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")

Note

良好的初始化策略有助于你的模型在训练时更快地收敛。

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