暹罗网络输出

时间：2016-11-22 14:14:34

标签： machine-learning computer-vision neural-network deep-learning caffe

我试图在caffe中实现一个暹罗网络，它由两个不共享权重的图像网组成。所以我基本上要做的就是给每个网络一个图像，最后试着找出它们之间的相似距离，下面是我的原型。所以我的主要问题是我应该如何设置我的＆＃34; num_output＆＃34;太？我的训练只有2个课程，0个不同，他们不相似，1个是相似的。

name: "Siamese_ImageNet"
layers {
  name: "data"
  type: IMAGE_DATA
  top: "data"
  top: "label"
  image_data_param {
    source: "train1.txt"
    batch_size: 32
    new_height: 256
    new_width: 256
  }
  include: { phase: TRAIN }
}
layers {
  name: "data"
  type: IMAGE_DATA
  top: "data"
  top: "label"
  image_data_param {
    source: "test1.txt"
    batch_size: 32
    new_height: 256
    new_width: 256
  }
  include: { phase: TEST }
}

layers {
  name: "data_p"
  type: IMAGE_DATA
  top: "data_p"
  top: "label_p"
  image_data_param {
    source: "train2.txt"
    batch_size: 32
    new_height: 256
    new_width: 256
  }
  include: { phase: TRAIN }
}
layers {
  name: "data_p"
  type: IMAGE_DATA
  top: "data_p"
  top: "label_p"
  image_data_param {
    source: "test2.txt"
    batch_size: 32
    new_height: 256
    new_width: 256
  }
  include: { phase: TEST }
}


layers {
  name: "conv1"
  type: CONVOLUTION
  bottom: "data"
  top: "conv1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu1"
  type: RELU
  bottom: "conv1"
  top: "conv1"
}
layers {
  name: "pool1"
  type: POOLING
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "norm1"
  type: LRN
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layers {
  name: "conv2"
  type: CONVOLUTION
  bottom: "norm1"
  top: "conv2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layers {
  name: "relu2"
  type: RELU
  bottom: "conv2"
  top: "conv2"
}
layers {
  name: "pool2"
  type: POOLING
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "norm2"
  type: LRN
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layers {
  name: "conv3"
  type: CONVOLUTION
  bottom: "norm2"
  top: "conv3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu3"
  type: RELU
  bottom: "conv3"
  top: "conv3"
}
layers {
  name: "conv4"
  type: CONVOLUTION
  bottom: "conv3"
  top: "conv4"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layers {
  name: "relu4"
  type: RELU
  bottom: "conv4"
  top: "conv4"
}
layers {
  name: "conv5"
  type: CONVOLUTION
  bottom: "conv4"
  top: "conv5"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layers {
  name: "relu5"
  type: RELU
  bottom: "conv5"
  top: "conv5"
}
layers {
  name: "pool5"
  type: POOLING
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "fc6"
  type: INNER_PRODUCT
  bottom: "pool5"
  top: "fc6"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layers {
  name: "relu6"
  type: RELU
  bottom: "fc6"
  top: "fc6"
}
layers {
  name: "drop6"
  type: DROPOUT
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  name: "fc7"
  type: INNER_PRODUCT
  bottom: "fc6"
  top: "fc7"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layers {
  name: "relu7"
  type: RELU
  bottom: "fc7"
  top: "fc7"
}
layers {
  name: "drop7"
  type: DROPOUT
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}

layers {
  name: "conv1_p"
  type: CONVOLUTION
  bottom: "data_p"
  top: "conv1_p"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu1_p"
  type: RELU
  bottom: "conv1_p"
  top: "conv1_p"
}
layers {
  name: "pool1_p"
  type: POOLING
  bottom: "conv1_p"
  top: "pool1_p"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "norm1_p"
  type: LRN
  bottom: "pool1_p"
  top: "norm1_p"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layers {
  name: "conv2_p"
  type: CONVOLUTION
  bottom: "norm1_p"
  top: "conv2_p"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layers {
  name: "relu2_p"
  type: RELU
  bottom: "conv2_p"
  top: "conv2_p"
}
layers {
  name: "pool2_p"
  type: POOLING
  bottom: "conv2_p"
  top: "pool2_p"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "norm2_p"
  type: LRN
  bottom: "pool2_p"
  top: "norm2_p"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layers {
  name: "conv3_p"
  type: CONVOLUTION
  bottom: "norm2_p"
  top: "conv3_p"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu3_p"
  type: RELU
  bottom: "conv3_p"
  top: "conv3_p"
}
layers {
  name: "conv4_p"
  type: CONVOLUTION
  bottom: "conv3_p"
  top: "conv4_p"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layers {
  name: "relu4_p"
  type: RELU
  bottom: "conv4_p"
  top: "conv4_p"
}
layers {
  name: "conv5_p"
  type: CONVOLUTION
  bottom: "conv4_p"
  top: "conv5_p"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layers {
  name: "relu5_p"
  type: RELU
  bottom: "conv5_p"
  top: "conv5_p"
}
layers {
  name: "pool5_p"
  type: POOLING
  bottom: "conv5_p"
  top: "pool5_p"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layers {
  name: "fc6_p"
  type: INNER_PRODUCT
  bottom: "pool5_p"
  top: "fc6_p"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layers {
  name: "relu6_p"
  type: RELU
  bottom: "fc6_p"
  top: "fc6_p"
}
layers {
  name: "drop6_p"
  type: DROPOUT
  bottom: "fc6_p"
  top: "fc6_p"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  name: "fc7_p"
  type: INNER_PRODUCT
  bottom: "fc6_p"
  top: "fc7_p"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layers {
  name: "relu7_p"
  type: RELU
  bottom: "fc7_p"
  top: "fc7_p"
}
layers {
  name: "drop7_p"
  type: DROPOUT
  bottom: "fc7_p"
  top: "fc7_p"
  dropout_param {
    dropout_ratio: 0.5
  }
}

layers {
    name: "loss"
    type: CONTRASTIVE_LOSS
    contrastive_loss_param {
        margin: 1.0
    }
    bottom: "fc7"
    bottom: "fc7_p"
    bottom: "label"
    top: "loss"
}

我的培训档案结构： 0是不相似的，1是相似的

 train1.txt:
 /aer/img1_1.jpg 0
 /aer/img1_2.jpg 1
 /aer/img1_3.jpg 1

 train2.txt:
 /tpd/img2_1.jpg 0
 /tpd/img2_2.jpg 1
 /tpd/img2_3.jpg 1

3 个答案:

答案 0 :(得分：7)

  

我应该如何设置我的＆＃34; num_output＆＃34;？

在了解您应该设置num_output的程度之前，请先解释一下它的含义。实际上，您可以将Simense网络的两侧data -> fc7，data_p -> fc7_p视为2个特征提取器。每个都从相应数据层中的图像中提取特征，例如fc7和fc7_p。因此num_output定义了提取的特征向量的维度。

在训练期间，ContrastiveLoss图层总是尝试最小化2个提取的特征向量＆＃39;当矢量表示的图像的距离类似（label == 1）并且在不熟悉时（label == 0）最大化距离。即，特征向量的距离越小，图像越相似。

那么特征向量的最佳维度是什么才能最好地包含指示相似性的信息？或者你应该设置num_output？可能没有确切的值，它取决于特征提取器的编码质量（您可以将该特征视为图像的代码）以及识别图像的相似性有多难。所以基本上如果网络（特征提取器）很深并且不太难以识别相似性，你可以选择相对较小的num_output例如200，因为该特征可以被更大的网络编码得更好并且更多歧视性。如果不是，您可以尝试更大的值，例如500,1000或尝试更复杂的网络。

如果您想尝试使用MultinomialLogisticLoss而不是ContrastiveLoss图层，则应首先使用类似{{{}的图层将2个要素向量fc7，fc7_p融合为1 1}}然后将其输入CONCAT图层，如下所示：

SOFTMAX_LOSS

更新

  

为了比较相似性并将其用于部署，Constrastive Loss或SoftMax Loss，实施哪种方法最好？

Softmax Loss简单易用。但它只能给你二进制预测，即相似或不相似。它给出的2类（相似的，不相似的）的概率分布通常太硬（不均匀），例如， ...#original layers layers { name: "concat" type: CONCAT bottom: "fc7" bottom: "fc7_p" top: "fc_concat" # concatenate fc7 and fc7_p along channel axis } layer { name: "fc_cls" type: INNER_PRODUCT bottom: "fc_concat" top: "fc_cls" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 2 # a binary classification problem in this case weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "accuracy" type: ACCURACY bottom: "fc_cls" bottom: "label" top: "accuracy" include { phase: TEST } } layer { name: "loss" type: SOFTMAX_LOSS bottom: "fc_cls" bottom: "label" top: "loss" } ，[0.9*, 0.0*]，....在许多情况下，它不会很好地反映真实的输入相似度。

使用Constrastive Loss时，您可以获得图像的判别特征向量。并且您可以使用向量来计算相似概率，正如CVPR 2005论文Learning a Similarity Metric Discriminatively, with Application to Face Verification在4.1节中所做的那样。（关键点是使用从属于图像的图像生成的特征向量计算多元法向密度。同一主题）。您还可以使用阈值来控制模型the false positive rate and the false negative rate以获得ROC curve以更好地评估模型。

顺便说一下，要挖掘更多CNN架构以预测相似性，您可以参考CVPR 2015论文Learning to Compare Image Patches via Convolutional Neural Networks。

答案 1 :(得分：1)

只是为了纠正Dale的优秀answer以上Caffe的超敏感语法，对于像我一样卡住的新手，这里有一些修正（层到层，有些引用） ，加上删除评论和有效大写）

layer {
  name: "concat"
  type: "Concat"
  bottom: "fc7"
  bottom: "fc7_p"  
  top: "fc_concat"
}
layer {
  name: "fc_cls"
  type: "InnerProduct"
  bottom: "fc_concat"
  top: "fc_cls"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "fc_cls"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc_cls"
  bottom: "label"
  top: "loss"
}

答案 2 :(得分：0)

我相信.declareNamespace( )定义了提取的特征向量的维度，然后提取的特征可用于确定num_output距离。如果L2距离大于 1 ，那么它是一个不同的类，如果它接近 0 ，则图像类似。休息戴尔的答案是完美的。

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