588 lines
7.8 KiB
Plaintext
588 lines
7.8 KiB
Plaintext
name: "GOTURN"
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input: "data1"
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input_dim: 1
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input_dim: 3
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input_dim: 227
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input_dim: 227
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input: "data2"
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input_dim: 1
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input_dim: 3
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input_dim: 227
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input_dim: 227
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layer {
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name: "conv11"
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type: "Convolution"
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bottom: "data1"
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top: "conv11"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 96
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kernel_size: 11
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stride: 4
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "relu11"
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type: "ReLU"
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bottom: "conv11"
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top: "conv11"
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}
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layer {
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name: "pool11"
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type: "Pooling"
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bottom: "conv11"
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top: "pool11"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "norm11"
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type: "LRN"
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bottom: "pool11"
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top: "norm11"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layer {
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name: "conv12"
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type: "Convolution"
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bottom: "norm11"
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top: "conv12"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 2
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kernel_size: 5
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 1
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}
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}
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}
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layer {
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name: "relu12"
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type: "ReLU"
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bottom: "conv12"
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top: "conv12"
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}
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layer {
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name: "pool12"
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type: "Pooling"
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bottom: "conv12"
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top: "pool12"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "norm12"
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type: "LRN"
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bottom: "pool12"
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top: "norm12"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layer {
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name: "conv13"
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type: "Convolution"
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bottom: "norm12"
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top: "conv13"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "relu13"
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type: "ReLU"
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bottom: "conv13"
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top: "conv13"
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}
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layer {
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name: "conv14"
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type: "Convolution"
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bottom: "conv13"
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top: "conv14"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 1
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}
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}
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}
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layer {
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name: "relu14"
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type: "ReLU"
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bottom: "conv14"
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top: "conv14"
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}
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layer {
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name: "conv15"
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type: "Convolution"
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bottom: "conv14"
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top: "conv15"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 1
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kernel_size: 3
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 1
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}
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}
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}
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layer {
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name: "relu15"
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type: "ReLU"
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bottom: "conv15"
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top: "conv15"
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}
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layer {
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name: "pool15"
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type: "Pooling"
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bottom: "conv15"
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top: "pool15"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "conv21"
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type: "Convolution"
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bottom: "data2"
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top: "conv21"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 96
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kernel_size: 11
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stride: 4
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "relu21"
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type: "ReLU"
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bottom: "conv21"
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top: "conv21"
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}
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layer {
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name: "pool21"
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type: "Pooling"
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bottom: "conv21"
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top: "pool21"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "norm21"
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type: "LRN"
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bottom: "pool21"
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top: "norm21"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layer {
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name: "conv22"
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type: "Convolution"
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bottom: "norm21"
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top: "conv22"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 2
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kernel_size: 5
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 1
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}
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}
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}
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layer {
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name: "relu22"
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type: "ReLU"
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bottom: "conv22"
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top: "conv22"
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}
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layer {
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name: "pool22"
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type: "Pooling"
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bottom: "conv22"
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top: "pool22"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "norm22"
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type: "LRN"
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bottom: "pool22"
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top: "norm22"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layer {
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name: "conv23"
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type: "Convolution"
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bottom: "norm22"
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top: "conv23"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "relu23"
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type: "ReLU"
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bottom: "conv23"
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top: "conv23"
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}
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layer {
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name: "conv24"
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type: "Convolution"
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bottom: "conv23"
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top: "conv24"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 1
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}
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}
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}
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layer {
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name: "relu24"
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type: "ReLU"
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bottom: "conv24"
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top: "conv24"
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}
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layer {
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name: "conv25"
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type: "Convolution"
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bottom: "conv24"
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top: "conv25"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 1
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kernel_size: 3
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 1
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}
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}
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}
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layer {
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name: "relu25"
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type: "ReLU"
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bottom: "conv25"
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top: "conv25"
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}
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layer {
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name: "pool25"
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type: "Pooling"
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bottom: "conv25"
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top: "pool25"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "concat1"
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type: "Concat"
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bottom: "pool15"
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bottom: "pool25"
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top: "poolConcat"
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}
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layer {
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name: "fc6"
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type: "InnerProduct"
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bottom: "poolConcat"
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top: "fc6"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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inner_product_param {
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num_output: 4096
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weight_filler {
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type: "gaussian"
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std: 0.005
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}
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bias_filler {
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type: "constant"
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value: 1
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}
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}
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}
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layer {
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name: "relu6"
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type: "ReLU"
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bottom: "fc6"
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top: "fc6"
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}
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layer {
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name: "drop6"
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type: "Dropout"
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bottom: "fc6"
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top: "fc6"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layer {
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name: "fc7"
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type: "InnerProduct"
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bottom: "fc6"
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top: "fc7"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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inner_product_param {
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num_output: 4096
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weight_filler {
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type: "gaussian"
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std: 0.005
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}
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bias_filler {
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type: "constant"
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value: 1
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}
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}
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}
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layer {
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name: "relu7"
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type: "ReLU"
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bottom: "fc7"
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top: "fc7"
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}
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layer {
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name: "drop7"
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type: "Dropout"
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bottom: "fc7"
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top: "fc7"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layer {
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name: "fc8"
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type: "InnerProduct"
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bottom: "fc7"
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top: "fc8"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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inner_product_param {
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num_output: 4
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "scale"
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bottom: "fc8"
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top: "out"
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type: "Power"
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power_param {
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power: 1
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scale: 10
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shift: 0
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}
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}
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