tf.keras.losses.categorical_crossentropy

TensorFlow 1 version View source on GitHub

Computes the categorical crossentropy loss.

tf.keras.losses.categorical_crossentropy(
    y_true, y_pred, from_logits=False, label_smoothing=0
)

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.

Returns:

Categorical crossentropy loss value.