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Calculates how often predictions matches integer labels.
tf.keras.metrics.sparse_categorical_accuracy( y_true, y_pred )
y_true = [2, 1] y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]] m = tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred) assert m.shape == (2,) m.numpy() array([0., 1.], dtype=float32)
You can provide logits of classes as y_pred
, since argmax of logits and probabilities are same.
Args | |
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y_true | Integer ground truth values. |
y_pred | The prediction values. |
Returns | |
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Sparse categorical accuracy values. |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/metrics/sparse_categorical_accuracy