加载数据包

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
print("packs loaded")

解压、读取数据包

print("Download and Extract MNIST dataset")
mnist = input_data.read_data_sets('data/',one_hot=True)
print
print("type of 'mnist' is %s" %(type(mnist)))
print("number of train data is %d" %(mnist.train.num_examples))
print("number of test data is %d" %(mnist.test.num_examples))

观察数据集

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,))

训练集数据元素的直观展示

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()

获得一批数据并观察数据类型和形状

print("Batch Learning")
batch_size = 100
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,))