pytorch MSELoss参数详解「建议收藏」

全栈程序员-站长 • 2022年9月18日下午5:00 • 未分类 • 阅读 4

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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)

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