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()
posted on 2021-01-12 22:54 yangliuliu 阅读(88) 评论(0) 编辑 收藏 举报