平滑L1损失-nn.SmoothL1Loss 负对数似然损失函数-PoissonNLLLoss 相对熵损失-nn.KLDivLoss nn.MarginRankingLoss nn.MultiLabelMarginLoss nn.SoftMarginLoss nn.MultiLabelSoftMarginLoss nn.MultiMarginLoss nn.TripletMarginLoss nn.HingeEmbeddingLoss nn.CosineEmbeddingLoss 损失函数 损失函数是什么 损失函数:衡量模型...
torch.nn.MultiLabelMarginLoss(size_average=None, reduce=None, reduction='mean') 1. 二分类逻辑损失函数: torch.nn.SoftMarginLoss(size_average=None, reduce=None, reduction='mean') 1. HingeEmbedding损失:专用于 Embedding,margin 为边界值 torch.nn.HingeEmbeddingLoss(margin=1.0, size_average=None, r...
2.1.1357 Part 3 Section 20.433, text:min-label-width 2.1.1358 Part 3 Section 20.435, text:space-before 2.1.1359 Part 3 Section 20.439, style:margin-gutter 2.1.1360 Part 3 Section 20.441, draw:decorative 2.1.1361 Part 2 Section 4.16.11, manifest:preferred-view-mode 2.1.1362 Part 2...
2.1.1357 Part 3 Section 20.433, text:min-label-width 2.1.1358 Part 3 Section 20.435, text:space-before 2.1.1359 Part 3 Section 20.439, style:margin-gutter 2.1.1360 Part 3 Section 20.441, draw:decorative 2.1.1361 Part 2 Section 4.16.11, manifest:preferred-view-mode 2.1.1362 Pa...
contrast the transductive learning setup used in [36] for un- 4This could be easily extended to multi-label case. 3491 supervised super-resolution, we consider an inductive learn- ing allowing for fast inference. In particular, we propose us- ing local a...
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each with 15 filters and a 3*3 pixel size. Binary cross-entropy was chosen as the loss function, while Adam's method was employed as the optimiser. The CNN model was trained with 150 epochs, as shown in Fig.4. Softmax, the last activation function used, produced the predicted probabilit...
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easy_margin: False margin_scheduler: obj: speakerlab.process.scheduler.MarginScheduler args: criterion: <loss> initial_margin: 0.0 final_margin: 0.3 increase_start_epoch: 20 fix_epoch: 50 step_per_epoch: checkpointer: obj: speakerlab.utils.checkpoint.Checkpointer args: checkpoints_dir: <exp_dir...