hyperparameter in textRNN

[longbowking]

https://github.com/brightmart/text_classification/blob/68e2fcf57a8dcec7e7d12f78953ed570451f0076/a03_TextRNN/p8_TextRNN_model.py#L74-L83

• the final loss is defined as the sum of cross_entropy_loss and L2-loss to penalty large variables, where a hyperparameter l2_lambda is used to balance these two items. In the algorithm, as the hyperparameter l2_lambda is directly set to 0.0001, would it be too small(or too large) under some circumstances such that one in the two items loses its contribution to the whole loss?
• in general, is there any pratical methods to guide me to set values of hyperparameters, e.g. l2_lambda @brightmart

[brightmart]

hi,

you can search l2_lambda among 0.01,0.001,0.0001, and choose the value that give you lowerest validation loss.

bright

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发件人: longbowking [notifications@github.com] 发送时间: 2018年1月27日 23:09 收件人: brightmart/text_classification 抄送: brightmart; Mention 主题: [brightmart/text_classification] on the hyperparameter in p8_text (#32)

https://github.com/brightmart/text_classification/blob/68e2fcf57a8dcec7e7d12f78953ed570451f0076/a03_TextRNN/p8_TextRNN_model.py#L74-L83

• the final loss is defined as the sum of cross_entropy_loss and L2-loss to penalty large variables, where a hyperparameter l2_lambda is used to balance these two items. In the algorithm, as the hyperparameter l2_lambda is directly set to 0.0001, would it be too small(or too large) under some circumstances such that one in the two items loses its contribution to the whole loss?
• in general, is there any pratical methods to guide me to set values of hyperparameters, e.g. l2_lambda @brightmarthttps://github.com/brightmart

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