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dssm模型使用subclass方式,在实际预测的时候因为召回要分别取用户的embedding和物品的embedding,那线上推理比如使用模型去推理用户的embedding,在使用subcalss方式中怎么把用户的网络结构拿到呢。我看DeepMatch使用的function API 中使用的是model.setattr("user_input", user_inputs_list),model.setattr("user_embedding", user_dnn_out)方式。在subclass实现里面有类似方法吗。 感谢
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dssm模型使用subclass方式,在实际预测的时候因为召回要分别取用户的embedding和物品的embedding,那线上推理比如使用模型去推理用户的embedding,在使用subcalss方式中怎么把用户的网络结构拿到呢。我看DeepMatch使用的function API 中使用的是model.setattr("user_input", user_inputs_list),model.setattr("user_embedding", user_dnn_out)方式。在subclass实现里面有类似方法吗。 感谢
The text was updated successfully, but these errors were encountered: