mnist作為最基礎的圖片數據集，在以后的cnn，rnn任務中都會用到

import numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltfrom tensorflow.examples.tutorials.mnist import input_data#數據集存放地址，采用0-1編碼mnist = input_data.read_data_sets('F:/mnist/data/',one_hot = True)print(mnist.train.num_examples)print(mnist.test.num_examples)trainimg = mnist.train.imagestrainlabel = mnist.train.labelstestimg = mnist.test.imagestestlabel = mnist.test.labels#打印相關信息print(type(trainimg))print(trainimg.shape,)print(trainlabel.shape,)print(testimg.shape,)print(testlabel.shape,)nsample = 5randidx = np.random.randint(trainimg.shape[0],size = nsample)#輸出幾張數字的圖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()

程序運行結果如下：

Extracting F:/mnist/data/train-images-idx3-ubyte.gzExtracting F:/mnist/data/train-labels-idx1-ubyte.gzExtracting F:/mnist/data/t10k-images-idx3-ubyte.gzExtracting F:/mnist/data/t10k-labels-idx1-ubyte.gz5500010000<class 'numpy.ndarray'>(55000, 784)(55000, 10)(10000, 784)(10000, 10)52636th 

輸出的圖片如下：

Training Datalabel is9

下面還有四張其他的類似圖片

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