def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs): """Weight-normalized Conv1d layer optimized for decoding""" m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs) std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) nn.init.normal_(m.weight, mean=0, std=std) nn.init.constant_(m.bias, 0) return nn.utils.weight_norm(m, dim=2)
def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0., **kwargs): """Weight-normalized Conv1d layer optimized for decoding""" m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs) std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) m.weight.data.normal_(mean=0, std=std) m.bias.data.zero_() return m