CNN入门mnist数据集运行环境搭建（安装Python，Pycharm,Anaconda,Tensorflow,CNN代码）_dataset.mnist怎么安装

安装环境运行大致步骤：

1. Python安装：选择3.8，安装教程具体可查看：https://blog.csdn.net/liming89/article/details/109632064?ops_request_misc=&request_id=&biz_id=102&utm_term=pycharm%E5%AE%89%E8%A3%85%E6%95%99%E7%A8%8B&utm_medium=distribute.pc_search_result.none-task-blog-2_allsobaiduweb~default-7-.pc_search_result_before_js&spm=1018.2226.3001.4187
   下载官网：https://www.python.org/downloads/

2. Pycharm 编辑器下载安装：下载官网：
   https://www.jetbrains.com/pycharm/download/#section=windows
   安装教程与Python安装教程在一起

3. 环境变量配置：配置教程：
   https://www.cnblogs.com/huangbiquan/p/7784533.html

4. 安装Anaconda：选择Anaconda3,安装教程具体可查看:
   https://blog.csdn.net/u010210864/article/details/94580873
   下载官网：https://www.anaconda.com/products/individual
   

5. 按步骤4教程安装Tensorflow:

6. 试运行CNN训练mnist数据集的代码：

# -*- coding: utf-8 -*-
# 使用卷积神经网络训练mnist数据集
import gzip

from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, Flatten, Reshape
import numpy as np
from sklearn.metrics import classification_report
import datetime

'''
# 装载数据集（从网上下载）
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
'''


# 装载数据集（本地导入）
def load_data():
    path = r"E:\PythonProject\CNN\MNIST_data\mnist.npz"
    f = np.load(path)
    train_images, train_labels = f['x_train'], f['y_train']
    test_images, test_labels = f['x_test'], f['y_test']
    f.close(
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