tf.keras.losses.sparse_categorical_crossentropy

TensorFlow 1 version View source on GitHub

Computes the sparse categorical crossentropy loss.

tf.keras.losses.sparse_categorical_crossentropy(
    y_true, y_pred, from_logits=False, axis=-1
)

Used in the notebooks

Used in the tutorials

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) 

Args:

  • 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: (Optional) Defaults to -1. The dimension along which the entropy is computed.

Returns:

Sparse categorical crossentropy loss value.