tensorflow实现加载mnist数据集_Python

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

									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