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multivariate regression example in Caffe(Deeplearning.ir)

Coderx Dec 16th, 2016 41 Never
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  1. #Multivariate Regression in Caffe
  2. #DeepLearning.ir
  3. #https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/gaze-based-human-computer-#interaction/appearance-based-gaze-estimation-in-the-wild-mpiigaze/
  4. #https://www.mpi-inf.mpg.de/fileadmin/inf/d2/xucong/MPIIGaze/train_test.prototxt
  5.  
  6. name: "MPIIGaze"
  7. layers {
  8.   name: "MPII_train"
  9.   type: HDF5_DATA
  10.   top: "data"
  11.   top: "label"
  12.   hdf5_data_param {
  13.     source: "../train_list.txt"
  14.     batch_size: 1000
  15.   }
  16.   include: { phase: TRAIN }
  17. }
  18.  
  19.  
  20. layers {
  21.   name: "MPII_test"
  22.   type: HDF5_DATA
  23.   top: "data"
  24.   top: "label"
  25.   hdf5_data_param {
  26.     source: "../test_list.txt"
  27.     batch_size: 1000
  28.   }
  29.   include: { phase: TEST }
  30. }
  31.  
  32. layers {
  33.   name: "cutLabel"
  34.   type: SLICE
  35.   bottom: "label"
  36.   top: "gaze"
  37.   top: "headpose"
  38.   slice_param {
  39.     slice_dim: 1
  40.     slice_point: 2
  41.   }
  42. }
  43.  
  44. layers {
  45.   name: "conv1"
  46.   type: CONVOLUTION
  47.   bottom: "data"
  48.   top: "conv1"
  49.   blobs_lr: 1
  50.   blobs_lr: 2
  51.   convolution_param {
  52.     num_output: 20
  53.     kernel_size: 5
  54.     stride: 1
  55.     weight_filler {
  56.       type: "gaussian"
  57.       std: 0.1
  58.     }
  59.     bias_filler {
  60.       type: "constant"
  61.     }
  62.   }
  63. }
  64. layers {
  65.   name: "pool1"
  66.   type: POOLING
  67.   bottom: "conv1"
  68.   top: "pool1"
  69.   pooling_param {
  70.     pool: MAX
  71.     kernel_size: 2
  72.     stride: 2
  73.   }
  74. }
  75.  
  76. layers {
  77.   name: "conv2"
  78.   type: CONVOLUTION
  79.   bottom: "pool1"
  80.   top: "conv2"
  81.   blobs_lr: 1
  82.   blobs_lr: 2
  83.   convolution_param {
  84.     num_output: 50
  85.     kernel_size: 5
  86.     stride: 1
  87.     weight_filler {
  88.       type: "gaussian"
  89.       std: 0.01
  90.     }
  91.     bias_filler {
  92.       type: "constant"
  93.     }
  94.   }
  95. }
  96.  
  97. layers {
  98.   name: "pool2"
  99.   type: POOLING
  100.   bottom: "conv2"
  101.   top: "pool2"
  102.   pooling_param {
  103.     pool: MAX
  104.     kernel_size: 2
  105.     stride: 2
  106.   }
  107. }
  108. layers {
  109.   name: "ip1"
  110.   type: INNER_PRODUCT
  111.   bottom: "pool2"
  112.   top: "ip1"
  113.   blobs_lr: 1
  114.   blobs_lr: 2
  115.   inner_product_param {
  116.     num_output: 500
  117.     weight_filler {
  118.       type: "xavier"
  119.     }
  120.     bias_filler {
  121.       type: "constant"
  122.     }
  123.   }
  124. }
  125.  
  126. layers {
  127.   name: "relu1"
  128.   type: RELU
  129.   bottom: "ip1"
  130.   top: "ip1"
  131. }
  132.  
  133. layers {
  134.   name: "concat_headpose_eyeappearance"
  135.   type: CONCAT
  136.   bottom: "ip1"
  137.   bottom: "headpose"
  138.   top: "cat"
  139. }
  140.  
  141.  
  142. layers {
  143.   name: "ip2"
  144.   type: INNER_PRODUCT
  145.   bottom: "cat"
  146.   top: "ip2"
  147.   blobs_lr: 1
  148.   blobs_lr: 2
  149.   inner_product_param {
  150.     num_output: 2
  151.     weight_filler {
  152.       type: "xavier"
  153.     }
  154.     bias_filler {
  155.       type: "constant"
  156.     }
  157.   }
  158. }
  159. layers {
  160.   name: "accuracy"
  161.   type: ACCURACY
  162.   bottom: "ip2"
  163.   bottom: "gaze"
  164.   top: "accuracy"
  165.   include: { phase: TEST }
  166. }
  167. layers {
  168.   name: "loss"
  169.   type: EUCLIDEAN_LOSS
  170.   bottom: "ip2"
  171.   bottom: "gaze"
  172.   top: "loss"
  173. }
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