464 lines
6.6 KiB
Plaintext
464 lines
6.6 KiB
Plaintext
layer {
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name: "data"
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type: "Python"
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top: "data"
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top: "label"
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python_param {
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module: "AugStegoDataLayer"
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layer: "AugmentDataLayerSync"
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param_str: "{\'im_shape\': [512, 512], \'root\': \'/home/mchen/tmp/caffe/data/JStego/JUNI_0.4/\', \'split\': \'train\', \'batch_size\': 40}"
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}
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}
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layer {
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name: "conv0"
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type: "Convolution"
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bottom: "data"
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top: "conv0"
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 4
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pad: 2
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kernel_size: 5
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stride: 1
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weight_filler {
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type: "constant"
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value: 0.0
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "conv1"
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type: "Convolution"
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bottom: "conv0"
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top: "conv1"
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 8
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pad: 2
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kernel_size: 5
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stride: 1
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weight_filler {
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type: "gaussian"
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std: 0.00999999977648
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "abs1"
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type: "AbsVal"
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bottom: "conv1"
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top: "abs1"
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}
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layer {
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name: "bn1"
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type: "BatchNorm"
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bottom: "abs1"
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top: "bn1"
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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batch_norm_param {
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moving_average_fraction: 0.980000019073
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eps: 9.99999974738e-05
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scale_filler {
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type: "constant"
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value: 1.0
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "tanh1"
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type: "TanH"
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bottom: "bn1"
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top: "bn1"
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}
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layer {
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name: "conv2"
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type: "Convolution"
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bottom: "bn1"
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top: "conv2"
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 16
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pad: 2
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kernel_size: 5
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stride: 1
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weight_filler {
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type: "gaussian"
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std: 0.00999999977648
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "bn2"
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type: "BatchNorm"
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bottom: "conv2"
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top: "bn2"
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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batch_norm_param {
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moving_average_fraction: 0.980000019073
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eps: 9.99999974738e-05
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scale_filler {
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type: "constant"
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value: 1.0
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "tanh2"
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type: "TanH"
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bottom: "bn2"
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top: "bn2"
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}
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layer {
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name: "sbp"
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type: "SplitByPhase"
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bottom: "bn2"
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top: "sbp"
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}
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layer {
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name: "conv3"
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type: "Convolution"
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bottom: "sbp"
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top: "conv3"
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 128
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pad: 0
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kernel_size: 1
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stride: 1
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weight_filler {
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type: "gaussian"
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std: 0.00999999977648
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "bn3"
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type: "BatchNorm"
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bottom: "conv3"
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top: "bn3"
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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batch_norm_param {
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moving_average_fraction: 0.980000019073
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eps: 9.99999974738e-05
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scale_filler {
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type: "constant"
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value: 1.0
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "relu3"
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type: "ReLU"
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bottom: "bn3"
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top: "bn3"
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}
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layer {
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name: "pool3"
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type: "Pooling"
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bottom: "bn3"
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top: "pool3"
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pooling_param {
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pool: AVE
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kernel_size: 5
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stride: 2
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pad: 1
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}
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}
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layer {
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name: "conv4"
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type: "Convolution"
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bottom: "pool3"
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top: "conv4"
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 256
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pad: 0
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kernel_size: 1
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stride: 1
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weight_filler {
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type: "gaussian"
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std: 0.00999999977648
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "bn4"
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type: "BatchNorm"
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bottom: "conv4"
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top: "bn4"
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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batch_norm_param {
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moving_average_fraction: 0.980000019073
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eps: 9.99999974738e-05
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scale_filler {
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type: "constant"
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value: 1.0
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "relu4"
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type: "ReLU"
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bottom: "bn4"
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top: "bn4"
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}
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layer {
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name: "pool4"
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type: "Pooling"
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bottom: "bn4"
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top: "pool4"
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pooling_param {
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pool: AVE
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kernel_size: 5
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stride: 2
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pad: 1
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}
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}
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layer {
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name: "conv5"
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type: "Convolution"
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bottom: "pool4"
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top: "conv5"
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 512
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pad: 0
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kernel_size: 1
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stride: 1
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weight_filler {
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type: "gaussian"
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std: 0.00999999977648
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "bn5"
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type: "BatchNorm"
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bottom: "conv5"
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top: "bn5"
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 1.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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param {
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lr_mult: 0.0
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decay_mult: 0.0
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}
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batch_norm_param {
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moving_average_fraction: 0.980000019073
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eps: 9.99999974738e-05
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scale_filler {
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type: "constant"
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value: 1.0
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "relu5"
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type: "ReLU"
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bottom: "bn5"
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top: "bn5"
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}
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layer {
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name: "pool5"
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type: "Pooling"
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bottom: "bn5"
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top: "pool5"
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pooling_param {
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pool: AVE
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global_pooling: true
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}
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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: "pool5"
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top: "fc6"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 0.0
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}
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inner_product_param {
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num_output: 2
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weight_filler {
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type: "xavier"
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}
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bias_filler {
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type: "constant"
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value: 0.00999999977648
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}
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}
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}
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layer {
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name: "loss"
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type: "SoftmaxWithLoss"
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bottom: "fc6"
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bottom: "label"
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top: "loss"
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}
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layer {
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name: "acc"
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type: "Accuracy"
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bottom: "fc6"
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bottom: "label"
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top: "acc"
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}
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