大家好,又见面了,我是你们的朋友全栈君。如果您正在找激活码,请点击查看最新教程,关注关注公众号 “全栈程序员社区” 获取激活教程,可能之前旧版本教程已经失效.最新Idea2022.1教程亲测有效,一键激活。
Jetbrains全系列IDE使用 1年只要46元 售后保障 童叟无欺
pytorch MSELoss参数详解
import torch
import numpy as np
loss_fn = torch.nn.MSELoss(reduce=False, size_average=False)
a=np.array([[1,2],[3,8]])
b=np.array([[5,4],[6,2]])
input = torch.autograd.Variable(torch.from_numpy(a))
target = torch.autograd.Variable(torch.from_numpy(b))
loss = loss_fn(input.float(), target.float())
print(loss)
loss_fn = torch.nn.MSELoss(reduce=False, size_average=True)
a=np.array([[1,2],[3,8]])
b=np.array([[5,4],[6,2]])
input = torch.autograd.Variable(torch.from_numpy(a))
target = torch.autograd.Variable(torch.from_numpy(b))
loss = loss_fn(input.float(), target.float())
print(loss)
a=np.array([[1,2],[3,8]])
b=np.array([[5,4],[6,2]])
loss_fn = torch.nn.MSELoss(reduce=True, size_average=False)
input = torch.autograd.Variable(torch.from_numpy(a))
target = torch.autograd.Variable(torch.from_numpy(b))
loss = loss_fn(input.float(), target.float())
print(loss)
a=np.array([[1,2],[3,8]])
b=np.array([[5,4],[6,2]])
loss_fn = torch.nn.MSELoss(reduce=True, size_average=True)
input = torch.autograd.Variable(torch.from_numpy(a))
target = torch.autograd.Variable(torch.from_numpy(b))
loss = loss_fn(input.float(), target.float())
print(loss)
a=np.array([[1,2],[3,8]])
b=np.array([[5,4],[6,2]])
loss_fn = torch.nn.MSELoss()##reduce=True, size_average=True
input = torch.autograd.Variable(torch.from_numpy(a))
target = torch.autograd.Variable(torch.from_numpy(b))
loss = loss_fn(input.float(), target.float())
print(loss)
loss_fn = torch.nn.MSELoss(reduction = 'none')
a=np.array([[1,2],[3,8]])
b=np.array([[5,4],[6,2]])
input = torch.autograd.Variable(torch.from_numpy(a))
target = torch.autograd.Variable(torch.from_numpy(b))
loss = loss_fn(input.float(), target.float())
print(loss)
loss_fn = torch.nn.MSELoss(reduction = 'sum')
a=np.array([[1,2],[3,8]])
b=np.array([[5,4],[6,2]])
input = torch.autograd.Variable(torch.from_numpy(a))
target = torch.autograd.Variable(torch.from_numpy(b))
loss = loss_fn(input.float(), target.float())
print(loss)
loss_fn = torch.nn.MSELoss(reduction = 'none')
a=np.array([[1,2],[3,8]])
b=np.array([[5,4],[6,2]])
input = torch.autograd.Variable(torch.from_numpy(a))
target = torch.autograd.Variable(torch.from_numpy(b))
loss = loss_fn(input.float(), target.float())
print(loss)
loss_fn = torch.nn.MSELoss(reduction = 'elementwise_mean')
a=np.array([[1,2],[3,8]])
b=np.array([[5,4],[6,2]])
input = torch.autograd.Variable(torch.from_numpy(a))
target = torch.autograd.Variable(torch.from_numpy(b))
loss = loss_fn(input.float(), target.float())
print(loss)
发布者:全栈程序员-用户IM,转载请注明出处:https://javaforall.cn/192085.html原文链接:https://javaforall.cn
【正版授权,激活自己账号】: Jetbrains全家桶Ide使用,1年售后保障,每天仅需1毛
【官方授权 正版激活】: 官方授权 正版激活 支持Jetbrains家族下所有IDE 使用个人JB账号...