Hinge metrics for "maximum-margin" classification
Hinge class
tf_keras.metrics.Hinge(name="hinge", dtype=None)
Computes 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.
Arguments
- name: (Optional) string name of the metric instance.
- dtype: (Optional) data type of the metric result.
Standalone usage:
>>> m = tf.keras.metrics.Hinge()
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.result().numpy()
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().numpy()
1.1
Usage with compile() API:
model.compile(
optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.Hinge()])
SquaredHinge class
tf_keras.metrics.SquaredHinge(name="squared_hinge", dtype=None)
Computes the squared 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.
Arguments
- name: (Optional) string name of the metric instance.
- dtype: (Optional) data type of the metric result.
Standalone usage:
>>> m = tf.keras.metrics.SquaredHinge()
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.result().numpy()
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().numpy()
1.46
Usage with compile() API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.SquaredHinge()])
CategoricalHinge class
tf_keras.metrics.CategoricalHinge(name="categorical_hinge", dtype=None)
Computes the categorical hinge metric between y_true and y_pred.
Arguments
- name: (Optional) string name of the metric instance.
- dtype: (Optional) data type of the metric result.
Standalone usage:
>>> m = tf.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().numpy()
1.2
Usage with compile() API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.CategoricalHinge()])