tf.keras.metrics.binary_accuracy

Calculates how often predictions matches binary labels.

Standalone usage:

y_true = [[1], [1], [0], [0]] y_pred = [[1], [1], [0], [0]] m = tf.keras.metrics.binary_accuracy(y_true, y_pred) assert m.shape == (4,) m.numpy() array([1., 1., 1., 1.], dtype=float32)

`y_true` Ground truth values. shape = `[batch_size, d0, .. dN]`.
`y_pred` The predicted values. shape = `[batch_size, d0, .. dN]`.
`threshold` (Optional) Float representing the threshold for deciding whether prediction values are 1 or 0.

Binary accuracy values. shape = `[batch_size, d0, .. dN-1]`

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]