TensorflowSimilarity学习笔记12

m0_59345023

于 2021-12-28 14:55:29 发布

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文章标签： tensorflow 人工智能

本文链接：https://blog.youkuaiyun.com/m0_59345023/article/details/122193419

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本文介绍了在Tensorflow中F1分数的概念，它是衡量分类器性能的一个指标，结合了精度和召回率。通过调和平均值计算得出，强调了在精度和召回率之间的权衡。举例说明了F1分数如何在不同情况下评价模型的表现。

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import tensorflow as tf

from tensorflow_similarity.types import FloatTensor
from .classification_metric import ClassificationMetric


class F1Score(ClassificationMetric):
    """Calculates the harmonic mean of precision and recall.
    Computes the F-1 Score given the query classification counts. The metric is
    computed as follows:
    $$
    F_1 = 2 \cdot \frac{\textrm{precision} \cdot \textrm{recall}}{\textrm{precision} + \textrm{recall}}
    $$
    args:
        name: Name associated with a specific metric object, e.g.,
        f1@0.1
    Usage with `tf.similarity.models.SimilarityModel()`:
    ```python
    model.calibra

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