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wangsheng1991
/
AgeAndGenderEstimation
mirror of https://github.com/wangsheng1991/AgeAndGenderEstimation.git

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deploy_age2.prototxt 2.3 KB
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  1. name: "CaffeNet"
    2. 

  2. input: "data"
    3. 

  3. input_dim: 1
    4. 

  4. input_dim: 3
    5. 

  5. input_dim: 227
    6. 

  6. input_dim: 227
    7. 

  7. layer {
    8. 

  8.   name: "conv1"
    9. 

  9.   type: "Convolution"
    10. 

  10.   bottom: "data"
    11. 

  11.   top: "conv1"
    12. 

  12.   convolution_param {
    13. 

  13.     num_output: 96
    14. 

  14.     kernel_size: 7
    15. 

  15.     stride: 4
    16. 

  16.   }
    17. 

  17. }
    18. 

  18. layer {
    19. 

  19.   name: "relu1"
    20. 

  20.   type: "ReLU"
    21. 

  21.   bottom: "conv1"
    22. 

  22.   top: "conv1"
    23. 

  23. }
    24. 

  24. layer {
    25. 

  25.   name: "pool1"
    26. 

  26.   type: "Pooling"
    27. 

  27.   bottom: "conv1"
    28. 

  28.   top: "pool1"
    29. 

  29.   pooling_param {
    30. 

  30.     pool: MAX
    31. 

  31.     kernel_size: 3
    32. 

  32.     stride: 2
    33. 

  33.   }
    34. 

  34. }
    35. 

  35. layer {
    36. 

  36.   name: "norm1"
    37. 

  37.   type: "LRN"
    38. 

  38.   bottom: "pool1"
    39. 

  39.   top: "norm1"
    40. 

  40.   lrn_param {
    41. 

  41.     local_size: 5
    42. 

  42.     alpha: 0.0001
    43. 

  43.     beta: 0.75
    44. 

  44.   }
    45. 

  45. }
    46. 

  46. layer {
    47. 

  47.   name: "conv2"
    48. 

  48.   type: "Convolution"
    49. 

  49.   bottom: "norm1"
    50. 

  50.   top: "conv2"
    51. 

  51.   convolution_param {
    52. 

  52.     num_output: 256
    53. 

  53.     pad: 2
    54. 

  54.     kernel_size: 5
    55. 

  55.   }
    56. 

  56. }
    57. 

  57. layer {
    58. 

  58.   name: "relu2"
    59. 

  59.   type: "ReLU"
    60. 

  60.   bottom: "conv2"
    61. 

  61.   top: "conv2"
    62. 

  62. }
    63. 

  63. layer {
    64. 

  64.   name: "pool2"
    65. 

  65.   type: "Pooling"
    66. 

  66.   bottom: "conv2"
    67. 

  67.   top: "pool2"
    68. 

  68.   pooling_param {
    69. 

  69.     pool: MAX
    70. 

  70.     kernel_size: 3
    71. 

  71.     stride: 2
    72. 

  72.   }
    73. 

  73. }
    74. 

  74. layer {
    75. 

  75.   name: "norm2"
    76. 

  76.   type: "LRN"
    77. 

  77.   bottom: "pool2"
    78. 

  78.   top: "norm2"
    79. 

  79.   lrn_param {
    80. 

  80.     local_size: 5
    81. 

  81.     alpha: 0.0001
    82. 

  82.     beta: 0.75
    83. 

  83.   }
    84. 

  84. }
    85. 

  85. layer {
    86. 

  86.   name: "conv3"
    87. 

  87.   type: "Convolution"
    88. 

  88.   bottom: "norm2"
    89. 

  89.   top: "conv3"
    90. 

  90.   convolution_param {
    91. 

  91.     num_output: 384
    92. 

  92.     pad: 1
    93. 

  93.     kernel_size: 3
    94. 

  94.   }
    95. 

  95. }
    96. 

  96. layer {
    97. 

  97.   name: "relu3"
    98. 

  98.   type: "ReLU"
    99. 

  99.   bottom: "conv3"
    100. 

  100.   top: "conv3"
    101. 

  101. }
    102. 

  102. layer {
    103. 

  103.   name: "pool5"
    104. 

  104.   type: "Pooling"
    105. 

  105.   bottom: "conv3"
    106. 

  106.   top: "pool5"
    107. 

  107.   pooling_param {
    108. 

  108.     pool: MAX
    109. 

  109.     kernel_size: 3
    110. 

  110.     stride: 2
    111. 

  111.   }
    112. 

  112. }
    113. 

  113. layer {
    114. 

  114.   name: "fc6"
    115. 

  115.   type: "InnerProduct"
    116. 

  116.   bottom: "pool5"
    117. 

  117.   top: "fc6"
    118. 

  118.   inner_product_param {
    119. 

  119.     num_output: 512
    120. 

  120.   }
    121. 

  121. }
    122. 

  122. layer {
    123. 

  123.   name: "relu6"
    124. 

  124.   type: "ReLU"
    125. 

  125.   bottom: "fc6"
    126. 

  126.   top: "fc6"
    127. 

  127. }
    128. 

  128. layer {
    129. 

  129.   name: "drop6"
    130. 

  130.   type: "Dropout"
    131. 

  131.   bottom: "fc6"
    132. 

  132.   top: "fc6"
    133. 

  133.   dropout_param {
    134. 

  134.     dropout_ratio: 0.5
    135. 

  135.   }
    136. 

  136. }
    137. 

  137. layer {
    138. 

  138.   name: "fc7"
    139. 

  139.   type: "InnerProduct"
    140. 

  140.   bottom: "fc6"
    141. 

  141.   top: "fc7"
    142. 

  142.   inner_product_param {
    143. 

  143.     num_output: 512
    144. 

  144.   }
    145. 

  145. }
    146. 

  146. layer {
    147. 

  147.   name: "relu7"
    148. 

  148.   type: "ReLU"
    149. 

  149.   bottom: "fc7"
    150. 

  150.   top: "fc7"
    151. 

  151. }
    152. 

  152. layer {
    153. 

  153.   name: "drop7"
    154. 

  154.   type: "Dropout"
    155. 

  155.   bottom: "fc7"
    156. 

  156.   top: "fc7"
    157. 

  157.   dropout_param {
    158. 

  158.     dropout_ratio: 0.5
    159. 

  159.   }
    160. 

  160. }
    161. 

  161. layer {
    162. 

  162.   name: "fc8"
    163. 

  163.   type: "InnerProduct"
    164. 

  164.   bottom: "fc7"
    165. 

  165.   top: "fc8"
    166. 

  166.   inner_product_param {
    167. 

  167.     num_output: 8
    168. 

  168.   }
    169. 

  169. }
    170. 

  170. layer {
    171. 

  171.   name: "prob"
    172. 

  172.   type: "Softmax"
    173. 

  173.   bottom: "fc8"
    174. 

  174.   top: "prob"
    175. 

  175. }
    176.
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