# tf.keras.metrics.mean_squared_logarithmic_error

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|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/losses.py#L1807-L1848) |

Computes the mean squared logarithmic error between `y_true` \& `y_pred`.

#### View aliases


**Main aliases**

[`tf.keras.losses.MSLE`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error), [`tf.keras.losses.mean_squared_logarithmic_error`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error), [`tf.keras.losses.msle`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error), [`tf.keras.metrics.MSLE`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error), [`tf.keras.metrics.msle`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error), [`tf.losses.MSLE`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error), [`tf.losses.mean_squared_logarithmic_error`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error), [`tf.losses.msle`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error), [`tf.metrics.MSLE`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error), [`tf.metrics.mean_squared_logarithmic_error`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error), [`tf.metrics.msle`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error)
**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

\`tf.compat.v1.keras.losses.MSLE\`, \`tf.compat.v1.keras.losses.mean_squared_logarithmic_error\`, \`tf.compat.v1.keras.losses.msle\`, \`tf.compat.v1.keras.metrics.MSLE\`, \`tf.compat.v1.keras.metrics.mean_squared_logarithmic_error\`, \`tf.compat.v1.keras.metrics.msle\`

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    tf.keras.metrics.mean_squared_logarithmic_error(
        y_true, 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))

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

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| ## Returns ||
|---|---|
| Mean squared logarithmic error values. shape = `[batch_size, d0, .. dN-1]`. ||

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