tf.keras.metrics.LogCoshError

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Class LogCoshError

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

Aliases:

  • Class tf.compat.v1.keras.metrics.LogCoshError
  • Class tf.compat.v2.keras.metrics.LogCoshError
  • Class tf.compat.v2.metrics.LogCoshError
  • Class tf.metrics.LogCoshError

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

Usage:

m = tf.keras.metrics.LogCoshError()
m.update_state([0., 1., 1.], [1., 0., 1.])
print('Final result: ', m.result().numpy())  # Final result: 0.289

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', metrics=[tf.keras.metrics.LogCoshError()])

__init__

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__init__(
    name='logcosh',
    dtype=None
)

Creates a MeanMetricWrapper instance.

Args:

  • fn: The metric function to wrap, with signature fn(y_true, y_pred, **kwargs).
  • name: (Optional) string name of the metric instance.
  • dtype: (Optional) data type of the metric result.
  • **kwargs: The keyword arguments that are passed on to fn.

Methods

reset_states

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reset_states()

Resets all of the metric state variables.

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

result

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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

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update_state(
    y_true,
    y_pred,
    sample_weight=None
)

Accumulates metric statistics.

y_true and y_pred should have the same shape.

Args:

  • y_true: The ground truth values.
  • y_pred: The predicted values.
  • sample_weight: Optional weighting of each example. Defaults to 1. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true.

Returns:

Update op.

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