tf.keras.metrics.LogCoshError

Computes the logarithm of the hyperbolic cosine of the prediction error.

Main aliases

tf.metrics.LogCoshError

Compat aliases for migration

See Migration guide for more details.

tf.keras.metrics.LogCoshError(
    name='logcosh', dtype=None
)

logcosh = log((exp(x) + exp(-x))/2), where x is the error (y_pred - y_true)

Args
`name`	(Optional) string name of the metric instance.
`dtype`	(Optional) data type of the metric result.

m = tf.keras.metrics.LogCoshError()
m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
m.result().numpy()
0.10844523

m.reset_state()
m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
               sample_weight=[1, 0])
m.result().numpy()
0.21689045

Usage with compile() API:

model.compile(optimizer='sgd',
              loss='mse',
              metrics=[tf.keras.metrics.LogCoshError()])

Methods

reset_state()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates metric statistics.

For sparse categorical metrics, the shapes of y_true and y_pred are different.

Args
`y_true`	Ground truth label values. shape = `[batch_size, d0, .. dN-1]` or shape = `[batch_size, d0, .. dN-1, 1]`.
`y_pred`	The predicted probability values. shape = `[batch_size, d0, .. dN]`.
`sample_weight`	Optional `sample_weight` acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the metric for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each metric element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on `dN-1`: all metric functions reduce by 1 dimension, usually the last axis (-1)).

Returns
Update op.