_data‘‘?are?lists?containing?10000
????2-tuples?‘‘（x?y）‘‘.??In?each?case?‘‘x‘‘?is?a?784-dimensional
????numpy.ndarry?containing?the?input?image?and?‘‘y‘‘?is?the
????corresponding?classification?i.e.?the?digit?values?（integers）
????corresponding?to?‘‘x‘‘.

????Obviously?this?means?we‘re?using?slightly?different?formats?for
????the?training?data?and?the?validation?/?test?data.??These?formats
????turn?out?to?be?the?most?convenient?for?use?in?our?neural?network
????code.“““
????tr_d?va_d?te_d?=?load_data（）
????training_inputs?=?[np.reshape（x?（784?1））?for?x?in?tr_d[0]]
????training_results?=?[vectorized_result（y）?for?y?in?tr_d[1]]
????training_data?=?zip（training_inputs?training_results）
????validation_inputs?=?[np.reshape（x?（784?1））?for?x?in?va_d[0]]
????validation_data?=?zip（validation_

?屬性????????????大小?????日期????時(shí)間???名稱
-----------?---------??----------?-----??----
?????文件????17051982??2016-11-12?12:49??data\mnist.pkl.gz
?????文件????????3480??2017-08-18?15:09??mnist_loader.py
?????文件????????6449??2017-08-18?15:36??network.py
?????文件?????????199??2017-08-18?16:26??recognize.py
?????目錄???????????0??2017-08-18?15:09??data\