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Fix for ValueError: The shape of the target variable and the shape of the target value in variable.assign(value) must match #727

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Fix for ValueError: The shape of the target variable and the shape of the target value in variable.assign(value) must match #727

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3c771e4
Fix for 'Variable' object has no attribute 'read_value'
flynhigher Jul 4, 2024
f4b43cf
Fix metric.update_state() returning nonetype by replacing it with met…
flynhigher Jul 4, 2024
d0fced0
Fix for cannot convert 1 to dtype of string when initializing identifier
flynhigher Jul 4, 2024
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6 changes: 3 additions & 3 deletions tensorflow_recommenders/layers/factorized_top_k.py
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Original file line number Diff line number Diff line change
Expand Up @@ -373,7 +373,7 @@ def __init__(self,
self._num_parallel_calls = num_parallel_calls
self._sorted = sorted_order

self._counter = self.add_weight("counter", dtype=tf.int32, trainable=False)
self._counter = self.add_weight(name="counter", dtype=tf.int32, trainable=False)

def index_from_dataset(
self,
Expand Down 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 Expand Up @@ -545,7 +545,7 @@ def index(
)

# We need any value that has the correct dtype.
identifiers_initial_value = tf.zeros((), dtype=identifiers.dtype)
identifiers_initial_value = tf.zeros(identifiers.shape, dtype=identifiers.dtype)

self._identifiers = self.add_weight(
name="identifiers",
Expand Down
6 changes: 4 additions & 2 deletions tensorflow_recommenders/metrics/factorized_top_k.py
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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)