Computes how often targets are in the top K predictions.
tf.keras.metrics.top_k_categorical_accuracy(
y_true, y_pred, k=5
)
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.top_k_categorical_accuracy(y_true, y_pred, k=3)
assert m.shape == (2,)
m.numpy()
array([1., 1.], dtype=float32)
Args |
|---|
y_true
|
The ground truth values.
|
y_pred
|
The prediction values.
|
k
|
(Optional) Number of top elements to look at for computing accuracy.
Defaults to 5.
|
Returns |
|---|
|
Top K categorical accuracy value.
|
View source on GitHub