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tf.keras.losses.CategoricalCrossentropy

Computes the crossentropy loss between the labels and predictions.

Inherits From: Loss

Used in the notebooks

Used in the guide Used in the tutorials

Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided in a one_hot representation. If you want to provide labels as integers, please use SparseCategoricalCrossentropy loss. There should be # classes floating point values per feature.

In the snippet below, there is # classes floating pointing values per example. The shape of both y_pred and y_true are [batch_size, num_classes].

Standalone usage:

y_true = [[0, 1, 0], [0, 0, 1]]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
# Using 'auto'/'sum_over_batch_size' reduction type.
cce = tf.keras.losses.CategoricalCrossentropy()
cce(y_true, y_pred).numpy()
1.177
# Calling with 'sample_weight'.
cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()
0.814
# Using 'sum' reduction type.
cce = tf.keras.losses.CategoricalCrossentropy(
    reduction=tf.keras.losses.Reduction.SUM)
cce(y_true, y_pred).numpy()
2.354
# Using 'none' reduction type.
cce = tf.keras.losses.CategoricalCrossentropy(
    reduction=tf.keras.losses.Reduction.NONE)
cce(y_true, y_pred).numpy()
array([0.0513, 2.303], dtype=float32)

Usage with the compile() API:

model.compile(optimizer='sgd', loss=tf.keras.losses.CategoricalCrossentropy())

from_logits Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.
label_smoothing Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. For example, if 0.1, use 0.1 / num_classes for non-target labels and 0.9 + 0.1 / num_classes for target labels.
axis The axis along which to compute crossentropy (the features axis). Defaults to -1.
reduction Type of tf.keras.losses.Reduction to apply to loss. Default value is AUTO. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used with tf.distribute.Strategy, outside of built-in training loops such as tf.keras compile and fit, using AUTO or SUM_OVER_BATCH_SIZE will raise an error. Please see this custom training tutorial for more details.
name Optional name for the instance. Defaults to 'categorical_crossentropy'.

Methods

from_config

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