tf.keras.losses.MSLE

Computes the mean squared logarithmic error between y_true & y_pred.

View aliases

Main aliases

tf.keras.losses.msle, tf.keras.metrics.MSLE, tf.keras.metrics.msle

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

Formula:

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

Note that y_pred and y_true cannot be less or equal to 0. Negative values and 0 values will be replaced with keras.backend.epsilon() (default to 1e-7).

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

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

Example:

y_true = np.random.randint(0, 2, size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = keras.losses.mean_squared_logarithmic_error(y_true, y_pred)