tf.keras.metrics.mean_squared_logarithmic_error

Computes the mean squared logarithmic error between y_true and y_pred.

Main aliases

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

Compat aliases for migration

See Migration guide for more details.

`tf.compat.v1.keras.losses.MSLE`, `tf.compat.v1.keras.losses.mean_squared_logarithmic_error`, `tf.compat.v1.keras.losses.msle`, `tf.compat.v1.keras.metrics.MSLE`, `tf.compat.v1.keras.metrics.mean_squared_logarithmic_error`, `tf.compat.v1.keras.metrics.msle`

 
tf.keras.metrics.mean_squared_logarithmic_error(
    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))

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

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