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ValueError: Could not determine an appropriate value for field `logits` in object `tfp.distributions.CosinePenalizedPlackettLuce("PlackettLuce", batch_shape=[?],
event_shape=[3], dtype=int32)`. Looked for
1. an attr called `logits`,
2. an attr called `_logits`,
3. an entry in `obj.parameters` with key "logits".
no_penalty_ranking_policy error:
ValueError: Could not determine an appropriate value for field `features` in object `tfp.distributions.NoPenaltyPlackettLuce("PlackettLuce", batch_shape=[?], event_shape=[50], dtype=int32)`.
Looked for
1. an attr called `features`,
2. an attr called `_features`,
3. an entry in `obj.parameters` with key "features".
descending_score_ranking_policy error:
TypeError: To be compatible with tf.function, Python functions must return zero or more Tensors or ExtensionTypes or None values; in compilation of <function PolicySaver.__init__.<locals>.polymorphic_distribution_fn at 0x7ced7451f010>, found return value of type DescendingScoreSampler, which is not a Tensor or ExtensionType.
The text was updated successfully, but these errors were encountered:
What is recommended way to save ranking policies?
For each agent policy in the ranking tutorial, im getting errors like below when trying to save with PolicySaver
penalize_cosine_distance_ranking_policy
error:no_penalty_ranking_policy
error:descending_score_ranking_policy
error:TypeError: To be compatible with tf.function, Python functions must return zero or more Tensors or ExtensionTypes or None values; in compilation of <function PolicySaver.__init__.<locals>.polymorphic_distribution_fn at 0x7ced7451f010>, found return value of type DescendingScoreSampler, which is not a Tensor or ExtensionType.
The text was updated successfully, but these errors were encountered: