tf.keras.metrics.sparse_categorical_crossentropy
Computes the sparse categorical crossentropy loss.
tf.keras.metrics.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 |
Defaults to -1. The dimension along which the entropy is computed. |
Returns | |
---|---|
Sparse categorical crossentropy loss value. |
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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/metrics/sparse_categorical_crossentropy