tf.keras.losses.SparseCategoricalCrossentropy

Computes the crossentropy loss between the labels and predictions.

Inherits From: Loss

tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=False,
    ignore_class=None,
    reduction='sum_over_batch_size',
    axis=-1,
    name='sparse_categorical_crossentropy',
    dtype=None
)

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 num_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].

from_logits Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.
reduction Type of reduction to apply to the loss. In almost all cases this should be "sum_over_batch_size". Supported options are "sum", "sum_over_batch_size", "mean", "mean_with_sample_weight" or None. "sum" sums the loss, "sum_over_batch_size" and "mean" sum the loss and divide by the sample size, and "mean_with_sample_weight" sums the loss and divides by the sum of the sample weights. "none" and None perform no aggregation. Defaults to "sum_over_batch_size".
axis The axis along which to compute crossentropy (the features axis). Defaults to -1.
name Optional name for the loss instance.
dtype The dtype of the loss's computations. Defaults to None, which means using keras.backend.floatx(). keras.backend.floatx() is a "float32" unless set to different value (via keras.backend.set_floatx()). If a keras.DTypePolicy is provided, then the compute_dtype will be utilized.

Examples:

y_true = np.array([1, 2])
y_pred = np.array([[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
# Using 'auto'/'sum_over_batch_size' reduction type.
scce = keras.losses.SparseCategoricalCrossentropy()
scce(y_true, y_pred)
1.177
 
# Calling with 'sample_weight'.
scce(y_true, y_pred, sample_weight=np.array([0.3, 0.7]))
0.814
 
# Using 'sum' reduction type.
scce = keras.losses.SparseCategoricalCrossentropy(
    reduction="sum")
scce(y_true, y_pred)
2.354
 
# Using 'none' reduction type.
scce = keras.losses.SparseCategoricalCrossentropy(
    reduction=None)
scce(y_true, y_pred)
array([0.0513, 2.303], dtype=float32)

Usage with the compile() API:

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

dtype

Methods

call

View source

call(
    y_true, y_pred
)

from_config

View source

@classmethod
from_config(
    config
)

get_config

View source

get_config()

__call__

View source

__call__(
    y_true, y_pred, sample_weight=None
)

Call self as a function.