init.py 文件源码

def xavier_normal(tensor, gain=1):
    """Fills the input Tensor or Variable with values according to the method
    described in "Understanding the difficulty of training deep feedforward
    neural networks" - Glorot, X. & Bengio, Y. (2010), using a normal
    distribution. The resulting tensor will have values sampled from
    :math:`N(0, std)` where
    :math:`std = gain \\times \sqrt{2 / (fan\_in + fan\_out)}`.
    Also known as Glorot initialisation.

    Args:
        tensor: an n-dimensional torch.Tensor or autograd.Variable
        gain: an optional scaling factor

    Examples:
        >>> w = torch.Tensor(3, 5)
        >>> nn.init.xavier_normal(w)
    """
    if isinstance(tensor, Variable):
        xavier_normal(tensor.data, gain=gain)
        return tensor

    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
    std = gain * math.sqrt(2.0 / (fan_in + fan_out))
    return tensor.normal_(0, std)