tf.keras.losses.squared_hinge
Computes the squared hinge loss between y_true and y_pred.
tf.keras.losses.squared_hinge(
y_true, y_pred
)
loss = mean(square(maximum(1 - y_true * y_pred, 0)), axis=-1)
Standalone usage:
y_true = np.random.choice([-1, 1], size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = tf.keras.losses.squared_hinge(y_true, y_pred)
assert loss.shape == (2,)
assert np.array_equal(
loss.numpy(),
np.mean(np.square(np.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 we will convert them to -1 or 1. shape = [batch_size, d0, .. dN]. |
y_pred |
The predicted values. shape = [batch_size, d0, .. dN]. |
| Returns | |
|---|---|
Squared hinge loss values. shape = [batch_size, d0, .. dN-1]. |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/losses/squared_hinge