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Oct 21st, 2016
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  1. Name: "MNIST Classification"
  2. layer {
  3. name: "data"
  4. type: "Data"
  5. top: "data"
  6. top: "label"
  7. transform_param {
  8. scale: 0.00392156862745
  9. }
  10. data_param {
  11. source: "mnist/mnist_train_lmdb"
  12. batch_size: 64
  13. backend: LMDB
  14. }
  15. }
  16. layer {
  17. name: "conv1"
  18. type: "Convolution"
  19. bottom: "data"
  20. top: "conv1"
  21. convolution_param {
  22. num_output: 20
  23. kernel_size: 5
  24. weight_filler {
  25. type: "xavier"
  26. }
  27. }
  28. }
  29. layer {
  30. name: "pool1"
  31. type: "Pooling"
  32. bottom: "conv1"
  33. top: "pool1"
  34. pooling_param {
  35. pool: MAX
  36. kernel_size: 2
  37. stride: 2
  38. }
  39. }
  40. layer {
  41. name: "conv2"
  42. type: "Convolution"
  43. bottom: "pool1"
  44. top: "conv2"
  45. convolution_param {
  46. num_output: 50
  47. kernel_size: 5
  48. weight_filler {
  49. type: "xavier"
  50. }
  51. }
  52. }
  53. layer {
  54. name: "pool2"
  55. type: "Pooling"
  56. bottom: "conv2"
  57. top: "pool2"
  58. pooling_param {
  59. pool: MAX
  60. kernel_size: 2
  61. stride: 2
  62. }
  63. }
  64. layer {
  65. name: "fc1"
  66. type: "InnerProduct"
  67. bottom: "pool2"
  68. top: "fc1"
  69. inner_product_param {
  70. num_output: 500
  71. weight_filler {
  72. type: "xavier"
  73. }
  74. }
  75. }
  76. layer {
  77. name: "relu1"
  78. type: "ReLU"
  79. bottom: "fc1"
  80. top: "fc1"
  81. }
  82. layer {
  83. name: "score"
  84. type: "InnerProduct"
  85. bottom: "fc1"
  86. top: "score"
  87. inner_product_param {
  88. num_output: 10
  89. weight_filler {
  90. type: "xavier"
  91. }
  92. }
  93. }
  94. layer {
  95. name: "loss"
  96. type: "SoftmaxWithLoss"
  97. bottom: "score"
  98. bottom: "label"
  99. top: "loss"
  100. }
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