WMRB on Serial Predictions #45
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jfkirk
commented
Mar 9, 2018
tensorrec/tensorrec.py
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| self.tf_learning_rate = tf.placeholder('float', None) | ||
| self.tf_alpha = tf.placeholder('float', None) | ||
| self.tf_sampled_item_indices = tf.placeholder('int64', [None, None]) | ||
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| # from nose.tools import set_trace;set_trace() |
jfkirk
commented
Mar 9, 2018
| 'tf_rankings': self.tf_rankings, | ||
| }) | ||
| if self.loss_graph_factory.is_sample_based: | ||
| tf_sample_predictions = gather_sampled_item_predictions( | ||
| tf_prediction=self.tf_prediction, tf_sampled_item_indices=self.tf_sampled_item_indices | ||
| tf_sample_predictions = densify_sampled_item_predictions( |
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Note: should rename one of these tf_sample_predictions
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Increases efficiency of WMRB (or any sample-based loss) by allowing it to function on a serial prediction of sampled items, rather than sampling from a dense prediction of all items.