Calculates how often predictions matches one-hot labels.
tf.keras.metrics.categorical_accuracy(
y_true, y_pred
)
Standalone usage:
y_true = [[0, 0, 1], [0, 1, 0]]
y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]
m = tf.keras.metrics.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 |
|---|
y_true
|
One-hot ground truth values.
|
y_pred
|
The prediction values.
|
Returns |
|---|
|
Categorical accuracy values.
|
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TensorFlow 1 version
View source on GitHub