Python tf.keras.losses.CategoricalHinge用法及代码示例

计算 y_true 和 y_pred 之间的分类铰链损失。

继承自：Loss

用法

tf.keras.losses.CategoricalHinge(
    reduction=losses_utils.ReductionV2.AUTO, name='categorical_hinge'
)

loss = maximum(neg - pos + 1, 0) 其中neg=maximum((1-y_true)*y_pred) and pos=sum(y_true*y_pred)

单机使用：

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

# Using 'sum' reduction type.
h = tf.keras.losses.CategoricalHinge(
    reduction=tf.keras.losses.Reduction.SUM)
h(y_true, y_pred).numpy()
2.8

# Using 'none' reduction type.
h = tf.keras.losses.CategoricalHinge(
    reduction=tf.keras.losses.Reduction.NONE)
h(y_true, y_pred).numpy()
array([1.2, 1.6], dtype=float32)

compile() API 的用法：

model.compile(optimizer='sgd', loss=tf.keras.losses.CategoricalHinge())