tf.keras.losses.logcosh
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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.
Arguments |
|---|
y_true
|
tensor of true targets.
|
y_pred
|
tensor of predicted targets.
|
Returns |
|---|
|
Tensor with one scalar loss entry per sample.
|
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.losses.logcosh\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/keras/losses/logcosh) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/python/keras/losses.py#L918-L940) |\n\nLogarithm of the hyperbolic cosine of the prediction error.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.losses.logcosh`](/api_docs/python/tf/keras/losses/log_cosh)\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.losses.logcosh`](/api_docs/python/tf/keras/losses/log_cosh)\n\n\u003cbr /\u003e\n\n tf.keras.losses.logcosh(\n y_true, y_pred\n )\n\n`log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and\nto `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly\nlike the mean squared error, but will not be so strongly affected by the\noccasional wildly incorrect prediction.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|----------|------------------------------|\n| `y_true` | tensor of true targets. |\n| `y_pred` | tensor of predicted targets. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| Tensor with one scalar loss entry per sample. ||\n\n\u003cbr /\u003e"]]
TensorFlow 1 version
View source on GitHub