torch.nn.functional.conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) → Tensor source 在由几个输入平面组成的输入图像上应用3D卷积。 对于细节和输出形状,查看Conv3d 参数: input – 输入张量的形状 (minibatch xin_channels x
torch.nn.functional.conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1)对由几个输入平面组成的输入信号应用一维卷积。详细信息和输出形状,查看Conv1d参数: input– 输入张量的形状 (minibatch x in_channels x iW) weight– 过滤器的形状 (out_channels, in_channels, kW) ...
torch.nn.functional.triplet_margin_loss(anchor, positive, negative, margin=1.0, p=2, eps=1e-06, swap=False, size_average=None, reduce=None, reduction='mean') [source] See TripletMarginLoss for details triplet_margin_with_distance_losstorch.nn.functional.triplet_margin_with_distance_loss(...
triplet_margin_loss Vision functions pixel_shuffle pad interpolate upsample upsample_nearest upsample_bilinear grid_sample affine_grid DataParallel functions (multi-GPU, distributed) data_parallel Convolution functions conv1d torch.nn.functional.conv1d(input, weight, bias=None, stride=1, padding=0, dilat...
3 CrossEntropyLoss 交叉熵损失函数 交叉熵损失函数=nn.LogSoftmax()+nn.NLLLoss() 因为神经网络输出的是向量,并不是概率分布的形式。所以需要 softmax激活函数将一个向量进行“归一化”成概率分布的形式,再采用交叉熵损失函数计算 loss。 主要参数:
torch.nn.functional.triplet_margin_loss Supported 91 torch.nn.functional.pixel_shuffle Supported 92 torch.nn.functional.pad Supported 93 torch.nn.functional.interpolate Unsupported. 94 torch.nn.functional.upsample Unsupported. 95 torch.nn.functional.upsample_nearest ...
Calling triplet_loss = nn.TripletMarginLoss() results in: AttributeError: module 'torch.nn' has no attribute 'TripletMarginLoss' even with import torch.nn as nn Related PR that added introduced TripletMarginLoss: #1165 Work-around for no...
一个nn.Module包含各个层和一个forward(input)方法,该方法返回output。 模型示例 LeNet 这是一个简单的前馈神经网络 (feed-forward network)(LeNet)。它接受一个输入,然后将它送入下一层,一层接一层的传递,最后给出输出。 这是一个简单的前馈神经网络 (feed-forward network)(LeNet)。它接受一个输入,然后...
torch.nn.TripletMarginLoss(margin=1.0, p=2.0, eps=1e-06, swap=False, size_average=None, reduce=None, reduction='mean') 1. 功能: 计算三元组损失。 三元组: 这是一种数据的存储或者使用格式。<实体1,关系,实体2>。在项目中,也可以表示为< anchor, positive examples , negative examples> 在这个...
CosineEmbeddingLoss (@jon-tow Add C++ torch::nn::CosineEmbeddingLoss #27345) MultiMarginLoss (please see details in [Contributor Welcome] Implement C++ API version of torch.nn.MultiMarginLoss #27198) (@CarMiranda Implement C++ API torch::nn::MultiMarginLoss. #27424) TripletMarginLoss (please...