Computes the crossentropy metric between the labels and predictions
metric_categorical_crossentropy(
y_true,
y_pred,
from_logits = FALSE,
label_smoothing = 0,
axis = -1L,
...,
name = "categorical_crossentropy",
dtype = NULL
)If y_true and y_pred are missing, a (subclassed) Metric
instance is returned. The Metric object can be passed directly to
compile(metrics = ) or used as a standalone object. See ?Metric for
example usage.
Alternatively, if called with y_true and y_pred arguments, then the
computed case-wise values for the mini-batch are returned directly.
Tensor of true targets.
Tensor of predicted targets.
(Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution.
(Optional) Float in [0, 1]. When > 0, label values are
smoothed, meaning the confidence on label values are relaxed. e.g.
label_smoothing=0.2 means that we will use a value of 0.1 for label
0 and 0.9 for label 1"
(Optional) (1-based) Defaults to -1. The dimension along which the metric is computed.
Passed on to the underlying metric. Used for forwards and backwards compatibility.
(Optional) string name of the metric instance.
(Optional) data type of the metric result.
This is the crossentropy metric class to be used when there are multiple
label classes (2 or more). Here we assume that labels are given as a one_hot
representation. eg., When labels values are c(2, 0, 1):
y_true = rbind(c(0, 0, 1),
c(1, 0, 0),
c(0, 1, 0))`
Other metrics:
custom_metric(),
metric_accuracy(),
metric_auc(),
metric_binary_accuracy(),
metric_binary_crossentropy(),
metric_categorical_accuracy(),
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_sum(),
metric_top_k_categorical_accuracy(),
metric_true_negatives(),
metric_true_positives()