tf.keras.metrics.LogCoshError

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

Inherits From: Mean, Metric, Layer, Module

View aliases

Main aliases

tf.metrics.LogCoshError

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.LogCoshError

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.

Standalone usage:

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

m.reset_states()
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_states

View source

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

View source

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	Ground truth values. shape = [batch_size, d0, .. dN].
y_pred	The predicted 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.