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paddle.vision.models. googlenet ( pretrained: bool = False, **kwargs: Unpack[_GoogLeNetOptions] ) → GoogLeNet [source]
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GoogLeNet (Inception v1) model architecture from “Going Deeper with Convolutions”.

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 GoogLeNet.

Returns

Layer. An instance of GoogLeNet (Inception v1) model.

Examples

>>> import paddle
>>> from paddle.vision.models import googlenet

>>> # Build model
>>> model = googlenet()

>>> # Build model and load imagenet pretrained weight
>>> # model = googlenet(pretrained=True)

>>> x = paddle.rand([1, 3, 224, 224])
>>> out, out1, out2 = model(x)

>>> print(out.shape, out1.shape, out2.shape)
[1, 1000] [1, 1000] [1, 1000]