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tf.keras.losses.MSLE

Computes the mean squared logarithmic error between `y_true` and `y_pred`.

`loss = mean(square(log(y_true + 1) - log(y_pred + 1)), axis=-1)`

Standalone usage:

````y_true = np.random.randint(0, 2, size=(2, 3))`
`y_pred = np.random.random(size=(2, 3))`
`loss = tf.keras.losses.mean_squared_logarithmic_error(y_true, y_pred)`
`assert loss.shape == (2,)`
`y_true = np.maximum(y_true, 1e-7)`
`y_pred = np.maximum(y_pred, 1e-7)`
`assert np.allclose(`
`    loss.numpy(),`
`    np.mean(`
`        np.square(np.log(y_true + 1.) - np.log(y_pred + 1.)), axis=-1))`
```

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

Mean squared logarithmic 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" }]