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
)

Arguments

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.

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.

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].

See also