Logarithm of the hyperbolic cosine of the prediction error.
tf.keras.losses.logcosh(
y_true, y_pred
)
log(cosh(x))
is approximately equal to (x ** 2) / 2
for small x
and
to abs(x) - log(2)
for large x
. This means that 'logcosh' works mostly
like the mean squared error, but will not be so strongly affected by the
occasional wildly incorrect prediction.
Usage:
y_true = np.random.random(size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = tf.keras.losses.logcosh(y_true, y_pred)
assert loss.shape == (2,)
x = y_pred - y_true
assert np.allclose(
loss.numpy(),
np.mean(x + np.log(np.exp(-2. * x) + 1.) - math_ops.log(2.), axis=-1),
atol=1e-5)
Args |
y_true
|
Ground truth values. shape = [batch_size, d0, .. dN] .
|
y_pred
|
The predicted values. shape = [batch_size, d0, .. dN] .
|
Returns |
Logcosh error values. shape = [batch_size, d0, .. dN-1] .
|