Thumb_的博客

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

import numpy as np import torch from torch.utils.data import Dataset from torch.utils.data import DataLoader ## prepare dataset class DiabetesDataset(Dataset): def __init__(self, filepath): xy = np.loadtxt(filepath, delimiter=',', dtype=np.flo

import numpy as np import torch xy = np.loadtxt('E:/BaiduNetdiskDownload/PyTorch深度学习实践/diabetes.csv.gz', delimiter=',', dtype=np.float32) x_data = torch.from_numpy(xy[:, :-1]) y_data = torch.from_numpy(xy[:, [-1]]) class Model(torch.nn.Mod

import torch import matplotlib.pyplot as plt ## prepare dataset x_data = torch.Tensor([[1.0], [2.0], [3.0]]) y_data = torch.Tensor([[2.0], [4.0], [6.0]]) ## design model using class class LinearModel(torch.nn.Module): def __init__(self): supe

# 1. 加载数据集 import torch import torch.nn as nn import torchvision.transforms as transforms import torchvision.datasets as datasets import torchvision import numpy as np import pandas as pd import matplotlib.pyplot as plt # 2. 下载 mnist 数据集 trainsets = dat

Tensor的算术运算——加法运算 c = a + b c = torch.add(a,b) a.add(b) a.add_(b) 其中，前三种一样，第四种是对 a 进行了修改。 Tensor的算术运算——减法运算 减法同理 c = a - b c = torch.sub(a,b) a.sub(b) a.sub_(b) Tensor的算术运算——乘法运算 哈达玛积（element wise,对应元素相乘） c = a * b c = torch.mul(a,b) a.mul(b) a.mul_(b)

张量的存放位置 import torch dev = torch.device("cpu") # 将tensor放置在cpu上，用cpu进行计算 a = torch.tensor([2,2]，evice=dev) print(a) 输出：tensor([2, 2]) dev = torch.device("cpu") # 将tensor放置在cpu上，用cpu进行计算 dev = torch.device("cuda") # 存放在cuda上 a = torch.tensor([2,2],

Tensor的属性 Tensor的属性——稀疏的张量 torch.sparse_coo_tensor coo类型表示了非零元素的坐标形式 indices = torch.tensor([[0,1,1],[2,0,2]]) # 非零元素的坐标（0，2），（1，0），（1，2） values = torch.tensor([3,4,5],dtype=torch.float32) # 对应非零元素的值3，4，5 x = torch.sparse_coo_tensor(i,v,[2,4]) # 一个 2

import torch a = torch.Tensor([[1,2],[3,4]]) print(a) print(a.type()) a = torch.Tensor(2,3) print(a) print(a.type()) '''几种特殊的tensor''' a = torch.ones(2,2) # 全 1 的 tensor print(a) print(a.type()) a = torch.eye(2,2) # 对角线是 1 的 tensor print(a) prin