Datasets:
HenryAI
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KerasBERTv1-Data

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tf.keras.losses.Hinge(reduction="auto", name="hinge")
Computes the hinge loss between y_true and y_pred.
loss = maximum(1 - y_true * y_pred, 0)
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.
Standalone usage:
>>> y_true = [[0., 1.], [0., 0.]]
>>> y_pred = [[0.6, 0.4], [0.4, 0.6]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> h = tf.keras.losses.Hinge()
>>> h(y_true, y_pred).numpy()
1.3
>>> # Calling with 'sample_weight'.
>>> h(y_true, y_pred, sample_weight=[1, 0]).numpy()
0.55
>>> # Using 'sum' reduction type.
>>> h = tf.keras.losses.Hinge(
... reduction=tf.keras.losses.Reduction.SUM)
>>> h(y_true, y_pred).numpy()
2.6
>>> # Using 'none' reduction type.
>>> h = tf.keras.losses.Hinge(
... reduction=tf.keras.losses.Reduction.NONE)
>>> h(y_true, y_pred).numpy()
array([1.1, 1.5], dtype=float32)
Usage with the compile() API:
model.compile(optimizer='sgd', loss=tf.keras.losses.Hinge())
SquaredHinge class
tf.keras.losses.SquaredHinge(reduction="auto", name="squared_hinge")
Computes the squared hinge loss between y_true and y_pred.
loss = square(maximum(1 - y_true * y_pred, 0))
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.
Standalone usage:
>>> y_true = [[0., 1.], [0., 0.]]
>>> y_pred = [[0.6, 0.4], [0.4, 0.6]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> h = tf.keras.losses.SquaredHinge()
>>> h(y_true, y_pred).numpy()
1.86
>>> # Calling with 'sample_weight'.
>>> h(y_true, y_pred, sample_weight=[1, 0]).numpy()
0.73
>>> # Using 'sum' reduction type.
>>> h = tf.keras.losses.SquaredHinge(
... reduction=tf.keras.losses.Reduction.SUM)
>>> h(y_true, y_pred).numpy()
3.72
>>> # Using 'none' reduction type.
>>> h = tf.keras.losses.SquaredHinge(
... reduction=tf.keras.losses.Reduction.NONE)
>>> h(y_true, y_pred).numpy()
array([1.46, 2.26], dtype=float32)
Usage with the compile() API:
model.compile(optimizer='sgd', loss=tf.keras.losses.SquaredHinge())
CategoricalHinge class
tf.keras.losses.CategoricalHinge(reduction="auto", name="categorical_hinge")
Computes the categorical hinge loss between y_true and y_pred.
loss = maximum(neg - pos + 1, 0) where neg=maximum((1-y_true)*y_pred) and pos=sum(y_true*y_pred)
Standalone usage:
>>> y_true = [[0, 1], [0, 0]]
>>> y_pred = [[0.6, 0.4], [0.4, 0.6]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> h = tf.keras.losses.CategoricalHinge()
>>> h(y_true, y_pred).numpy()
1.4
>>> # Calling with 'sample_weight'.
>>> h(y_true, y_pred, sample_weight=[1, 0]).numpy()
0.6