tf.keras.metrics.sparse_categorical_crossentropy

Computes the sparse categorical crossentropy loss.

Main aliases

tf.keras.losses.sparse_categorical_crossentropy, tf.losses.sparse_categorical_crossentropy, tf.metrics.sparse_categorical_crossentropy

 
tf.keras.metrics.sparse_categorical_crossentropy(
    y_true, y_pred, from_logits=False, axis=-1, ignore_class=None
)

Standalone usage:

y_true = [1, 2]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred)
assert loss.shape == (2,)
loss.numpy()
array([0.0513, 2.303], dtype=float32)
 
y_true = [[[ 0,  2],
           [-1, -1]],
          [[ 0,  2],
           [-1, -1]]]
y_pred = [[[[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]],
            [[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]],
          [[[1.0, 0.0, 0.0], [0.0, 0.5, 0.5]],
           [[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]]]
loss = tf.keras.losses.sparse_categorical_crossentropy(
  y_true, y_pred, ignore_class=-1)
loss.numpy()
array([[[2.3841855e-07, 2.3841855e-07],
        [0.0000000e+00, 0.0000000e+00]],
       [[2.3841855e-07, 6.9314730e-01],
        [0.0000000e+00, 0.0000000e+00]]], dtype=float32)

y_true Ground truth values.
y_pred The predicted values.
from_logits Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.
axis Defaults to -1. The dimension along which the entropy is computed.
ignore_class Optional integer. The ID of a class to be ignored during loss computation. This is useful, for example, in segmentation problems featuring a "void" class (commonly -1 or 255) in segmentation maps. By default (ignore_class=None), all classes are considered.

Sparse categorical crossentropy loss value.