95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
from keras_core.api_export import keras_core_export
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from keras_core.losses.losses import categorical_hinge
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from keras_core.losses.losses import hinge
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from keras_core.losses.losses import squared_hinge
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from keras_core.metrics import reduction_metrics
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@keras_core_export("keras_core.metrics.Hinge")
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class Hinge(reduction_metrics.MeanMetricWrapper):
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"""Computes the hinge metric between `y_true` and `y_pred`.
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`y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are
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provided we will convert them to -1 or 1.
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Args:
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name: (Optional) string name of the metric instance.
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dtype: (Optional) data type of the metric result.
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Standalone usage:
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>>> m = keras_core.metrics.Hinge()
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>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
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>>> m.result()
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1.3
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>>> m.reset_state()
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>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
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... sample_weight=[1, 0])
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>>> m.result()
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1.1
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"""
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def __init__(self, name="hinge", dtype=None):
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super().__init__(fn=hinge, name=name, dtype=dtype)
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def get_config(self):
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return {"name": self.name, "dtype": self.dtype}
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@keras_core_export("keras_core.metrics.SquaredHinge")
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class SquaredHinge(reduction_metrics.MeanMetricWrapper):
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"""Computes the hinge metric between `y_true` and `y_pred`.
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`y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are
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provided we will convert them to -1 or 1.
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Args:
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name: (Optional) string name of the metric instance.
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dtype: (Optional) data type of the metric result.
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Standalone usage:
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>>> m = keras_core.metrics.SquaredHinge()
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>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
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>>> m.result()
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1.86
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>>> m.reset_state()
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>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
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... sample_weight=[1, 0])
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>>> m.result()
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1.46
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"""
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def __init__(self, name="squared_hinge", dtype=None):
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super().__init__(fn=squared_hinge, name=name, dtype=dtype)
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def get_config(self):
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return {"name": self.name, "dtype": self.dtype}
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@keras_core_export("keras_core.metrics.CategoricalHinge")
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class CategoricalHinge(reduction_metrics.MeanMetricWrapper):
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"""Computes the categorical hinge metric between `y_true` and `y_pred`.
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Args:
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name: (Optional) string name of the metric instance.
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dtype: (Optional) data type of the metric result.
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Standalone usage:
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>>> m = keras_core.metrics.CategoricalHinge()
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>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
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>>> m.result().numpy()
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1.4000001
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>>> m.reset_state()
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>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
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... sample_weight=[1, 0])
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>>> m.result()
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1.2
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"""
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def __init__(self, name="categorical_hinge", dtype=None):
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super().__init__(fn=categorical_hinge, name=name, dtype=dtype)
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def get_config(self):
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return {"name": self.name, "dtype": self.dtype}
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