metric_sparse_categorical_crossentropy
Computes the crossentropy metric between the labels and predictions
Description
Computes the crossentropy metric between the labels and predictions
Usage
metric_sparse_categorical_crossentropy(
y_true,
y_pred,
from_logits = FALSE,
axis = -1L,
...,
name = "sparse_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. |
| 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
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].
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_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()