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1、神经网络的基本骨架-nn.Module的使用 2、卷积操作实例
3、神经网络-卷积层
4、神经网络-最大池化的使用
#xff08;1#xff09;最大池化画图理解#xff1a;
#xff08;2#xff09;代码实现#xff1a;
5、神经网络-非线性激活
#xff08;1#xf…目录
1、神经网络的基本骨架-nn.Module的使用 2、卷积操作实例
3、神经网络-卷积层
4、神经网络-最大池化的使用
1最大池化画图理解
2代码实现
5、神经网络-非线性激活
1代码实现调用sigmoid 函数
6、神经网络-线性层
1代码
7、网络搭建-小实战
1完整代码 1、神经网络的基本骨架-nn.Module的使用
官网地址pytorch里的nn
import torch
from torch import nnclass Tudui(nn.Module):def __init__(self):super().__init__()def forward(self, input):output input 1return outputtudui Tudui()
x torch.tensor(1.0)
output tudui(x)
print(output) 2、卷积操作实例
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, 2, 3, 1, 1],[2, 1, 0, 1, 1]])
kernel torch.tensor([[1, 2, 1],[0, 1, 0],[2, 1, 0]])# 转换成要求的格式 shape(N,C,H,W)
input torch.reshape(input, (1, 1, 5, 5))
kernel torch.reshape(kernel, (1, 1, 3, 3))print(input.shape)
print(kernel.shape)
# stride1 的情况
output F.conv2d(input, kernel, stride1)
print(output)# stride2 的情况
output2 F.conv2d(input, kernel, stride2)
print(output2)# 设置了padding
output3 F.conv2d(input, kernel, stride1, padding1)
print(output3) 运行结果 3、神经网络-卷积层
Conv2d文档地址torch.nn.Conv2d in_channels 输入的通道数
out_channels 输出的通道数
kernel_size 卷积核大小
stride 默认为移动为1
padding是否在边缘进行填充
例子
import torch
import torchvision
import ssl
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriterssl._create_default_https_context ssl._create_unverified_contextdataset torchvision.datasets.CIFAR10(root./test11_data, trainFalse,transformtorchvision.transforms.ToTensor(), downloadTrue)
dataloader DataLoader(dataset, batch_size64)class Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()self.conv1 Conv2d(in_channels3, out_channels6, kernel_size3, stride1, padding0 )def forward(self, x):x self.conv1(x)return xtudui Tudui()
writer SummaryWriter(test11_logs)
step 0
for data in dataloader:imgs, targets dataoutput tudui(imgs)writer.add_images(input, imgs, step)output torch.reshape(output, (-1, 3, 30, 30), ) # 不知道是多少的时候直接写-1writer.add_images(output, output, step)step step 1writer.close()结果输出 4、神经网络-最大池化的使用
1最大池化画图理解 2代码实现
import torch
from torch import nn
from torch.nn import MaxPool2dinput torch.tensor([[1, 2, 0, 3, 1],[0, 1, 2, 3, 1],[1, 2, 1, 0, 0],[5, 2, 3, 1, 1],[2, 1, 0, 1, 1]], dtypetorch.float32)input torch.reshape(input, (-1, 1, 5, 5))
print(input.shape)class Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()self.maxpool1 MaxPool2d(kernel_size3, ceil_modeTrue)def forward(self,input):output self.maxpool1(input)return outputtudui Tudui()
output tudui(input)
print(output)
运行结果 3展示池化的图片代码
import torch
import ssl
import torchvision
from torch import nn
from torch.nn import MaxPool2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
ssl._create_default_https_context ssl._create_unverified_contextdataset torchvision.datasets.CIFAR10(./test12_data, trainFalse, downloadTrue,transformtorchvision.transforms.ToTensor())
dataloader DataLoader(dataset, batch_size64)# input torch.tensor([[1, 2, 0, 3, 1],
# [0, 1, 2, 3, 1],
# [1, 2, 1, 0, 0],
# [5, 2, 3, 1, 1],
# [2, 1, 0, 1, 1]], dtypetorch.float32)
#
#
# input torch.reshape(input, (-1, 1, 5, 5))
# print(input.shape)class Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()self.maxpool1 MaxPool2d(kernel_size3, ceil_modeTrue)def forward(self,input):output self.maxpool1(input)return outputwriter SummaryWriter(test12_logs_maxpool)
tudui Tudui()
step 0
for data in dataloader:imgs, targets datawriter.add_images(input, imgs, step)output tudui(imgs)writer.add_images(output, output, step)step step 1writer.close()# tudui Tudui()
# output tudui(input)
# print(output)
运行结果 5、神经网络-非线性激活
非线性激活函数 1代码实现调用sigmoid 函数
import torch
import ssl
import torchvision
from torch import nn
from torch.nn import ReLU, Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
ssl._create_default_https_context ssl._create_unverified_contextinput torch.tensor([[1, -0.5],[-1, 3]])input torch.reshape(input, (-1, 1, 2, 2))
print(input.shape)dataset torchvision.datasets.CIFAR10(./test13_data, trainFalse, downloadTrue,transformtorchvision.transforms.ToTensor())
dataloader DataLoader(dataset, batch_size64)class Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()self.relu1 ReLU()self.sigmoid1 Sigmoid()def forward(self,input):output_ self.sigmoid1(input)return output_tudui Tudui()
writer SummaryWriter(test13_logs_sigmoid)
step 0
for data in dataloader:imgs, targets datawriter.add_images(input, imgs, global_stepstep)output tudui(imgs)writer.add_images(output, output, step)step step 1writer.close()
输出结果 6、神经网络-线性层
1代码
import torch
import ssl
import torchvision
from torch import nn
from torch.nn import Linear
from torch.utils.data import DataLoader
ssl._create_default_https_context ssl._create_unverified_contextdataset torchvision.datasets.CIFAR10(./test14_data, trainFalse, downloadTrue,transformtorchvision.transforms.ToTensor())
dataloader DataLoader(dataset, batch_size64, drop_lastTrue)class Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()self.linear1 Linear(196608, 10)def forward(self,input):output self.linear1(input)return outputtudui Tudui()for data in dataloader:imgs, targets dataprint(imgs.shape)output torch.reshape(imgs, (1,1,1,-1)) # torch.Size([1, 1, 1, 196608])# output torch.flatten(imgs) # 会变成一行 torch.Size([196608])print(output.shape)output tudui(output)print(output.shape)结果展示 7、网络搭建-小实战 1完整代码
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.tensorboard import SummaryWriterclass Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()# self.conv1 Conv2d(in_channels3, out_channels32, kernel_size5, padding2) # 卷积# self.maxpool1 MaxPool2d(2) # 池化# self.conv2 Conv2d(in_channels32, out_channels32, kernel_size5, padding2)# self.maxpool2 MaxPool2d(2)# self.conv3 Conv2d(in_channels32, out_channels64, kernel_size5, padding2)# self.maxpool3 MaxPool2d(2)# self.flatten Flatten()# self.linear1 Linear(in_features1024, out_features64)# self.linear2 Linear(in_features64, out_features10)self.model1 Sequential(Conv2d(in_channels3, out_channels32, kernel_size5, padding2),MaxPool2d(2),Conv2d(in_channels32, out_channels32, kernel_size5, padding2),MaxPool2d(2),Conv2d(in_channels32, out_channels64, kernel_size5, padding2),MaxPool2d(2),Flatten(),Linear(in_features1024, out_features64),Linear(in_features64, out_features10))def forward(self,x):# x self.conv1(x)# x self.maxpool1(x)# x self.conv2(x)# x self.maxpool2(x)# x self.conv3(x)# x self.maxpool3(x)# x self.flatten(x)# x self.linear1(x)# x self.linear2(x)x self.model1(x)return xtudui Tudui()
print(tudui) # 输出网络结构input torch.ones((64, 3, 32, 32))
output tudui(input)
print(output.shape)writer SummaryWriter(test15_logs)
writer.add_graph(tudui, input)
writer.close()
运行结果
