pytorch MSELoss參數詳解

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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