- 张量（Tensors）类似于NumPy的ndarrays（点NumPy实践了解），但张量可以在GPU上进行计算。 所以从本质上来说，PyTorch是一个处理张量的库。一个张量是一个数字、向量、矩阵或任何n维数组。
# # 张量的创建
# import torch
# import numpy
# torch.manual_seed(7) 随机创建7个种子数# # 直接创建
# a = torch.tensor([[1,2,3],[4,5,6]])
# print(a)
# import numpy
# import torch
# a = numpy.array([1,2,3])
# t = torch.from_numpy(a)
# print(a)# 依据数值创建#torch.zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False)
# import torch
# # b = torch.zeros(2,3)
# # print(b)
# #
# # #torch.zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False)
# # # 布局相同
# # imput = torch.empty(1,3)
# # c = torch.zeros_like(imput)
# # print(c)
# #
# # torch.full((2, 3), 3.141592)
# # torch.arange(1, 2.5, 0.5)
# # torch.linspace(start=-10, end=10, steps=5)
# a = torch.eye(2)
# print(a)# 依概率分布创建张量
# 创建正态分布张量
import torch
# a = torch.normal(mean=torch.arange(1.0, 11.0),std = torch.arange(1, 0, -0.1))
# print(a)
#
# std = torch.arange(1, 0, -0.1)
# print(std)
# mean=torch.arange(1.0, 11.0)
# print(mean)
#
# a = torch.normal(2, 3, size=(1, 4))
# print(a)# a = torch.randn(2,3) #标准正太分布
# print(a)
#
# a =torch.randint(3, 10, (2, 2)) #整数均匀分布
# print(a)# a =torch.randperm(4)#生成从0到n-1的随机排列
# print(a)
# 以input为概率，生成伯努利分布（0-1分布，两点分布）
# input = torch.empty(3,3).uniform_(0,1)
# a = torch.bernoulli(input)
# print(a)# 张量的操作
# 1.张量拼接与切分
# x = torch.randn(2,3)
# a = torch.cat((x,x,x),1)
# print(a)
# a = torch.zeros (1,1)
# b = torch.ones (1,1)
# c = torch.stack([a, b], dim=2)
# print (c)
#
# input = torch.randn(2, 3)
# c = torch.chunk(input,1, dim=0)
# print (c)# a = torch.arange(10).reshape(5,2)
# b = torch.split(a,2)
# print(b)# 张量索引
# x = torch.randn(3,4)
# indices = torch.tensor([0 ,2]) #索引数据序号
# a = torch.index_select(x, 0,indices)
# print(a)# x = torch.randn(3,4) #将正太分布中大于等于0.5的张良跳出来
# mask = x.ge(0.5)
# b = torch.masked_select(x,mask)
# print(b)# 张量变换
# a = torch.arange(4.)
# b = torch.reshape(a,(2,2))
# print(b)# x = torch.randn(3,4)
# c = torch.transpose(x,0,1) #两个维度的转换，原本是一个三行四列的转换成四列三行的
# print(c)# 转秩
# a = torch.randn(())
# b = torch.t(a)
# print(b)
# x = torch.zeros(2, 1, 2, 1, 2)
# y = torch.squeeze(x)
# print(y)# x = torch.tensor([1, 2, 3, 4])
# b = torch.unsqueeze(x, 0)
# print(b)# 线性回归模型
# 训练样本x与y
x = torch.rand(20,1)*10
y = 2*x +(5+torch.rand(20,1))
# 构建线性回归参数
w = torch.rand((1),requires_grad=True)
b = torch.zeros((1),requires_grad=True)
# 设置学习率
lr = 0.1for iteration in range(100):# 前向传播wx = torch.mul(w, x)y_pred = torch.add(wx, b)# 计算lossloss = (0.5 * (y - y_pred) ** 2).mean()# 反向传播loss.backward()# 更新参数b.data.sub_(lr * b.grad) # 这种_的加法操作时从自身减，相当于-=w.data.sub_(lr * w.grad)# 梯度清零w.grad.data.zero_()b.grad.data.zero_()print(w.data, b.data)
