CLASS torch.nn.KLDivLoss(size_average=None,reduce=None, reduction='mean', log_target=False) The Kullback-Leibler divergence loss. For tensors of the same shapeypred,ytrueypred,ytrue,whereypredypredis the input andytrueytrueis the target,we definethe pointwise KL-divergenceas To avoid underfl...
在pytorch中,nn.KLDivLoss()的计算公式如下: 上图y为标签,x为预测值,则pytorch应该以如下代码使用 :(1)、lossfunc=nn.KLDivLoss()(2)、loss = lossfunc(预测值, 标签值) 所以,在pytorch中预测值和标签值分别做如下处理:(1)、F.log_softmax(预测值/ temp, dim=1) (2)、F.softmax(标签值/ temp, ...
torch.nn.functional.kl_div(input, target, size_average=True) KL 散度损失函数,详细请看KLDivLoss 参数: input– 变量的任意形状 target- 与输入相同形状的变量 size_average– 如果是真的,输出就除以输入张量中的元素个数 torch.nn.functional.cross_entropy(input, target, weight=None, size_average=True)...
torch.nn.functional.kl_div(input, target, size_average=True) KL散度损失函数,详细请看KLDivLoss 参数:-input– 任意形状的 Variable -target– 与输入相同形状的 Variable -size_average– 如果为TRUE,loss则是平均值,需要除以输入 tensor 中 element 的数目 torch.nn.functional.cross_entropy(input, target,...
torch.nn.functional.max_pool3d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)source对由几个输入平面组成的输入进行3D最大池化。 有关详细信息和输出形状,参考MaxPool3dtorch.nn.functional.max_unpool1d(input, indices, kernel_size, stride=None, ...
🐛 Bug I got a negative loss value when using nn.KLDivLoss To Reproduce Steps to reproduce the behavior: use the code as, import numpy as np import torch import torch.nn as nn loss = nn.KLDivLoss() output = torch.from_numpy(np.array([[0.1...
( torch._C._nn.avg_pool3d, r""" avg_pool3d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor Applies 3D average-pooling operation in :math:`kT \times kH \times kW` regions by step size :math:`sT \times ...
Default: False output is a Tensor of size N containing computed target log probabilities for each example loss is a Scalar representing the computed negative log likelihood loss input: (N,in_features)(N, in\_features)(N,in_features) target: (N)(N)(N) where each value satisfies 0<...
torch.nn.functional.lp_pool1d(input, norm_type, kernel_size, stride=None, ceil_mode=False)[source] Applies a 1D power-average pooling over an input signal composed of several input planes. If the sum of all inputs to the power of p is zero, the gradient is set to zero as well. ...
Conv2d(20, 10, kernel_size=(4, 4), stride=(1, 1)) 将所有的模型参数(parameters)和buffers复制到CPU NOTE:官方文档用的move,但我觉着copy更合理。 cuda(device_id=None) 将所有的模型参数(parameters)和buffers赋值GPU 参数说明: device_id (int, optional) – 如果指定的话,所有的模型参数都会复制到...