209 lines
8.4 KiB
Python
209 lines
8.4 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""All Keras metrics."""
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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# Base classes and utilities
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from keras.metrics.base_metric import Mean
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from keras.metrics.base_metric import MeanMetricWrapper
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from keras.metrics.base_metric import MeanTensor
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from keras.metrics.base_metric import Metric
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from keras.metrics.base_metric import Reduce
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from keras.metrics.base_metric import Sum
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from keras.metrics.base_metric import SumOverBatchSize
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from keras.metrics.base_metric import SumOverBatchSizeMetricWrapper
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from keras.metrics.base_metric import clone_metric
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from keras.metrics.base_metric import clone_metrics
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from keras.saving.legacy import serialization as legacy_serialization
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from keras.saving.legacy.serialization import deserialize_keras_object
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from keras.saving.legacy.serialization import serialize_keras_object
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# Individual metric classes
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# Accuracy metrics
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from keras.metrics.accuracy_metrics import Accuracy
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from keras.metrics.accuracy_metrics import BinaryAccuracy
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from keras.metrics.accuracy_metrics import CategoricalAccuracy
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from keras.metrics.accuracy_metrics import SparseCategoricalAccuracy
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from keras.metrics.accuracy_metrics import SparseTopKCategoricalAccuracy
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from keras.metrics.accuracy_metrics import TopKCategoricalAccuracy
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from keras.metrics.accuracy_metrics import accuracy
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from keras.metrics.accuracy_metrics import binary_accuracy
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from keras.metrics.accuracy_metrics import categorical_accuracy
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from keras.metrics.accuracy_metrics import sparse_categorical_accuracy
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from keras.metrics.accuracy_metrics import sparse_top_k_categorical_accuracy
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from keras.metrics.accuracy_metrics import top_k_categorical_accuracy
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# Probabilistic metrics
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from keras.metrics.probabilistic_metrics import BinaryCrossentropy
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from keras.metrics.probabilistic_metrics import CategoricalCrossentropy
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from keras.metrics.probabilistic_metrics import KLDivergence
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from keras.metrics.probabilistic_metrics import Poisson
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from keras.metrics.probabilistic_metrics import SparseCategoricalCrossentropy
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from keras.metrics.probabilistic_metrics import binary_crossentropy
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from keras.metrics.probabilistic_metrics import categorical_crossentropy
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from keras.metrics.probabilistic_metrics import poisson
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from keras.metrics.probabilistic_metrics import kullback_leibler_divergence
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from keras.metrics.probabilistic_metrics import sparse_categorical_crossentropy
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# Regression metrics
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from keras.metrics.regression_metrics import CosineSimilarity
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from keras.metrics.regression_metrics import LogCoshError
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from keras.metrics.regression_metrics import MeanAbsoluteError
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from keras.metrics.regression_metrics import MeanAbsolutePercentageError
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from keras.metrics.regression_metrics import MeanRelativeError
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from keras.metrics.regression_metrics import MeanSquaredError
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from keras.metrics.regression_metrics import MeanSquaredLogarithmicError
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from keras.metrics.regression_metrics import RootMeanSquaredError
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from keras.metrics.regression_metrics import cosine_similarity
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from keras.metrics.regression_metrics import logcosh
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from keras.metrics.regression_metrics import mean_absolute_error
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from keras.metrics.regression_metrics import mean_absolute_percentage_error
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from keras.metrics.regression_metrics import mean_squared_error
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from keras.metrics.regression_metrics import mean_squared_logarithmic_error
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# Confusion metrics
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from keras.metrics.confusion_metrics import AUC
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from keras.metrics.confusion_metrics import FalseNegatives
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from keras.metrics.confusion_metrics import FalsePositives
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from keras.metrics.confusion_metrics import Precision
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from keras.metrics.confusion_metrics import PrecisionAtRecall
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from keras.metrics.confusion_metrics import Recall
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from keras.metrics.confusion_metrics import RecallAtPrecision
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from keras.metrics.confusion_metrics import SensitivityAtSpecificity
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from keras.metrics.confusion_metrics import SensitivitySpecificityBase
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from keras.metrics.confusion_metrics import SpecificityAtSensitivity
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from keras.metrics.confusion_metrics import TrueNegatives
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from keras.metrics.confusion_metrics import TruePositives
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# IoU metrics
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from keras.metrics.iou_metrics import BinaryIoU
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from keras.metrics.iou_metrics import IoU
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from keras.metrics.iou_metrics import MeanIoU
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from keras.metrics.iou_metrics import OneHotIoU
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from keras.metrics.iou_metrics import OneHotMeanIoU
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# Hinge metrics
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from keras.metrics.hinge_metrics import CategoricalHinge
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from keras.metrics.hinge_metrics import Hinge
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from keras.metrics.hinge_metrics import SquaredHinge
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from keras.metrics.hinge_metrics import categorical_hinge
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from keras.metrics.hinge_metrics import squared_hinge
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from keras.metrics.hinge_metrics import hinge
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# Aliases
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acc = ACC = accuracy
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bce = BCE = binary_crossentropy
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mse = MSE = mean_squared_error
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mae = MAE = mean_absolute_error
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mape = MAPE = mean_absolute_percentage_error
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msle = MSLE = mean_squared_logarithmic_error
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log_cosh = logcosh
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cosine_proximity = cosine_similarity
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@keras_export("keras.metrics.serialize")
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def serialize(metric, use_legacy_format=False):
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"""Serializes metric function or `Metric` instance.
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Args:
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metric: A Keras `Metric` instance or a metric function.
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Returns:
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Metric configuration dictionary.
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"""
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if use_legacy_format:
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return legacy_serialization.serialize_keras_object(metric)
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return serialize_keras_object(metric)
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@keras_export("keras.metrics.deserialize")
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def deserialize(config, custom_objects=None, use_legacy_format=False):
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"""Deserializes a serialized metric class/function instance.
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Args:
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config: Metric configuration.
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custom_objects: Optional dictionary mapping names (strings) to custom
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objects (classes and functions) to be considered during deserialization.
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Returns:
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A Keras `Metric` instance or a metric function.
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"""
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if use_legacy_format:
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return legacy_serialization.deserialize_keras_object(
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config,
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module_objects=globals(),
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custom_objects=custom_objects,
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printable_module_name="metric function",
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)
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return deserialize_keras_object(
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config,
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module_objects=globals(),
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custom_objects=custom_objects,
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printable_module_name="metric function",
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)
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@keras_export("keras.metrics.get")
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def get(identifier):
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"""Retrieves a Keras metric as a `function`/`Metric` class instance.
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The `identifier` may be the string name of a metric function or class.
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>>> metric = tf.keras.metrics.get("categorical_crossentropy")
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>>> type(metric)
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<class 'function'>
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>>> metric = tf.keras.metrics.get("CategoricalCrossentropy")
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>>> type(metric)
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<class '...metrics.CategoricalCrossentropy'>
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You can also specify `config` of the metric to this function by passing dict
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containing `class_name` and `config` as an identifier. Also note that the
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`class_name` must map to a `Metric` class
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>>> identifier = {"class_name": "CategoricalCrossentropy",
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... "config": {"from_logits": True}}
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>>> metric = tf.keras.metrics.get(identifier)
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>>> type(metric)
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<class '...metrics.CategoricalCrossentropy'>
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Args:
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identifier: A metric identifier. One of None or string name of a metric
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function/class or metric configuration dictionary or a metric function
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or a metric class instance
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Returns:
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A Keras metric as a `function`/ `Metric` class instance.
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Raises:
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ValueError: If `identifier` cannot be interpreted.
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"""
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if isinstance(identifier, dict):
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use_legacy_format = "module" not in identifier
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return deserialize(identifier, use_legacy_format=use_legacy_format)
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elif isinstance(identifier, str):
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return deserialize(str(identifier))
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elif callable(identifier):
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return identifier
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else:
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raise ValueError(f"Could not interpret metric identifier: {identifier}")
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