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# tf.keras.losses.MAPE

Computes the mean absolute percentage error between `y_true` and `y_pred`.

`loss = 100 * mean(abs((y_true - y_pred) / y_true), axis=-1)`

#### Standalone usage:

````y_true = np.random.random(size=(2, 3))`
`y_true = np.maximum(y_true, 1e-7)  # Prevent division by zero`
`y_pred = np.random.random(size=(2, 3))`
`loss = tf.keras.losses.mean_absolute_percentage_error(y_true, y_pred)`
`assert loss.shape == (2,)`
`assert np.array_equal(`
`    loss.numpy(),`
`    100. * np.mean(np.abs((y_true - y_pred) / y_true), axis=-1))`
```

`y_true` Ground truth values. shape = `[batch_size, d0, .. dN]`.
`y_pred` The predicted values. shape = `[batch_size, d0, .. dN]`.

Mean absolute percentage error values. shape = `[batch_size, d0, .. dN-1]`.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"필요한 정보가 없음" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"너무 복잡함/단계 수가 너무 많음" },{ "type": "thumb-down", "id": "outOfDate", "label":"오래됨" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"기타" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"이해하기 쉬움" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"문제가 해결됨" },{ "type": "thumb-up", "id": "otherUp", "label":"기타" }]