用table做的网站优化模型有哪些

今天分享的学习内容主要就是神经网络里面的知识啦，用到的框架就是torch

在这里我也是对自己做一个学习记录，如果不符合大家的口味，大家划走就可以啦

可能没有什么文字或者原理上的讲解，基本上都是代码，但是我还是想说，如果基础不是很好，认认真真敲一遍，会有不一样的感受！！

在这里还有一篇相关内容的补充，大家也可以看一看：

由浅入深，走进深度学习（补充篇：神经网络基础）-CSDN博客

由浅入深，走进深度学习（补充篇：神经网络结构层基础）-CSDN博客

主要内容：

目录

内容六 卷积原理、卷积层、卷积层处理图片

内容七 最大池化层

内容八 非线性激活

内容九 线性层以及其他层

内容十 实战，搭建一个小型的神经网络

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正片开始

内容六 卷积原理、卷积层、卷积层处理图片

import torch
import torch.nn.functional as Finput = torch.tensor([[1, 2, 0, 3, 1],[0, 1, 2, 3, 1],[1, 2, 1, 0, 0],[5, 6, 2, 2, 1],[3, 2, 3, 5, 1]])kernel = torch.tensor([[1, 2, 1],[2, 3, 1],[3, 0, 1]])print(input.shape)
print(kernel.shape)input = torch.reshape(input, (1, 1, 5, 5))
kernel = torch.reshape(kernel, (1, 1, 3, 3))
print(input.shape)
print(input)
print(kernel.shape)
print(kernel)output = F.conv2d(input, kernel, stride = 1)
print(output.shape)
print(output)import torch
import torch.nn.functional as Finput = torch.tensor([[1, 2, 0, 3, 1],[0, 1, 2, 3, 1],[1, 2, 1, 0, 0],[5, 6, 2, 2, 1],[3, 2, 3, 5, 1]])kernel = torch.tensor([[1, 2, 1],[2, 3, 1],[3, 0, 1]])print(input.shape)
print(kernel.shape)input = torch.reshape(input, (1, 1, 5, 5))
kernel = torch.reshape(kernel, (1, 1, 3, 3))
print(input.shape)
print(input)
print(kernel.shape)
print(kernel)output = F.conv2d(input, kernel, stride = 2)
print(output.shape)
print(output)# 步幅、填充原理
# 步幅：卷积核经过输入特征图的采样间隔。设置步幅的目的：希望减小输入参数的数目，减少计算量
# 填充：在输入特征图的每一边添加一定数目的行列。设置填充的目的：希望每个输入方块都能作为卷积窗口的中心，或使得输出的特征图的长、宽 = 输入的特征图的长、宽。
# 一个尺寸 a * a 的特征图，经过 b * b 的卷积层，步幅（stride）= c，填充（padding）= d，若d等于0，也就是不填充，输出的特征图的尺寸 =（a-b）/ c+1；若d不等于0，也就是填充，输出的特征图的尺寸 =（a+2d-b）/ c+1
import torch
import torch.nn.functional as Finput = torch.tensor([[1, 2, 0, 3, 1],[0, 1, 2, 3, 1],[1, 2, 1, 0, 0],[5, 6, 2, 2, 1],[3, 2, 3, 5, 1]])kernel = torch.tensor([[1, 2, 1],[2, 3, 1],[3, 0, 1]])print(input.shape)
print(kernel.shape)input = torch.reshape(input, (1, 1, 5, 5))
kernel = torch.reshape(kernel, (1, 1, 3, 3))
print(input.shape)
print(input)
print(kernel.shape)
print(kernel)output = F.conv2d(input, kernel, stride = 1, padding = 1) # 周围只填充一层
print(output.shape)
print(output)# 内容六 卷积层
# Conv1d代表一维卷积，Conv2d代表二维卷积，Conv3d代表三维卷积
# kernel_size在训练过程中不断调整，定义为3就是3 * 3的卷积核，实际我们在训练神经网络过程中其实就是对kernel_size不断调整import torch
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
import torchvision# dataset = torchvision.datasets.CIFAR10("dataset", train=False, transform=torchvision.transforms.ToTensor(), download=True)
# dataloader = DataLoader(dataset, batch_size = 64)class net(nn.Module):def __init__(self):super(net, self).__init__()self.conv1 = Conv2d(in_channels = 3, out_channels = 6, kernel_size = 3, stride = 1, padding = 0) # 彩色图像输入为3层，我们想让它的输出为6层，选3 * 3 的卷积def forward(self, x):x = self.conv1return xmodel = net()
print(model)# 卷积层处理图片
import torch
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
import torchvisiondataset = torchvision.datasets.CIFAR10("dataset", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size = 64)class net(nn.Module):def __init__(self):super(net, self).__init__()self.conv1 = Conv2d(in_channels = 3, out_channels = 6, kernel_size = 3, stride = 1, padding = 0)def forward(self, x):x = self.conv1(x)return xmodel = net()
for data in dataloader:img, targets = dataoutput = model(img)# print(img.shape)# print(output.shape) # 输入为3通道32×32的64张图片# print(targets.shape) # 输出为6通道30×30的64张图片

内容七 最大池化层

# 最大池化层有时也被称为下采样   dilation为空洞卷积
# Ceil_model为当超出区域时，只取最左上角的值
# 池化使得数据由5 * 5 变为3 * 3,甚至1 * 1的，这样导致计算的参数会大大减小。例如1080P的电影经过池化的转为720P的电影、或360P的电影后，同样的网速下，视频更为不卡
import torch
from torch import nn
from torch.nn import MaxPool2dinput = torch.tensor([[3, 4, 6, 1, 8],[4, 0, 8, 0, 1],[1, 2, 4, 5, 1],[2, 3, 1, 5, 1],[3, 3, 1, 5, 0]], dtype = torch.float32)input = torch.reshape(input, (-1, 1, 5, 5))
print(input.shape)class net(nn.Module):def __init__(self):super(net, self).__init__()self.maxpool = MaxPool2d(kernel_size = 3, ceil_mode = True)def forward(self, x):x = self.maxpool(x)return xmodel = net()
output = model(input)
print(output.shape)
print(output)import torch
import torchvision
from torch import nn
from torch.nn import MaxPool2d
from torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10("dataset", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size = 64)class net(nn.Module):def __init__(self):super(net, self).__init__()self.maxpool = MaxPool2d(kernel_size = 3, ceil_mode = True)def forward(self, x):x = self.maxpool(x)return xmodel = net()
epoch = 0for data in dataloader:img, tagets = data# print('input', img, epoch)output = model(img)# print('output', output, epoch)epoch = epoch + 1

内容八 非线性激活

# inplace为原地替换，若为True，则变量的值被替换。若为False，则会创建一个新变量，将函数处理后的值赋值给新变量，原始变量的值没有修改
import torch
from torch import nn
from torch.nn import ReLUinput = torch.tensor([[1, -2],[-0.7, 3]])input = torch.reshape(input, (-1, 1, 2, 2))
print(input.shape)class net(nn.Module):def __init__(self):super(net, self).__init__()self.relu = ReLU()def forward(self, x):x = self.relu(x)return xmodel = net()
output = model(input)
print(output.shape)
print(output)
print(output[0][0][1][1])import torch
import torchvision
from torch import nn
from torch.nn import ReLU, Sigmoid
from torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10("dataset", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size = 64)class net(nn.Module):def __init__(self):super(net, self).__init__()self.relu = ReLU()self.sigmoid = Sigmoid()def forward(self, x):x1 = self.relu(x)x2 = self.sigmoid(x1)return x2model = net()
epoch = 0for data in dataloader:imgs, targets = dataoutput = model(imgs)# print(output.shape)epoch = epoch + 1    

内容九 线性层以及其他层

# 线性拉平
import torch
import torchvision
from torch import nn
from torch.nn import ReLU, Sigmoid
from torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10("dataset", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size = 64)for data in dataloader:imgs, targets = data# print(imgs.shape)output = torch.reshape(imgs, (1, 1, 1, -1))# print(output.shape)# 线性层
import torch
import torchvision
from torch import nn
from torch.nn import Linear
from torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10("dataset", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size = 64, drop_last=True)
# drop_last=True：如果设置为 True，则当数据集的大小不能被 batch_size 整除时，会丢弃最后一个不足一个批次的数据
# drop_last=False：如果设置为 False（也是默认值），则当数据集的大小不能被 batch_size 整除时，最后一个批次会包含剩下的样本，可能少于 batch_size
class net(nn.Module):def __init__(self):super(net, self).__init__()self.linear = Linear(196608, 10)def forward(self, x):x = self.linear(x)return xmodel = net()
epoch = 0for data in dataloader:imgs, targets = data# print(imgs.shape)imgs_reshape = torch.reshape(imgs, (1, 1, 1, -1)) # 方法一 拉平# print(imgs_reshape.shape)output = model(imgs_reshape)# print(output.shape)# epoch = epoch + 1# 线性层
import torch
import torchvision
from torch import nn
from torch.nn import Linear
from torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10("dataset", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size = 64, drop_last=True)
# drop_last=True：如果设置为 True，则当数据集的大小不能被 batch_size 整除时，会丢弃最后一个不足一个批次的数据
# drop_last=False：如果设置为 False（也是默认值），则当数据集的大小不能被 batch_size 整除时，最后一个批次会包含剩下的样本，可能少于 batch_size
class net(nn.Module):def __init__(self):super(net, self).__init__()self.linear = Linear(196608, 20)def forward(self, x):x = self.linear(x)return xmodel = net()
epoch = 0for data in dataloader:imgs, targets = data# print(imgs.shape)imgs_flatten = torch.flatten(imgs) # 方法二 拉平展为一维# print(imgs_flatten.shape)output = model(imgs_flatten)# print(output.shape)# epoch = epoch + 1

内容十 实战，搭建一个小型的神经网络

# 把网络结构放在Sequential里面，好处就是代码写起来比较简介、易懂
# 可以根据神经网络每层的尺寸，根据下图的公式计算出神经网络中的参数
import torch
import torchvision
from torch import nn
from torch.nn import Linear, Conv2d, MaxPool2d, Flatten
from torch.utils.data import DataLoader# dataset = torchvision.datasets.CIFAR10("dataset", train=False, transform=torchvision.transforms.ToTensor(), download=True)
# dataloader = DataLoader(dataset, batch_size = 64, drop_last=True)class net(nn.Module):def __init__(self):super(net, self).__init__()self.conv1 = Conv2d(in_channels = 3, out_channels = 32, kernel_size = 5, stride = 1, padding = 2)self.maxpool1 = MaxPool2d(kernel_size = 2, ceil_mode = True)self.conv2 = Conv2d(in_channels = 32, out_channels = 32, kernel_size = 5, stride = 1, padding = 2)self.maxpool2 = MaxPool2d(kernel_size = 2, ceil_mode = True)self.conv3 = Conv2d(in_channels = 32, out_channels = 64, kernel_size = 5, stride = 1, padding = 2)self.maxpool3 = MaxPool2d(kernel_size = 2, ceil_mode = True)self.flatten = Flatten()self.linear1 = Linear(1024, 64)self.linear2 = Linear(64, 10)def forward(self, x):x = self.conv1(x)print(x.shape)x = self.maxpool1(x)print(x.shape)x = self.conv2(x)print(x.shape)x = self.maxpool2(x)print(x.shape)x = self.conv3(x)print(x.shape)x = self.maxpool3(x)print(x.shape)x = self.flatten(x)print(x.shape)x = self.linear1(x)print(x.shape)x = self.linear2(x)print(x.shape)return xmodel = net()
print(model)input = torch.ones((64, 3, 32, 32))
output = model(input)
print(output.shape)

# Sequential神经网络
import torch
import torchvision
from torch import nn
from torch.nn import Linear, Conv2d, MaxPool2d, Flatten, Sequential
from torch.utils.data import DataLoaderclass net(nn.Module):def __init__(self):super(net, self).__init__()self.model = Sequential(Conv2d(in_channels = 3, out_channels = 32, kernel_size = 5, stride = 1, padding = 2),MaxPool2d(kernel_size = 2, ceil_mode = True),Conv2d(in_channels = 32, out_channels = 32, kernel_size = 5, stride = 1, padding = 2),MaxPool2d(kernel_size = 2, ceil_mode = True),Conv2d(in_channels = 32, out_channels = 64, kernel_size = 5, stride = 1, padding = 2),MaxPool2d(kernel_size = 2, ceil_mode = True),Flatten(),Linear(1024, 64),Linear(64, 10))def forward(self, x):x = self.model(x)return xmodel = net()
print(model)input = torch.ones((64, 3, 32, 32))
output = model(input)
print(output.shape)

注：上述内容参考b站up主“我是土堆”的视频！！！