一.基本概念

1.Convolutional Neural Network Intro

mnist数据集

2.Image Filter / Image Kernel

Image Kernels explained visually

访问这个网站可以直观看到image kernels对图片的影响

3.Convolutional Layer and RGB

为什么要用巻积神经网络代替人工神经网络

上图是常规神经网络

在完全连接的神经元之间进行数百万个数据点的操作，需要强大的处理能力和大量时间

上图是卷积神经网络，神经元只是局部连接，不是全连接，存在绿色区域的过滤层filter layer

4.Pooling Layer in Convolutional Neural Network

使用池化层进一步减少参数，这个过程称为下采样down sampling

常见的是最大池化和平均池化

二.

1.Import MNIST Images

# 导入PyTorch库用于深度学习import torchimport torch.nn as nn# nn模块包含了构建神经网络所需的所有组件import torch.nn.functional as F# F提供了一些常用的激活函数和损失函数from torch.utils.data import DataLoader# DataLoader用于包装数据集，便于批处理和迭代from torchvision import datasets, transforms# datasets用于加载数据集，transforms用于数据预处理from torchvision.utils import make_grid# make_grid用于将多张图像拼接在一起，便于显示# 导入其他科学计算和数据处理的库import numpy as np# NumPy用于高效的多维数组操作import pandas as pd# Pandas提供了强大的数据结构和数据分析工具from sklearn.metrics import confusion_matrix# confusion_matrix用于计算分类的混淆矩阵import matplotlib.pyplot as plt# Matplotlib是Python的绘图库，用于绘制图表和图像%matplotlib inline# %matplotlib inline 是Jupyter Notebook的魔法命令，用于在Notebook中直接显示matplotlib生成的图像# convert MNIST Image Files into a Tensor of 4-dimensions(# of images, height, width, colors)transform = transforms.ToTensor() # 将 [0, 255] 范围内的 PIL 图像或 numpy.ndarray (H x W x C) 转换为形状 (C x H x W) 的 torch.FloatTensor# train datatrain_data = datasets.MNIST(root= 'dateset/cnn_data', train= True, download= True, transform= transform)# test datatest_data = datasets.MNIST(root= 'dataset/cnn_data', train= False, download= True, transform= transform)test_data

2.Convolutional and Pooling Layers

Conv2d — PyTorch 2.2 documentation

max_pool2d — PyTorch 2.2 documentation

通过神经网络运行一张图像

# test datatest_data = datasets.MNIST(root= 'dataset/cnn_data', train= False, download= True, transform= transform)test_data## 2.Convolutional and Pooling Layers# create a small batch size for images --- let's say 10train_loader = DataLoader(train_data, batch_size= 10, shuffle= True)test_loader = DataLoader(test_data, batch_size= 10, shuffle= False)#define our cnn model#describe convolutional layer and what it is doing (2 convotional layers)# this is just an example in the next vedio we will build out the actual modelconv1 = nn.Conv2d(in_channels=1, out_channels= 6, kernel_size= 3, stride= 1)conv2 = nn.Conv2d(in_channels=6, out_channels= 16, kernel_size= 3, stride= 1)# grab 1 MNIST record/imagefor i, (X_Train, y_train) in enumerate(train_data):breakX_Train.shapex = X_Train.view(1,1,28,28)# perform our first convolutionx = F.relu(conv1(x)) #rectified linear unit for our activation function# 1singel image, 6 is the filters we asked for , 26x26x.shape# pass thru the pooling layerx = F.max_pool2d(x, kernel_size= 2, stride= 2)x.shape # 26/2# do our second convolutional layerx = F.relu(conv2(x))#again we did not set padding so we lose 2 pixels around the outside of the imagex.shape# pooling layerx = F.max_pool2d(x, kernel_size= 2, stride= 2)x.shape # 11 / 2 = 5.5 but we have to round down,because we can not invent data to round up((28 - 2)/2 -2) /2