tf.keras.metrics.sparse_categorical_accuracy
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Calculates how often predictions match integer labels.
tf.keras.metrics.sparse_categorical_accuracy(
y_true, y_pred
)
Standalone usage:
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 |
|---|
y_true
|
Integer ground truth values.
|
y_pred
|
The prediction values.
|
Returns |
|---|
|
Sparse categorical accuracy values.
|
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Last updated 2023-10-06 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[],[]]
View source on GitHub