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  • tensorflow学习05(Mnist数据集)

    今天我主要学习了Mnist数据集的大致使用流程以及如何使用Mnist数据集

    1、导入工具包

    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")

    2、输出Mnist数据集个数

    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))

    3、输出Mnist数据集种类及特征

    # What does the data of MNIST look like? 
    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,))

    4、输出训练数据种类及特征

    # 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()
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  • 原文地址:https://www.cnblogs.com/yang2000/p/14535063.html
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