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tf.keras.metrics.Hinge

TensorFlow 2.0 version View source on GitHub

Class Hinge

Computes the hinge metric between y_true and y_pred.

Aliases:

  • Class tf.compat.v1.keras.metrics.Hinge
  • Class tf.compat.v2.keras.metrics.Hinge
  • Class tf.compat.v2.metrics.Hinge

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.

For example, if y_true is [-1., 1., 1.], and y_pred is [0.6, -0.7, -0.5] the hinge metric value is 1.6.

Usage:

m = tf.keras.metrics.Hinge()
m.update_state([-1., 1., 1.], [0.6, -0.7, -0.5])

# result = max(0, 1-y_true * y_pred) = [1.6 + 1.7 + 1.5] / 3

print('Final result: ', m.result().numpy())  # Final result: 1.6

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', metrics=[tf.keras.metrics.Hinge()])

__init__

View source

__init__(
    name='hinge',
    dtype=None
)

Methods

reset_states

View source

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

View source

result()

update_state

View source

update_state(
    y_true,
    y_pred,
    sample_weight=None
)

Accumulates metric statistics.

y_true and y_pred should have the same shape.

Args:

  • y_true: The ground truth values.
  • y_pred: The predicted values.
  • sample_weight: Optional weighting of each example. Defaults to 1. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true.

Returns:

Update op.