tf.keras.losses.MSLE | TensorFlow v2.16.1 (original) (raw)
tf.keras.losses.MSLE
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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)
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Last updated 2024-06-07 UTC.