shufflenet_v2_swish¶

paddle.vision.models. shufflenet_v2_swish ( pretrained=False, **kwargs ) [source]

ShuffleNetV2 with swish activation function, as described in “ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design”.

Parameters

pretrained (bool, optional) – Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False.
**kwargs (optional) – Additional keyword arguments. For details, please refer to ShuffleNetV2.

Returns

Layer. An instance of ShuffleNetV2 with swish activation function.

Examples

           >>> import paddle
>>> from paddle.vision.models import shufflenet_v2_swish

>>> # build model
>>> model = shufflenet_v2_swish()

>>> # build model and load imagenet pretrained weight
>>> # model = shufflenet_v2_swish(pretrained=True)

>>> x = paddle.rand([1, 3, 224, 224])
>>> out = model(x)

>>> print(out.shape)
[1, 1000]

          