1，基本概念
MNIST是一個非常有名的手寫體數字識別數據集，在很多資料中，這個數據集都會被用作深度學習的入門樣例。而TensorFlow的封裝讓使用MNIST數據集變得更加方便。MNIST數據集是NIST數據集的一個子集，MNIST 數據集可在 http://yann.lecun.com/exdb/mnist/ 獲取, 它包含了四個部分:
（1）Training set images: train-images-idx3-ubyte.gz (9.9 MB, 解壓后 47 MB, 包含 60,000 個樣本)
（2）Training set labels: train-labels-idx1-ubyte.gz (29 KB, 解壓后 60 KB, 包含 60,000 個標簽)
（3）Test set images: t10k-images-idx3-ubyte.gz (1.6 MB, 解壓后 7.8 MB, 包含 10,000 個樣本)
（4）Test set labels: t10k-labels-idx1-ubyte.gz (5KB, 解壓后 10 KB, 包含 10,000 個標簽)
2，代碼解讀

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data#下載mnist數據集并查看大小
mnist = input_data.read_data_sets('data',one_hot = True)
print("type of mnist is %s" %(type(mnist)))
print("number of trian data is %d" %(mnist.train.num_examples))#訓練集樣本個數
print("number of test data is %d" %(mnist.test.num_examples))#測試集樣本個數#mnist數據集具體信息（樣本，標簽）
trainimg = mnist.train.images
trainlabel = mnist.train.labels
testimg =  mnist.test.images
testlabel =  mnist.test.labelsprint("type of trainimg is %s" %(type(trainimg))) #訓練集樣本的類型
print("type of trainlabel is %s" %(type(trainlabel)))#訓練標簽的類型
print("type of testimg is %s" %(type(testimg)))
print("type of testlabel is %s" %(type(testlabel)))print("shape of trainimg is %s" %(trainimg.shape,))#訓練樣本的個數，單個樣本像素點的個數（28*28）
print("shape of trainlabel is %s" %(trainlabel.shape,))#訓練標簽的個數，單個樣本可能類別的個數（10）
print("shape of testimg is %s" %(testimg.shape,))
print("shape of testlabel is %s" %(testlabel.shape,))

運行結果：

#展示訓練集樣本的實例
nsample=3
randidx = np.random.randint(trainimg.shape[0],size=nsample)for i in randidx:cur_img = np.reshape(trainimg[i,:],(28,28))cur_label = np.argmax(trainlabel[i,:])plt.matshow(cur_img,cmap = plt.get_cmap('gray'))plt.title(""+str(i)+"th Training Data"+"Label is"+str(cur_label))plt.show()

運行結果：

#Batch Learning
batch_size = 128  #batch大小
batch_xs, batch_ys = mnist.train.next_batch(batch_size) #訓練集batch中樣本的個數（batch_xs），相應標簽的個數（batch_ys）print("type of batch_xs is %s" %(type(batch_xs)))
print("type of batch_ys is %s" %(type(batch_ys)))print("shape of batch_xs is %s" %(batch_xs.shape,))
print("shape of batch_ys is %s" %(batch_ys.shape,))

運行結果：

總結

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