hoge.py

test.pt
name: "AlexNet"

input: "data"
input_dim: 1 input_dim: 3 input_dim: 64 input_dim: 64

layer {
  name: "conv1" type: "Convolution" bottom: "data" top: "conv1"
  param { lr_mult: 1 decay_mult: 1 }
  param { lr_mult: 2 decay_mult: 0 }
  convolution_param { num_output: 96 kernel_size: 11 stride: 4 }
}
layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1" }
layer {
  name: "norm1" type: "LRN" bottom: "conv1" top: "norm1"
  lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 }
}
layer {
  name: "pool1" type: "Pooling" bottom: "norm1" top: "pool1"
  pooling_param { pool: MAX kernel_size: 3 stride: 2 }
}
layer {
  name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2"
  param { lr_mult: 1 decay_mult: 1 }
  param { lr_mult: 2 decay_mult: 0 }
  convolution_param { num_output: 256 pad: 2 kernel_size: 5 group: 2 }
}
layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2" }
layer {
  name: "norm2" type: "LRN" bottom: "conv2" top: "norm2"
  lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 }
}
layer {
  name: "pool2" type: "Pooling" bottom: "norm2" top: "pool2"
  pooling_param { pool: MAX kernel_size: 3 stride: 2 }
}
layer {
  name: "conv3" type: "Convolution" bottom: "pool2" top: "conv3"
  param { lr_mult: 1 decay_mult: 1 }
  param { lr_mult: 2 decay_mult: 0 }
  convolution_param { num_output: 384 pad: 1 kernel_size: 3 }
}
layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3" }
layer {
  name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4"
  param { lr_mult: 1 decay_mult: 1 }
  param { lr_mult: 2 decay_mult: 0 }
  convolution_param { num_output: 384 pad: 1 kernel_size: 3 group: 2 }
}
layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4" }
layer {
  name: "conv5" type: "Convolution" bottom: "conv4" top: "conv5"
  param { lr_mult: 1 decay_mult: 1 }
  param { lr_mult: 2 decay_mult: 0 }
  convolution_param { num_output: 256 pad: 1 kernel_size: 3 group: 2 }
}
layer { name: "relu5" type: "ReLU" bottom: "conv5" top: "conv5" }
layer {
  name: "pool5" type: "Pooling" bottom: "conv5" top: "pool5"
  pooling_param { pool: MAX kernel_size: 3 stride: 2 }
}
layer {
  name: "fc6-ft" type: "InnerProduct" bottom: "pool5" top: "fc6"
  param { lr_mult: 1 decay_mult: 1 }
  param { lr_mult: 2 decay_mult: 0 }
  inner_product_param { num_output: 4096 }
}
layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" }
layer {
  name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6"
  dropout_param { dropout_ratio: 0.5 }
}
layer {
  name: "fc7-ft" type: "InnerProduct" bottom: "fc6" top: "fc7"
  param { lr_mult: 1 decay_mult: 1 }
  param { lr_mult: 2 decay_mult: 0 }
  inner_product_param { num_output: 4096 }
}
layer { name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7" }
layer {
  name: "drop7" type: "Dropout" bottom: "fc7" top: "fc7"
  dropout_param { dropout_ratio: 0.5 }
}
layer {
  name: "fc8-ft" type: "InnerProduct" bottom: "fc7" top: "fc8"
  param { lr_mult: 1 decay_mult: 1 }
  param { lr_mult: 2 decay_mult: 0 }
  inner_product_param { num_output: 6 }
}
layer { 
  name: "prob" type: "Softmax" bottom: "fc8" top: "prob" 
}