这篇教程Pytorch训练网络过程中loss突然变为0的解决方案写得很实用，希望能帮到您。
问题// loss 突然变成0python train.py -b=8INFO: Using device cpuINFO: Network: 1 input channels 7 output channels (classes) Bilinear upscalingINFO: Creating dataset with 868 examplesINFO: Starting training: Epochs: 5 Batch size: 8 Learning rate: 0.001 Training size: 782 Validation size: 86 Checkpoints: True Device: cpu Images scaling: 1 Epoch 1/5: 10%|██████████████▏ | 80/782 [01:33<13:21, 1.14s/img, loss (batch)=0.886INFO: Validation cross entropy: 1.86862473487854 Epoch 1/5: 20%|███████████████████████████▊ | 160/782 [03:34<11:51, 1.14s/img, loss (batch)=2.35e-7INFO: Validation cross entropy: 5.887489884504049e-10 Epoch 1/5: 31%|███████████████████████████████████████████▌ | 240/782 [05:41<11:29, 1.27s/img, loss (batch)=0INFO: Validation cross entropy: 0.0 Epoch 1/5: 41%|██████████████████████████████████████████████████████████ | 320/782 [07:49<09:16, 1.20s/img, loss (batch)=0INFO: Validation cross entropy: 0.0 Epoch 1/5: 51%|████████████████████████████████████████████████████████████████████████▋ | 400/782 [09:55<07:31, 1.18s/img, loss (batch)=0INFO: Validation cross entropy: 0.0 Epoch 1/5: 61%|███████████████████████████████████████████████████████████████████████████████████████▏ | 480/782 [12:02<05:58, 1.19s/img, loss (batch)=0INFO: Validation cross entropy: 0.0 Epoch 1/5: 72%|█████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 560/782 [14:04<04:16, 1.15s/img, loss (batch)=0INFO: Validation cross entropy: 0.0 Epoch 1/5: 82%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 640/782 [16:11<02:49, 1.20s/img, loss (batch)=0INFO: Validation cross entropy: 0.0 Epoch 1/5: 92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 720/782 [18:21<01:18, 1.26s/img, loss (batch)=0INFO: Validation cross entropy: 0.0 Epoch 1/5: 94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 736/782 [19:17<01:12, 1.57s/img, loss (batch)=0]Traceback (most recent call last): File "train.py", line 182, in <module> val_percent=args.val / 100) File "train.py", line 66, in train_net for batch in train_loader: File "/public/home/lidd/.conda/envs/lgg2/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 819, in __next__ return self._process_data(data) File "/public/home/lidd/.conda/envs/lgg2/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 846, in _process_data data.reraise() File "/public/home/lidd/.conda/envs/lgg2/lib/python3.6/site-packages/torch/_utils.py", line 385, in reraise raise self.exc_type(msg)RuntimeError: Caught RuntimeError in DataLoader worker process 4.Original Traceback (most recent call last): File "/public/home/lidd/.conda/envs/lgg2/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop data = fetcher.fetch(index) File "/public/home/lidd/.conda/envs/lgg2/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch return self.collate_fn(data) File "/public/home/lidd/.conda/envs/lgg2/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py", line 74, in default_collate return {key: default_collate([d[key] for d in batch]) for key in elem} File "/public/home/lidd/.conda/envs/lgg2/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py", line 74, in <dictcomp> return {key: default_collate([d[key] for d in batch]) for key in elem} File "/public/home/lidd/.conda/envs/lgg2/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py", line 55, in default_collate return torch.stack(batch, 0, out=out)RuntimeError: Expected object of scalar type Double but got scalar type Byte for sequence element 4 in sequence argument at position #1 'tensors'交叉熵损失函数是衡量输出与标签之间的损失，通过求导确定梯度下降的方向。 loss突然变为0，有两种可能性。一是因为预测输出为0，二是因为标签为0。 如果是因为标签为0，那么一开始loss就可能为0. 检查参数初始化 检查前向传播的网络 检查loss的计算格式 检查梯度下降 是否出现梯度消失。 实际上是标签出了错误 补充：pytorch训练出现loss=na 遇到一个很坑的情况，在pytorch训练过程中出现loss=nan的情况 有以下几种可能：1.学习率太高。 2.loss函数有问题 3.对于回归问题，可能出现了除0 的计算，加一个很小的余项可能可以解决 4.数据本身，是否存在Nan、inf，可以用np.isnan(),np.isinf()检查一下input和target 5.target本身应该是能够被loss函数计算的，比如sigmoid激活函数的target应该大于0，同样的需要检查数据集 以上为个人经验，希望能给大家一个参考，也希望大家多多支持51zixue.net。如有错误或未考虑完全的地方，望不吝赐教。
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