tf.keras.losses.sparse_categorical_crossentropy
Computes the sparse categorical crossentropy loss.
tf.keras.losses.sparse_categorical_crossentropy(
y_true, y_pred, from_logits=False, axis=-1
)
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)
| 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. |
