d6bcc56001
* Added confusion metrics -- still using TF ops * Fixed structure + tests pass for TF (still need to port to multi-backend) * Got rid of most tf deps, still a few more to go * Full removal of TF. Tests pass for both Jax and TF * Full removal of TF. Tests pass for both Jax and TF * Formatting * Formatting * Review comments * More review comments + formatting
55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
from keras_core.api_export import keras_core_export
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from keras_core.metrics.confusion_metrics import FalsePositives
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from keras_core.metrics.metric import Metric
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from keras_core.metrics.reduction_metrics import Mean
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from keras_core.metrics.reduction_metrics import MeanMetricWrapper
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from keras_core.metrics.reduction_metrics import Sum
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from keras_core.metrics.regression_metrics import MeanSquaredError
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def deserialize(obj):
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raise NotImplementedError
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@keras_core_export("keras_core.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 = metrics.get("categorical_crossentropy")
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>>> type(metric)
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<class 'function'>
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>>> metric = 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 = 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
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function 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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"""
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if isinstance(identifier, dict):
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return deserialize(identifier)
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elif isinstance(identifier, str):
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# TODO
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# return deserialize(str(identifier))
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return globals()[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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