def _initialize_weights(self):
    for m in self.modules():
        if isinstance(m, torch.nn.Conv2d):
            torch.nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            if (m.bias is not None):
                torch.nn.init.constant_(m.bias, 0)
        elif isinstance(m, torch.nn.BatchNorm2d):
            torch.nn.init.constant_(m.weight, 1)
            torch.nn.init.constant_(m.bias, 0)
        elif isinstance(m, torch.nn.Linear):
            torch.nn.init.normal_(m.weight, 0, 0.01)
            torch.nn.init.constant_(m.bias, 0)
def _initialize_weights(self):
    for m in self.modules():
        if isinstance(m, torch.nn.Conv2d):
            torch.nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            if (m.bias is not None):
                torch.nn.init.constant_(m.bias, 0)
        elif isinstance(m, torch.nn.BatchNorm2d):
            torch.nn.init.constant_(m.weight, 1)
            torch.nn.init.constant_(m.bias, 0)
        elif isinstance(m, torch.nn.Linear):
            torch.nn.init.normal_(m.weight, 0, 0.01)
            torch.nn.init.constant_(m.bias, 0)