Computes the categorical crossentropy loss.
tf.keras.metrics.categorical_crossentropy(
y_true, y_pred, from_logits=False, label_smoothing=0.0, axis=-1
)
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)
Args |
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.
|
axis
|
Defaults to -1. The dimension along which the entropy is
computed.
|
Returns |
Categorical crossentropy loss value.
|