DL3-MINIST数据集简介

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

数据集如果下载失败，则手动直接从网上下载即可

print ("Download and Extract MNIST dataset")
mnist = input_data.read_data_sets('./data/MNIST/', one_hot=True) #参数表示从0开始计数
print()
print (" tpye of 'mnist' is %s" % (type(mnist)))
print (" number of trian data is %d" % (mnist.train.num_examples))
print (" number of test data is %d" % (mnist.test.num_examples))
``
    
     tpye of 'mnist' is <class 'tensorflow.contrib.learn.python.learn.datasets.base.Datasets'>
     number of trian data is 55000
     number of test data is 10000
    


```python
print ("What does the data of MNIST look like?")
trainimg = mnist.train.images
trainlabel = mnist.train.labels
testimg    = mnist.test.images
testlabel  = mnist.test.labels
print
print (" type of 'trainimg' is %s"    % (type(trainimg)))
print (" type of 'trainlabel' is %s"  % (type(trainlabel)))
print (" type of 'testimg' is %s"     % (type(testimg)))
print (" type of 'testlabel' is %s"   % (type(testlabel)))
print (" shape of 'trainimg' is %s"   % (trainimg.shape,))
print (" shape of 'trainlabel' is %s" % (trainlabel.shape,))
print (" shape of 'testimg' is %s"    % (testimg.shape,))
print (" shape of 'testlabel' is %s"  % (testlabel.shape,))

What does the data of MNIST look like?
 type of 'trainimg' is 
 type of 'trainlabel' is 
 type of 'testimg' is 
 type of 'testlabel' is 
 shape of 'trainimg' is (55000, 784)
 shape of 'trainlabel' is (55000, 10)
 shape of 'testimg' is (10000, 784)
 shape of 'testlabel' is (10000, 10)

print ("How does the training data look like?")
nsample = 5
randidx = np.random.randint(trainimg.shape[0], size=nsample)

for i in randidx:
    curr_img   = np.reshape(trainimg[i, :], (28, 28)) # 28 by 28 matrix 
    curr_label = np.argmax(trainlabel[i, :] ) # Label
    plt.matshow(curr_img, cmap=plt.get_cmap('gray'))
    plt.title("" + str(i) + "th Training Data " 
              + "Label is " + str(curr_label))
    print ("" + str(i) + "th Training Data " 
           + "Label is " + str(curr_label))
    plt.show()

How does the training data look like?
33051th Training Data Label is 9

2389th Training Data Label is 6

53386th Training Data Label is 0

38656th Training Data Label is 8

7009th Training Data Label is 8

print ("Batch Learning? ")
batch_size = 128
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
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,))

Batch Learning? 
type of 'batch_xs' is 
type of 'batch_ys' is 
shape of 'batch_xs' is (128, 784)
shape of 'batch_ys' is (128, 10)