Computes the mean squared logarithmic error between y_true & y_pred.
tf.keras.losses.MSLE(
y_true, y_pred
)
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)
View source on GitHub