tf.keras.losses.hinge | TensorFlow v2.16.1 (original) (raw)
tf.keras.losses.hinge
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Computes the hinge loss between y_true & y_pred.
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tf.keras.losses.hinge(
y_true, y_pred
)
Formula:
loss = mean(maximum(1 - y_true * y_pred, 0), axis=-1)
| Args | |
|---|---|
| y_true | The ground truth values. y_true values are expected to be -1 or 1. If binary (0 or 1) labels are provided they will be converted to -1 or 1 with shape = [batch_size, d0, .. dN]. |
| y_pred | The predicted values with shape = [batch_size, d0, .. dN]. |
| Returns |
|---|
| Hinge loss values with shape = [batch_size, d0, .. dN-1]. |
Example:
y_true = np.random.choice([-1, 1], size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = keras.losses.hinge(y_true, y_pred)
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Last updated 2024-06-07 UTC.