torch.nn.functional.softmax(input, dim) 对n维输入张量运用Softmax函数,将张量的每个元素缩放到(0,1)区间且和为1。Softmax函数定义如下: 参数: dim:指明维度,dim=0表示按列计算;dim=1表示按行计算。默认dim的方法已经弃用了,最好声明dim,否则会警告: UserWarning: Implicit dimension choiceforsoftmax has bee...
例2:对二维张量进行softmax归一化 import torch import torch.nn.functional as F # 创建一个二维张量 input_tensor = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) # 对输入张量进行softmax归一化,dim=1表示对第1维度进行归一化 output_tensor = F.softmax(input_tensor, dim=1) print(output...
softmax相关 核心引用:知乎链接 importtorch.nn.functionalasFimporttorch truth = torch.tensor([[1,0,0]], dtype=torch.float) predicted1 = torch.tensor([[0.5,0.4,0.1]], dtype=torch.float)print(truth.softmax(0))#dim=0,每一列的概率之和为1print(truth.softmax(1))#dim=1,每一行的概率之和...
output2 = torch.nn.functional.smooth_l1_loss(input, target) print('output1: ',output1) print('output2: ',output2) # output1: tensor(0.7812, grad_fn=<SmoothL1LossBackward0>) # output2: tensor(0.7812, grad_fn=<SmoothL1LossBackward0>) 0-1 Loss 0-1 Loss 它直接比较预测值和真实值是否...
1. 2. 3. 4. conv3d torch.nn.functional.conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) → Tensor Applies a 3D convolution over an input image composed of several input planes. SeeConv3dfor details and output shape. ...
x1 = torch.softmax(x, dim=-1) x2 = torch.nn.Softmax(dim=-1)(x) x3 = torch.nn.functional.softmax(x, dim=-1) x4 = torch.nn.functional.log_softmax(x, dim=-1) print(x1) print(x2) print(x3) print(x4) print(torch.log(x3)) ...
torch.nn.functional.conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) 在由几个输入平面组成的输入图像上应用三维转置卷积,有时也称为“去卷积”。 有关详细信息和输出形状,参考ConvTranspose3d。
[num_users=1]=call_module[target=lin_w](args=(%l_x_,),kwargs={})%buf0:[num_users=1]=call_function[target=operator.add](args=(%lin_w,%l_b_),kwargs={})%buf1:[num_users=1]=call_function[target=torch.nn.functional.softmax](args=(%buf0,),kwargs={dim:-1})return(buf1,)...
softmax常⽤于多分类过程中，它将多个神经元的输出，归⼀化到(0, 1)区间内，因此Softmax的输出可以看成概率，从⽽来进⾏多分类。nn.CrossEntropyLoss() in Pytorch 其实归根结底，交叉熵损失的计算只需要⼀个term。这个term就是在softmax输出层中找到ground-truth⾥正确标签对应的那个entry j，也就是...
nn.functional.log_softmax(x_NV @ w_DV, dim=1)[torch.arange(N), c_N].double() err = abs(output_N.double() - ref) / abs(ref) print(torch.mean(err).item(), torch.quantile(err, 0.95).item(), torch.quantile(err, 0.99).item()) Output: 9.208518284318959e-05 0.0 ...