Calculates how often predictions match 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.
 |