metric_categorical_crossentropy
Computes the crossentropy metric between the labels and predictions
Description
Computes the crossentropy metric between the labels and predictions
Usage
metric_categorical_crossentropy(
y_true,
y_pred,
from_logits = FALSE,
label_smoothing = 0,
axis = -1L,
...,
name = "categorical_crossentropy",
dtype = NULL
) Arguments
| Arguments | Description |
|---|---|
| y_true | Tensor of true targets. |
| y_pred | Tensor of predicted targets. |
| from_logits | (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution. |
| label_smoothing | (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” |
| axis | (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. |
| name | (Optional) string name of the metric instance. |
| dtype | (Optional) data type of the metric result. |
Details
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))` ```
Value
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.
See Also
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()