tensorflow实现MNIST手写数字识别演示

一、加载数据

import tensorflow as tf  #导入tensorflow库
mnist=tf.keras.datasets.mnist  
(x_train,y_train),(x_test,y_test)=mnist.load_data()  #加载数据

 如果无法加载数据的话，如类似出现如下图所示问题，则可以根据下载地址，将mnist.npz文件下载到本地。

然后通过以下代码加载本地数据文件

import tensorflow as tf
import numpy as np
def load_data(path):
    with np.load(path) as f:
        x_train, y_train = f['x_train'], f['y_train']
        x_test, y_test = f['x_test'], f['y_test']
        return (x_train, y_train), (x_test, y_test)

(x_train, y_train),(x_test, y_test) = load_data(path="../data/mnist.npz") #mnist的本地路径

x_train表示训练数据集，y_train表示训练数据集对应的结果；x_test表示测试数据集，y_test表示测试集对应的结果。加载完成后，可以通过以下代码来检查一下

x_train.shape