description: Computes the mean squared logarithmic error between y_true and y_pred.

tf.keras.losses.MSLE

Computes the mean squared logarithmic error between y_true and y_pred.

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

loss = mean(square(log(y_true + 1) - log(y_pred + 1)), axis=-1)

Standalone usage:

>>> y_true = np.random.randint(0, 2, size=(2, 3))
>>> y_pred = np.random.random(size=(2, 3))
>>> loss = tf.keras.losses.mean_squared_logarithmic_error(y_true, y_pred)
>>> assert loss.shape == (2,)
>>> y_true = np.maximum(y_true, 1e-7)
>>> y_pred = np.maximum(y_pred, 1e-7)
>>> assert np.allclose(
...     loss.numpy(),
...     np.mean(
...         np.square(np.log(y_true + 1.) - np.log(y_pred + 1.)), axis=-1))

Args

y_true Ground truth values. shape = [batch_size, d0, .. dN].
y_pred The predicted values. shape = [batch_size, d0, .. dN].

Returns

Mean squared logarithmic error values. shape = [batch_size, d0, .. dN-1].