torchvision/models/vgg.py
def vgg11_bn(pretrained=False, **kwargs):
    """VGG 11-layer model (configuration "A") with batch normalization

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg11_bn']))
    return model
def vgg11_bn(pretrained=False, **kwargs):
    """VGG 11-layer model (configuration "A") with batch normalization

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['vgg11_bn']))
    return model
def vgg11_bn ( pretrained = False , * * kwargs ) : if pretrained : kwargs [ 'init_weights' ] = False model = VGG ( make_layers ( cfg [ 'A' ] , batch_norm = True ) , * * kwargs ) if pretrained : model . load_state_dict ( model_zoo . load_url ( model_urls [ 'vgg11_bn' ] ) ) return model
VGG 11-layer model (configuration "A") with batch normalization

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
VGG 11 - layer model ( configuration A ) with batch normalization