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Thank you for your contribution! Now ,I want to use it for 256 dimensional labels classification. Each label is 0 or 1. But the loss of training is strange. It will sharp drop at first. Then it will keep almost no changed.
The text was updated successfully, but these errors were encountered:
Thank you for your contribution! Now ,I want to use it for 256 dimensional labels classification. Each label is 0 or 1. But the loss of training is strange. It will sharp drop at first. Then it will keep almost no changed.
The text was updated successfully, but these errors were encountered: