Siamese network output
<p>I'm trying to implement a siamese network in caffe in which it is composed of two imagenets that don't share weights. So what I am basically trying to do is give each network an image, and in the end try to find out the distance between them for similarity, below is my prototxt. So my main question is what should I set my "num_output" too? I have only 2 classes for my training, 0 for wither they are not alike, and 1 for if they are similar.</p>

<pre><code>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"
}
</code></pre>

<p>My training file structure:
0 is dissimilar, 1 is similar</p>

<pre><code> 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
</code></pre>