Computes the crossentropy metric between the labels and predictions
metric_sparse_categorical_crossentropy(
y_true,
y_pred,
from_logits = FALSE,
axis = -1L,
...,
name = "sparse_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) (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.
Use this crossentropy metric when there are two or more label classes.
We expect labels to be provided as integers. If you want to provide labels
using one-hot representation, please use CategoricalCrossentropy metric.
There should be # classes floating point values per feature for y_pred
and a single floating point value per feature for y_true.
In the snippet below, there is a single floating point value per example for
y_true and # classes floating pointing values per example for y_pred.
The shape of y_true is [batch_size] and the shape of y_pred is
[batch_size, num_classes].
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_top_k_categorical_accuracy(),
metric_specificity_at_sensitivity(),
metric_squared_hinge(),
metric_sum(),
metric_top_k_categorical_accuracy(),
metric_true_negatives(),
metric_true_positives()