kaiming_normal_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu')参数: tensor-一个n维torch.Tensor a-此层之后使用的整流器的负斜率(仅与 'leaky_relu' 一起使用) mode-'fan_in'(默认)或 'fan_out' 。选择'fan_in' 会保留前向传递中权重的方差大小。选择'fan_out' 会保留反向传播中的...
mode– either'fan_in'(default) or'fan_out'. Choosing'fan_in'preserves the magnitude of the variance of the weights in the forward pass. Choosing'fan_out'preserves the magnitudes in the backwards pass. nonlinearity– the non-linear function (nn.functional name), recommended to use only with'...
mode– either'fan_in'(default) or'fan_out'. Choosing'fan_in'preserves the magnitude of the variance of the weights in the forward pass. Choosing'fan_out'preserves the magnitudes in the backwards pass. nonlinearity– the non-linear function (nn.functional name), recommended to use only with'...
weight.data, a=0, mode='fan_in') elif classname.find('Linear') != -1: init.kaiming_normal(m.weight.data, a=0, mode='fan_out') init.constant(m.bias.data, 0.0) elif classname.find('BatchNorm1d') != -1: init.normal(m.weight.data, 1.0, 0.02) if hasattr(m.bias, 'data'):...
make sure that you have posted enough message to demo your request. You may also check out the...
weight.data, a=0, mode='fan_in') # For old pytorch, you may use kaiming_normal. elif classname.find('Linear') != -1: init.kaiming_normal_(m.weight.data, a=0, mode='fan_out') init.constant_(m.bias.data, 0.0) elif classname.find('BatchNorm1d') != -1: init.normal_(m....