metric_sum: Computes the (weighted) sum of the given values
In keras: R Interface to 'Keras'
metric_sum R Documentation
Computes the (weighted) sum of the given values
Description
Computes the (weighted) sum of the given values
Usage
metric_sum(..., name = NULL, dtype = NULL)
Arguments
...
Passed on to the underlying metric. Used for forwards and backwards compatibility.
name
(Optional) string name of the metric instance.
dtype
(Optional) data type of the metric result.
Details
For example, if values is c(1, 3, 5, 7) then the sum is 16.
If the weights were specified as c(1, 1, 0, 0) then the sum would be 4.
This metric creates one variable, total, that is used to compute the sum of
values. This is ultimately returned as sum.
If sample_weight is NULL, weights default to 1. Use sample_weight of 0
to mask values.
Value
A (subclassed) Metric instance that can be passed directly to
compile(metrics = ), or used as a standalone object. See ?Metric for
example usage.
See Also
Other metrics:
custom_metric(),
metric_accuracy(),
metric_auc(),
metric_binary_accuracy(),
metric_binary_crossentropy(),
metric_categorical_accuracy(),
metric_categorical_crossentropy(),
metric_categorical_hinge(),
metric_cosine_similarity(),
metric_false_negatives(),
metric_false_positives(),
metric_hinge(),
metric_kullback_leibler_divergence(),
metric_logcosh_error(),
metric_mean_absolute_error(),
metric_mean_absolute_percentage_error(),
metric_mean_iou(),
metric_mean_relative_error(),
metric_mean_squared_error(),
metric_mean_squared_logarithmic_error(),
metric_mean_tensor(),
metric_mean_wrapper(),
metric_mean(),
metric_poisson(),
metric_precision_at_recall(),
metric_precision(),
metric_recall_at_precision(),
metric_recall(),
metric_root_mean_squared_error(),
metric_sensitivity_at_specificity(),
metric_sparse_categorical_accuracy(),
metric_sparse_categorical_crossentropy(),
metric_sparse_top_k_categorical_accuracy(),
metric_specificity_at_sensitivity(),
metric_squared_hinge(),
metric_top_k_categorical_accuracy(),
metric_true_negatives(),
metric_true_positives()
keras documentation built on Aug. 16, 2023, 1:07 a.m.
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| metric_sum | R Documentation |
Computes the (weighted) sum of the given values
Description
Computes the (weighted) sum of the given values
Usage
metric_sum(..., name = NULL, dtype = NULL)
Arguments
... |
Passed on to the underlying metric. Used for forwards and backwards compatibility. |
name |
(Optional) string name of the metric instance. |
dtype |
(Optional) data type of the metric result. |
Details
For example, if values is c(1, 3, 5, 7) then the sum is 16.
If the weights were specified as c(1, 1, 0, 0) then the sum would be 4.
This metric creates one variable, total, that is used to compute the sum of
values. This is ultimately returned as sum.
If sample_weight is NULL, weights default to 1. Use sample_weight of 0
to mask values.
Value
A (subclassed) Metric instance that can be passed directly to
compile(metrics = ), or used as a standalone object. See ?Metric for
example usage.
See Also
Other metrics:
custom_metric(),
metric_accuracy(),
metric_auc(),
metric_binary_accuracy(),
metric_binary_crossentropy(),
metric_categorical_accuracy(),
metric_categorical_crossentropy(),
metric_categorical_hinge(),
metric_cosine_similarity(),
metric_false_negatives(),
metric_false_positives(),
metric_hinge(),
metric_kullback_leibler_divergence(),
metric_logcosh_error(),
metric_mean_absolute_error(),
metric_mean_absolute_percentage_error(),
metric_mean_iou(),
metric_mean_relative_error(),
metric_mean_squared_error(),
metric_mean_squared_logarithmic_error(),
metric_mean_tensor(),
metric_mean_wrapper(),
metric_mean(),
metric_poisson(),
metric_precision_at_recall(),
metric_precision(),
metric_recall_at_precision(),
metric_recall(),
metric_root_mean_squared_error(),
metric_sensitivity_at_specificity(),
metric_sparse_categorical_accuracy(),
metric_sparse_categorical_crossentropy(),
metric_sparse_top_k_categorical_accuracy(),
metric_specificity_at_sensitivity(),
metric_squared_hinge(),
metric_top_k_categorical_accuracy(),
metric_true_negatives(),
metric_true_positives()
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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