MDNet中layers层的输出

最新推荐文章于 2024-04-25 10:57:19 发布

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最新推荐文章于 2024-04-25 10:57:19 发布

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本文详细解析了一个深度学习网络的结构，包括多个卷积层、全连接层等，并介绍了每层的功能与参数设置，如卷积核大小、步长等。通过此结构，读者可以了解到深度学习模型的基本构建方式。

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layers = nn.Sequential(OrderedDict([
                ('conv1', nn.Sequential(nn.Conv2d(3, 96, kernel_size=7, stride=2),
                                        nn.ReLU(inplace=True),#激活函数，同时inplace设置为True，原值将会被覆盖。
                                        nn.LocalResponseNorm(2),#归一化
                                        nn.MaxPool2d(kernel_size=3, stride=2))),
                ('conv2', nn.Sequential(nn.Conv2d(96, 256, kernel_size=5, stride=2),
                                        nn.ReLU(inplace=True),
                                        nn.LocalResponseNorm(2),
                                        nn.MaxPool2d(kernel_size=3, stride=2))),
                ('conv3', nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=1),
                                        nn.ReLU(inplace=True))),
                ('fc4',   nn.Sequential(nn.Linear(512 * 3 * 3, 512),
                                        nn.ReLU(inplace=True))),
                ('fc5',   nn.Sequential(nn.Dropout(0.5),
                                        nn.Linear(512, 512),
                                        nn.ReLU(inplace=True)))]))
for l in layers:
    print(l)

输出结果为：

Sequential(
  (0): Conv2d(3, 96, kernel_size=(7, 7), stride=(2, 2))
  (1): ReLU(inplace=True)
  (2): LocalResponseNorm(2, alpha=0.0001, beta=0.75, k=1.0)
  (3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
Sequential(
  (0): Conv2d(96, 256, kernel_size=(5, 5), stride=(2, 2))
  (1): ReLU(inplace=True)
  (2): LocalResponseNorm(2, alpha=0.0001, beta=0.75, k=1.0)
  (3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
Sequential(
  (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1))
  (1): ReLU(inplace=True)
)
Sequential(
  (0): Linear(in_features=4608, out_features=512, bias=True)
  (1): ReLU(inplace=True)
)
Sequential(
  (0): Dropout(p=0.5, inplace=False)
  (1): Linear(in_features=512, out_features=512, bias=True)
  (2): ReLU(inplace=True)
)