Comments (2)
Hi sano, you can try setting weights for different classes in losses for data skewing problem. We provided example configuration file for how to set weights in losses.
from neuronblocks.
losses": [
{
"type": "CrossEntropyLoss",
"conf": {
"weight": [0.1,0.9],
"size_average": true
},
"inputs": ["output","label"]
from neuronblocks.
Related Issues (20)
- non-contiguous input HOT 1
- print OOV ratio during encoding stage
- train unique word embeddding of unk tokens
- add a demo script
- AttributeError: 'ReLU' object has no attribute 'weight' HOT 1
- 请问支持中文了吗?字向量那一部分的机制目前支持中文吗? HOT 1
- how to deal the large data when training model HOT 6
- no such file 'requirements.txt' HOT 2
- Segmentation fault(error) HOT 3
- CNNCharEmbedding ERROR
- output layer only 'Linear Layer'? HOT 6
- Can NeuronBlocks support tensorflow ? HOT 1
- Issues with NLTK packages HOT 1
- Issue when running the Quick Start steps from Tutorial
- how to get the softmax output vectors instead of the predicted label?
- support pytorch>=1.7
- install issue - ERROR: scipy 1.7.1 has requirement numpy<1.23.0,>=1.16.5, but you'll have numpy 1.16.3 which is incompatible. HOT 1
- This repo is missing important files HOT 1
- Project dependencies may have API risk issues
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from neuronblocks.