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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Keras metric for computing the (weighted) variance of a tensor."""
from typing import Optional
import tensorflow as tf, tf_keras
class Variance(tf_keras.metrics.Metric):
"""Computes the (weighted) variance of the given values.
For example, if values is [1, 2, 1, 4] then the variance is 1.5.
If the weights were specified as [1, 0, 1, 0] then the variance would be 0.
If `sample_weight` is `None`, weights default to 1.
Use `sample_weight` of 0 to mask values.
Standalone usage:
>>> m = Variance()
>>> m.update_state([1, 2, 1, 4])
>>> m.result().numpy()
1.5
>>> m.reset_state()
>>> m.update_state([1, 2, 1, 4], sample_weight=[1, 0, 1, 0])
>>> m.result().numpy()
0.0
Usage within a Keras layer:
```python
layer.add_metric(Variance(name="variance")(values))
```
"""
def __init__(self, name: str = "variance", dtype: Optional[tf.DType] = None):
"""Initializes a Variance metric instance.
Args:
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.
"""
super().__init__(name=name, dtype=dtype)
self._first_moment = tf_keras.metrics.Mean(name="first_moment", dtype=dtype)
self._second_moment = tf_keras.metrics.Mean(
name="second_moment", dtype=dtype
)
def update_state(
self, values: tf.Tensor, sample_weight: Optional[tf.Tensor] = None
):
self._first_moment.update_state(values=values, sample_weight=sample_weight)
self._second_moment.update_state(
values=tf.math.square(values), sample_weight=sample_weight
)
def result(self) -> tf.Tensor:
return self._second_moment.result() - tf.math.square(
self._first_moment.result()
)
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