caffe-siamese network

最新推荐文章于 2025-03-25 23:40:50 发布

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本文详细介绍了在神经网络训练中参数配置的重要性，包括参数名称设定、权重共享模式的选择、学习率乘数及权重衰减乘数等关键概念。通过具体的示例说明了如何在不同层间进行参数共享，并解释了这些配置如何影响模型的学习效率。

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有参数的layer

param {

name: "xxx"

}

// Specifies training parameters (multipliers on global learning constants,
// and the name and other settings used for weight sharing).
message ParamSpec {
  // The names of the parameter blobs -- useful for sharing parameters among
  // layers, but never required otherwise.  To share a parameter between two
  // layers, give it a (non-empty) name.
  optional string name = 1;

  // Whether to require shared weights to have the same shape, or just the same
  // count -- defaults to STRICT if unspecified.
  optional DimCheckMode share_mode = 2;
  enum DimCheckMode {
    // STRICT (default) requires that num, channels, height, width each match.
    STRICT = 0;
    // PERMISSIVE requires only the count (num*channels*height*width) to match.
    PERMISSIVE = 1;
  }

  // The multiplier on the global learning rate for this parameter.
  optional float lr_mult = 3 [default = 1.0];

  // The multiplier on the global weight decay for this parameter.
  optional float decay_mult = 4 [default = 1.0];
}

layerparameters
  // Specifies training parameters (multipliers on global learning constants,
  // and the name and other settings used for weight sharing).
  repeated ParamSpec param = 6;

------------------------reference---------------------------------------------

0. https://github.com/BVLC/caffe/tree/master/examples/siamese