1. 神经网络¶
① 神经网络训练的就是函数系数$K_{k}$与d。
2. 线性拉平¶
In [1]:
import torch
import torchvision
from torch import nn
from torch.nn import ReLU
from torch.nn import Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset = 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)
Files already downloaded and verified torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) 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torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([16, 3, 32, 32]) torch.Size([1, 1, 1, 49152])
3. 线性层¶
In [2]:
import torch
import torchvision
from torch import nn
from torch.nn import Linear
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)
dataloader = DataLoader(dataset, batch_size=64,drop_last=True)
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 output
tudui = Tudui()
writer = SummaryWriter("logs")
step = 0
for data in dataloader:
imgs, targets = data
print(imgs.shape)
writer.add_images("input", imgs, step)
output = torch.reshape(imgs,(1,1,1,-1)) # 方法一：拉平
print(output.shape)
output = tudui(output)
print(output.shape)
writer.add_images("output", output, step)
step = step + 1
Files already downloaded and verified torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) 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torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10])
① 在 Anaconda 终端里面，激活py3.6.3环境，再输入 tensorboard --logdir=C:\Users\wangy\Desktop\03CV\logs 命令，将网址赋值浏览器的网址栏，回车，即可查看tensorboard显示日志情况。
In [3]:
import torch
import torchvision
from torch import nn
from torch.nn import Linear
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)
dataloader = DataLoader(dataset, batch_size=64,drop_last=True)
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 output
tudui = Tudui()
writer = SummaryWriter("logs")
step = 0
for data in dataloader:
imgs, targets = data
print(imgs.shape)
writer.add_images("input", imgs, step)
output = torch.flatten(imgs) # 方法二：拉平。展开为一维
print(output.shape)
output = tudui(output)
print(output.shape)
step = step + 1
Files already downloaded and verified torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) torch.Size([196608]) torch.Size([10]) torch.Size([64, 3, 32, 32]) 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