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wenruij avatar wenruij commented on July 19, 2024 2

@hexiangnan
I just tried --loss_type=log_loss, there maybe some points need be updated:

  1. Line 149: self.lambda_bilinear > 0, the typo is different from the one self.lamda_bilinear in __init__

  2. Line 150 and Line 152: parameter oftf.contrib.losses.log_loss should be weights, not weight. Maybe TF 1.0 using weight, but TF 1.2+ using weights

  3. Line 304: decision condition of early_stop for self.loss_type == 'log_loss' should be the same as self.loss_type == 'square_loss', or the train loops will always stop at the 6th epoch.

  4. there exist 2 different loss calculation rules: tf.contrib.losses.log_loss and sklearn.metrics.log_loss. Should it be better to unify the calculation rule? But maybe you have your own consideration to use 2 rules. Please ignore my advice, if that is the case.

At last, if you consider using the tf.estimator.Estimator and tf.contrib.learn.Experiment, please let me know. I'm pleasure to join the contribution of this part. Your idea of neural factorization machine looks great ~

from neural_factorization_machine.

hexiangnan avatar hexiangnan commented on July 19, 2024

from neural_factorization_machine.

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