o 2h @sddlmZddlmZddlmZddlmZddlmZedGdddejZ ed Gd d d ejZ ed Gd ddejZ dS)) keras_export)categorical_hinge)hinge) squared_hinge)reduction_metricszkeras.metrics.Hingec*eZdZdZdfdd ZddZZS) HingeaoComputes the hinge metric between `y_true` and `y_pred`. `y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Examples: >>> m = keras.metrics.Hinge() >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]]) >>> m.result() 1.3 >>> m.reset_state() >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]], ... sample_weight=[1, 0]) >>> m.result() 1.1 rNctjt||dd|_dSN)fnnamedtypedown)super__init__r _directionselfr r  __class__Z/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/metrics/hinge_metrics.pyr  zHinge.__init__cC|j|jdSNr r rrrrr get_config%zHinge.get_config)rN__name__ __module__ __qualname____doc__rr __classcell__rrrrrrzkeras.metrics.SquaredHingecr) SquaredHingeawComputes the hinge metric between `y_true` and `y_pred`. `y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Example: >>> m = keras.metrics.SquaredHinge() >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]]) >>> m.result() 1.86 >>> m.reset_state() >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]], ... sample_weight=[1, 0]) >>> m.result() 1.46 rNcr r )rrrrrrrrrArzSquaredHinge.__init__cCrrrrrrrrFrzSquaredHinge.get_config)rNrrrrrr&)r%r&zkeras.metrics.CategoricalHingecr) CategoricalHingeaComputes the categorical hinge metric between `y_true` and `y_pred`. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Example: >>> m = keras.metrics.CategoricalHinge() >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]]) >>> m.result().numpy() 1.4000001 >>> m.reset_state() >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]], ... sample_weight=[1, 0]) >>> m.result() 1.2 rNcr r )rrrrrrrrr^rzCategoricalHinge.__init__cCrrrrrrrrcrzCategoricalHinge.get_config)rNrrrrrr'Jsr'N) keras.src.api_exportrkeras.src.losses.lossesrrrkeras.src.metricsrMeanMetricWrapperrr&r'rrrrs