tf.keras.losses.KLD

Computes Kullback-Leibler divergence loss between y_true & y_pred.

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

Formula:

loss = y_true * log(y_true / y_pred)

y_true and y_pred are expected to be probability distributions, with values between 0 and 1. They will get clipped to the [0, 1] range.

y_true Tensor of true targets.
y_pred Tensor of predicted targets.

KL Divergence loss values with shape = [batch_size, d0, .. dN-1].

Example:

y_true = np.random.randint(0, 2, size=(2, 3)).astype(np.float32)
y_pred = np.random.random(size=(2, 3))
loss = keras.losses.kl_divergence(y_true, y_pred)
assert loss.shape == (2,)
y_true = ops.clip(y_true, 1e-7, 1)
y_pred = ops.clip(y_pred, 1e-7, 1)
assert np.array_equal(
    loss, np.sum(y_true * np.log(y_true / y_pred), axis=-1))