[人工智能]学习Pytorch语法中遇到的问题

广播

# -*- coding: utf-8 -*-
import torch
import math

# x'shape is [2000,]
x = torch.linspace(-math.pi, math.pi, 2000)
y = torch.sin(x)

p = torch.tensor([1, 2, 3])
xx = x.unsqueeze(-1).pow(p)
# In the above code, x.unsqueeze(-1) has shape (2000, 1), and p has shape
# (3,), for this case, broadcasting semantics will apply to obtain a tensor
# of shape (2000, 3) 

函数

损失函数

损失函数的参数基本一样

比如对于nn.MSELoss函数，其中的reduction函数；

import torch
import torch.nn as nn
 
a = torch.tensor([[1, 2], [3, 4]], dtype=torch.float)
b = torch.tensor([[3, 5], [8, 6]], dtype=torch.float)
 
loss_fn1 = torch.nn.MSELoss(reduction='none')
loss1 = loss_fn1(a.float(), b.float())
print(loss1)   # 输出结果：tensor([[ 4.,  9.],
               #                 [25.,  4.]])
 
loss_fn2 = torch.nn.MSELoss(reduction='sum')
loss2 = loss_fn2(a.float(), b.float())
print(loss2)   # 输出结果：tensor(42.)
 
 
loss_fn3 = torch.nn.MSELoss(reduction='mean')
loss3 = loss_fn3(a.float(), b.float())
print(loss3)   # 输出结果：tensor(10.5000)