DenseNet¶

class paddle.vision.models. DenseNet ( layers=121, bn_size=4, dropout=0.0, num_classes=1000, with_pool=True ) [source]

DenseNet model from “Densely Connected Convolutional Networks”.

Parameters

• layers (int, optional) – Layers of DenseNet. Default: 121.

• bn_size (int, optional) – Expansion of growth rate in the middle layer. Default: 4.

• dropout (float, optional) – Dropout rate. Default: \(0.0\).

• num_classes (int, optional) – Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000.

• with_pool (bool, optional) – Use pool before the last fc layer or not. Default: True.

Returns

Layer. An instance of DenseNet model.

Examples

import paddle
from paddle.vision.models import DenseNet

# build model
densenet = DenseNet()

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

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

forward ( input )

forward¶

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments