tensorflow实现加载mnist数据集

2018-10-15 17:35

阅读：469

mnist作为最基础的图片数据集，在以后的cnn，rnn任务中都会用到

import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data #数据集存放地址，采用0-1编码els testimg = mnist.test.images testlabel = mnist.test.labels #打印相关信息 print(type(trainimg)) print(trainimg.shape,) print(trainlabel.shape,) print(testimg.shape,) print(testlabel.shape,) nsample = 5 randidx = np.random.randint(trainimg.shape[0],size = nsample) #输出几张数字的图 for i in randidx: curr_img = np.reshape(trainimg[i,:],(28,28)) curr_label = np.argmax(trainlabel[i,:]) 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()

程序运行结果如下：

Extracting F:/mnist/data/train-images-idx3-ubyte.gz Extracting F:/mnist/data/train-labels-idx1-ubyte.gz Extracting F:/mnist/data/t10k-images-idx3-ubyte.gz Extracting F:/mnist/data/t10k-labels-idx1-ubyte.gz 55000 10000 <class numpy.ndarray> (55000, 784) (55000, 10) (10000, 784) (10000, 10) 52636th

输出的图片如下：

Training Datalabel is9