import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

#數(shù)據(jù)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.images
trainlabel = mnist.train.labels
testimg = mnist.test.images
testlabel = mnist.test.labels

#打印相關(guān)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)

#輸出幾張數(shù)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()

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