Hi Sir,

@bahadir

@nramon

@eruzanski

@javier

@marcalph

@elece

what is the exact math formula for xavier initlization ? and what value should we suppose tune the hyperparameter here in this formula ?

Hi Sir,

@bahadir

@nramon

@eruzanski

@javier

@marcalph

@elece

what is the exact math formula for xavier initlization ? and what value should we suppose tune the hyperparameter here in this formula ?

Hi, @Anbu.

For Xavier uniform:

```
Draws samples from a uniform distribution within `[-limit, limit]`, where
`limit = sqrt(6 / (fan_in + fan_out))` (`fan_in` is the number of input units
in the weight tensor and `fan_out` is the number of output units).
```

For Xavier normal:

```
Draws samples from a truncated normal distribution centered on 0 with `stddev
= sqrt(2 / (fan_in + fan_out))` where `fan_in` is the number of input units in
the weight tensor and `fan_out` is the number of output units in the weight
tensor.
```

There’s nothing else to fine tune, other than selecting the right initializer, of course