| # Mulit-task learning example |
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| This is a simple example to show how to use mxnet for multi-task learning. It uses MNIST as an example and mocks up the multi-label task. |
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| ## Usage |
| First, you need to write a multi-task iterator on your own. The iterator needs to generate multiple labels according to your applications, and the label names should be specified in the `provide_label` function, which needs to be consist with the names of output layers. |
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| Then, if you want to show metrics of different tasks separately, you need to write your own metric class and specify the `num` parameter. In the `update` function of metric, calculate the metrics separately for different tasks. |