# megengine.module.init¶

>>> import megengine.module as M
>>> m = M.Conv2d(16, 33, 3, stride=2)
>>> M.init.msra_normal_(m.weight, mode="fan_out", nonlinearity="relu")


## Initialization¶

 fill_ Fills the given tensor with value val. zeros_ Fills the given tensor with scalar value 0. ones_ Fills the given tensor with the scalar value 1. uniform_ Fills the given tensor with random value sampled from uniform distribution $$\mathcal{U}(\text{a}, \text{b})$$. normal_ Fills the given tensor with random value sampled from normal distribution $$\mathcal{N}(\text{mean}, \text{std}^2)$$. calculate_gain Returns a recommended gain value (see the table below) for the given nonlinearity function. calculate_fan_in_and_fan_out Calculates fan_in / fan_out value for given weight tensor. calculate_correct_fan Calculates fan_in / fan_out value for given weight tensor, depending on given mode. xavier_uniform_ Fills tensor with random values sampled from $$\mathcal{U}(-a, a)$$ where xavier_normal_ Fills tensor with random values sampled from $$\mathcal{N}(0, \text{std}^2)$$ where msra_uniform_ Fills tensor wilth random values sampled from $$\mathcal{U}(-\text{bound}, \text{bound})$$ where msra_normal_ Fills tensor wilth random values sampled from $$\mathcal{N}(0, \text{std}^2)$$ where