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

计算 y_true 和 y_pred 之间的 Kullback-Leibler 散度损失。

继承自：Loss

用法

tf.keras.losses.KLDivergence(
    reduction=losses_utils.ReductionV2.AUTO, name='kl_divergence'
)

loss = y_true * log(y_true / y_pred)

看：https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence

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.
kl = tf.keras.losses.KLDivergence()
kl(y_true, y_pred).numpy()
0.458

# Calling with 'sample_weight'.
kl(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
0.366

# Using 'sum' reduction type.
kl = tf.keras.losses.KLDivergence(
    reduction=tf.keras.losses.Reduction.SUM)
kl(y_true, y_pred).numpy()
0.916

# Using 'none' reduction type.
kl = tf.keras.losses.KLDivergence(
    reduction=tf.keras.losses.Reduction.NONE)
kl(y_true, y_pred).numpy()
array([0.916, -3.08e-06], dtype=float32)

compile() API 的用法：

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