/TensorFlow 2.4


Computes the crossentropy metric between the labels and predictions.

Inherits From: Mean, Metric, Layer, Module

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].

name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
from_logits (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution.
axis (Optional) Defaults to -1. The dimension along which the metric is computed.

Standalone usage:

# y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]
# logits = log(y_pred)
# softmax = exp(logits) / sum(exp(logits), axis=-1)
# softmax = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]
# xent = -sum(y * log(softmax), 1)
# log(softmax) = [[-2.9957, -0.0513, -16.1181],
#                [-2.3026, -0.2231, -2.3026]]
# y_true * log(softmax) = [[0, -0.0513, 0], [0, 0, -2.3026]]
# xent = [0.0513, 2.3026]
# Reduced xent = (0.0513 + 2.3026) / 2
m = tf.keras.metrics.SparseCategoricalCrossentropy()
m.update_state([1, 2],
               [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
m.update_state([1, 2],
               [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
               sample_weight=tf.constant([0.3, 0.7]))

Usage with compile() API:




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Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.


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Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.


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Accumulates metric statistics.

y_true and y_pred should have the same shape.

y_true Ground truth values. shape = [batch_size, d0, .. dN].
y_pred The predicted values. shape = [batch_size, d0, .. dN].
sample_weight Optional sample_weight acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the metric for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each metric element of y_pred is scaled by the corresponding value of sample_weight. (Note on dN-1: all metric functions reduce by 1 dimension, usually the last axis (-1)).
Update op.

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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.