tf.keras.losses.KLD | TensorFlow v2.16.1 (original) (raw)
tf.keras.losses.KLD
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Computes Kullback-Leibler divergence loss between y_true & y_pred.
View aliases
Main aliases
tf.keras.losses.kld, tf.keras.losses.kullback_leibler_divergence, tf.keras.metrics.KLD, tf.keras.metrics.kld, tf.keras.metrics.kullback_leibler_divergence
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.
| Args | |
|---|---|
| y_true | Tensor of true targets. |
| y_pred | Tensor of predicted targets. |
| Returns |
|---|
| 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))