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 Ecient 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]