qq_48983365的博客

import torchvision.datasets as datasets from torchvision import transforms,utils from torch.utils.data import dataloader,DataLoader trans = transforms.Compose([transforms.ToTensor(),transforms.Resize((48,48))]) data = datasets.ImageFolder(root=r'E:\pyth

在这里插入代码 ################dataset1111111######################### import torch import os from torch.utils.data import Dataset ,DataLoader from torch import nn as nn from torchvision import transforms import numpy as np from PIL import Image import cv2 c

class Mydataset(Dataset): def __init__(self,fliepatch ,lablespatch ,transfrom =None ): self.trans = transfrom self.images = [] self.classes = [] subist= os.listdir(fliepatch) for i in subist: self.

import torch c = torch.randn((2,3,4)) d = torch.randn((2,3,4)) print(c,d) print(c.shape,d.shape) print(torch.cat((c,d),dim=0)) print(torch.cat((c,d),dim=1)) print(torch.cat((c,d),dim=2)) m = torch.cat((c,d),dim=0) n = torch.cat((c,d),dim=1) l = torch.cat(

```python import torch import torch.nn as nn import torch.nn.functional as F class conv1(nn.Module): def __init__(self , inchannel ,outchannel): super(conv1, self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(in_cha.

代码： import torch import torch.nn as nn import torch.nn.functional as F class inceptionModuleV1(nn.Module): def __init__(self,inputs, outputs1 , outputs2 ,outputs3 ,outputs4 ,outputs5 ,outputs6): super(inceptionModuleV1,self).__init__()

```python import torch import torch.nn as nn import torch.nn.functional as F class ALX(nn.Module): def __init__(self): self.conv1 = nn.Conv2d(in_channels=3 ,out_channels= 96 ,kernel_size= 11 ,stride= 4 ,padding= 0) self.maxpool =.

我就不上传csdn了，下载需要花钱。有需要的可以私信我。

这里 我就直接上代码了 ，因为沐神的是分开的 ，我调试放到了一起，` import torch import random import zipfile from acem.utils import * device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print(torch.__version__) print(device) # # with zipfile.ZipFile('./jaychou_lyrics.t

import torch x_da = [1.0,2.0,3.0] y_da = [2.0,4.0,6.0] w = torch.Tensor([1.0]) w.requires_grad = True def forward(x): return w * x def loss(x , y): return (forward(x) - y)**2 epoc = [] costs = [] for epo in range(100): for xs, ys in z..

import torch from torchvision import transforms from torchvision import datasets from torch.utils.data import DataLoader import torch.nn.functional as F #relu import torch.optim as optim batch_size = 64 transform = transforms.Compose([transforms.ToTen

import torch x_data = torch.tensor([[1.0],[2.0],[3.0]]) y_data = torch.tensor([[5.0],[6.0],[7.0]]) class LinerModel(torch.nn.Module): def __init__(self): super(LinerModel,self).__init__() self.lin = torch.nn.Linear(1,1) def fo

代码如下： import torch x_data = torch.tensor([[1.0],[2.0],[3.0]]) y_data = torch.tensor([[2.0],[4.0],[6.0]]) class Linear(torch .nn.Module): def __init__(self): super(Linear,self).__init__() self.linear = torch.nn.Linear(1,1)

PyTorch反向传播学习实例（p4）刘二 import torch x_da = [1.0,2.0,3.0] y_da = [2.0,4.0,6.0] w1 = torch.tensor([1.0]) w1.requires_grad= True w2= torch.tensor([1.0]) w2.requires_grad=True b = torch.tensor([1.0]) b.requires_grad=True def dors(x): return w1 *x *x + w