tf.keras.losses.SparseCategoricalCrossentropy

Computes the crossentropy loss between the labels and predictions.

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 as integers. If you want to provide labels using one-hot representation, please use CategoricalCrossentropy loss. 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].

Standalone usage:

y_true = [1, 2]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
# Using 'auto'/'sum_over_batch_size' reduction type.
scce = tf.keras.losses.SparseCategoricalCrossentropy()
scce(y_true, y_pred).numpy()
1.177
# Calling with 'sample_weight'.
scce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()
0.814
# Using 'sum' reduction type.
scce = tf.keras.losses.SparseCategoricalCrossentropy(
    reduction=tf.keras.losses.Reduction.SUM)
scce(y_true, y_pred).numpy()
2.354
# Using 'none' reduction type.
scce = tf.keras.losses.SparseCategoricalCrossentropy(
    reduction=tf.keras.losses.Reduction.NONE)
scce(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.SparseCategoricalCrossentropy())

from_logits Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution. **Note - Using from_logits=True may be more numerically stable.
reduction (Optional) 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 op. Defaults to 'sparse_categorical_crossentropy'.

Methods

from_config

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Instantiates a Loss from its config (output of get_config()).

Args
config Output of get_config().

Returns
A Loss instance.

get_config

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Returns the config dictionary for a Loss instance.

__call__

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