python如何加载mnist_tensorflow实现加载mnist数据集

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

(55000, 784)

(55000, 10)

(10000, 784)

(10000, 10)

52636th

输出的图片如下：

Training Datalabel is9

下面还有四张其他的类似图片

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。

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 (55000, 784) (55000, 10) (10000, 784) (10000, 10) 52636th 输出的图片如下： Training Datalabel is9 下面还有四张其他的类似图片 以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。