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

计算 y_true 和 y_pred 之间的分类铰链损失。

用法

tf.keras.losses.categorical_hinge(
    y_true, y_pred
)

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 = np.random.randint(0, 3, size=(2,))
y_true = tf.keras.utils.to_categorical(y_true, num_classes=3)
y_pred = np.random.random(size=(2, 3))
loss = tf.keras.losses.categorical_hinge(y_true, y_pred)
assert loss.shape == (2,)
pos = np.sum(y_true * y_pred, axis=-1)
neg = np.amax((1. - y_true) * y_pred, axis=-1)
assert np.array_equal(loss.numpy(), np.maximum(0., neg - pos + 1.))