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2 changes: 1 addition & 1 deletion tensorflow_recommenders/layers/factorized_top_k.py
Original file line number Diff line number Diff line change
Expand Up @@ -466,7 +466,7 @@ def top_k(state: Tuple[tf.Tensor, tf.Tensor],
def enumerate_rows(batch: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
"""Enumerates rows in each batch using a total element counter."""

starting_counter = self._counter.read_value()
starting_counter = self._counter.value
end_counter = self._counter.assign_add(tf.shape(batch)[0])

return tf.range(starting_counter, end_counter), batch
Expand Down
6 changes: 4 additions & 2 deletions tensorflow_recommenders/metrics/factorized_top_k.py
Original file line number Diff line number Diff line change
Expand Up @@ -177,7 +177,8 @@ def update_state(
tf.reduce_sum(ids_match[:, :k], axis=1, keepdims=True),
0.0, 1.0
)
update_ops.append(metric.update_state(match_found, sample_weight))
metric.update_state(match_found, sample_weight)
update_ops.append(metric.result())
else:
# Score-based evaluation.
y_pred = tf.concat([positive_scores, top_k_predictions], axis=1)
Expand All @@ -189,7 +190,8 @@ def update_state(
predictions=y_pred,
k=k
)
update_ops.append(metric.update_state(top_k_accuracy, sample_weight))
metric.update_state(top_k_accuracy, sample_weight)
update_ops.append(metric.result())

return tf.group(update_ops)