TensorFlow 1 version | View source on GitHub |
Calculates how often predictions matches 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. |