**Mean squared logarithmic error (MSLE)** is a loss function that is used to solve regression problems. MSLE is calculated as the average of the squared differences between the log-transformed actual and predicted values.

The formula to calculate the MSLE:

`n`

- the number of data points.`y`

- the actual value of the data point. Also known as true value.`ŷ`

- the predicted value of the data point. This value is returned by model.

Let's say we have the following sets of numbers:

actual values of `y` | 4 | 0 | 5 | 2 |

predicted values of `ŷ` | 3.5 | 1 | 5 | 3 |

Here is example how MSLE can be calculated using these numbers:

TensorFlow 2 allows to calculate the MSLE. It can be done by using `MeanSquaredLogarithmicError`

class.

```
from tensorflow import keras
yActual = [4, 0, 5, 2]
yPredicted = [3.5, 1, 5, 3]
msleObject = keras.losses.MeanSquaredLogarithmicError()
msleTensor = msleObject(yActual, yPredicted)
msle = msleTensor.numpy()
print(msle)
```

MSLE also can be calculated by using `mean_squared_logarithmic_error`

, `msle`

or `MSLE`

function.

```
msleTensor = keras.losses.mean_squared_logarithmic_error(yActual, yPredicted)
msle = msleTensor.numpy()
msleTensor = keras.losses.msle(yActual, yPredicted)
msle = msleTensor.numpy()
msleTensor = keras.losses.MSLE(yActual, yPredicted)
msle = msleTensor.numpy()
```

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