学习进度笔记6

观看Tensorflow案例实战视频课程06 Mnist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
import input_data

print("packs loaded")

print("Download and Extract MNIST dataset")
mnist=input_data.read_data_sets('data/',one_hot=True)
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))

#What does the data of MNIST look like?
print("What does the data of MNIST look like?")
trainimg=mnist.train.images
trainlabel=mnist.train.lables
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,))

#How does the training data look like?
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()

#Batch Learning?
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,))