Function xavier_uniform_

imperative/python/megengine/module/init.py:177–196

r"""Fills tensor with random values sampled from :math:`\mathcal{U}(-a, a)` where .. math:: a = \text{gain} \times \sqrt{\frac{6}{\text{fan_in} + \text{fan_out}}} Also known as Glorot initialization. Detailed information can be retrieved from `Understanding the difficulty

(tensor: Tensor, gain: float = 1.0)

def xavier_uniform_(tensor: Tensor, gain: float = 1.0) -> None:
    r"""Fills tensor with random values sampled from :math:`\mathcal{U}(-a, a)`
    where

    .. math::

        a = \text{gain} \times \sqrt{\frac{6}{\text{fan_in} + \text{fan_out}}}

    Also known as Glorot initialization. Detailed information can be retrieved from
    `Understanding the difficulty of training deep feedforward neural networks` -
    Glorot, X. & Bengio, Y. (2010).

    Args:
        tensor: tensor to be initialized.
        gain: scaling factor for :math:`a`.
    """
    fan_in, fan_out = calculate_fan_in_and_fan_out(tensor)
    std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
    a = math.sqrt(3.0) * std
    uniform_(tensor, -a, a)


def xavier_normal_(tensor: Tensor, gain: float = 1.0) -> None: