tf.keras.losses.logcosh | TensorFlow v2.16.1 (original) (raw)
tf.keras.losses.logcosh
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Logarithm of the hyperbolic cosine of the prediction error.
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Main aliases
tf.keras.losses.logcosh(
y_true, y_pred
)
Formula:
loss = mean(log(cosh(y_pred - y_true)), axis=-1)
Note that log(cosh(x)) is approximately equal to (x ** 2) / 2 for smallx 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.
Example:
y_true = [[0., 1.], [0., 0.]]
y_pred = [[1., 1.], [0., 0.]]
loss = keras.losses.log_cosh(y_true, y_pred)
0.108
| Args | |
|---|---|
| y_true | Ground truth values with shape = [batch_size, d0, .. dN]. |
| y_pred | The predicted values with shape = [batch_size, d0, .. dN]. |
| Returns |
|---|
| Logcosh error values with shape = [batch_size, d0, .. dN-1]. |
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Last updated 2024-06-07 UTC.