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tf.keras.losses.categorical_crossentropy

Computes the categorical crossentropy loss.

Standalone usage:

y_true = [[0, 1, 0], [0, 0, 1]]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred)
assert loss.shape == (2,)
loss.numpy()
array([0.0513, 2.303], dtype=float32)

y_true Tensor of one-hot true targets.
y_pred Tensor of predicted targets.
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]. If > 0 then smooth the labels. For example, if 0.1, use 0.1 / num_classes for non-target labels and 0.9 + 0.1 / num_classes for target labels.

Categorical crossentropy loss value.