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455 lines
7.2 KiB
Plaintext
455 lines
7.2 KiB
Plaintext
input: "data"
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input_dim: 1
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input_dim: 3
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input_dim: 160
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input_dim: 40
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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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convolution_param {
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num_output: 32
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bias_term: true
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pad_h: 1
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pad_w: 1
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kernel_h: 3
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kernel_w: 3
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stride_h: 1
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stride_w: 1
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}
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}
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layer {
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name: "bn0"
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type: "BatchNorm"
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bottom: "conv0"
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top: "bn0"
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batch_norm_param {
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moving_average_fraction: 0.99
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eps: 0.001
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}
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}
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layer {
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name: "bn0_scale"
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type: "Scale"
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bottom: "bn0"
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top: "bn0"
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "relu0"
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type: "ReLU"
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bottom: "bn0"
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top: "bn0"
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}
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layer {
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name: "pool0"
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type: "Pooling"
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bottom: "bn0"
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top: "pool0"
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pooling_param {
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pool: MAX
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kernel_h: 2
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kernel_w: 2
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stride_h: 2
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stride_w: 2
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pad_h: 0
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pad_w: 0
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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: "pool0"
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top: "conv1"
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convolution_param {
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num_output: 64
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bias_term: true
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pad_h: 1
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pad_w: 1
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kernel_h: 3
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kernel_w: 3
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stride_h: 1
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stride_w: 1
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}
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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: "conv1"
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top: "bn1"
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batch_norm_param {
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moving_average_fraction: 0.99
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eps: 0.001
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}
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}
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layer {
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name: "bn1_scale"
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type: "Scale"
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bottom: "bn1"
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top: "bn1"
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "relu1"
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type: "ReLU"
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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: "pool1"
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type: "Pooling"
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bottom: "bn1"
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top: "pool1"
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pooling_param {
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pool: MAX
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kernel_h: 2
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kernel_w: 2
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stride_h: 2
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stride_w: 2
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pad_h: 0
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pad_w: 0
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}
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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: "pool1"
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top: "conv2"
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convolution_param {
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num_output: 128
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bias_term: true
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pad_h: 1
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pad_w: 1
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kernel_h: 3
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kernel_w: 3
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stride_h: 1
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stride_w: 1
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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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batch_norm_param {
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moving_average_fraction: 0.99
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eps: 0.001
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}
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}
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layer {
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name: "bn2_scale"
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type: "Scale"
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bottom: "bn2"
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top: "bn2"
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "relu2"
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type: "ReLU"
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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: "pool2"
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type: "Pooling"
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bottom: "bn2"
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top: "pool2"
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pooling_param {
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pool: MAX
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kernel_h: 2
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kernel_w: 2
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stride_h: 2
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stride_w: 2
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pad_h: 0
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pad_w: 0
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}
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}
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layer {
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name: "conv2d_1"
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type: "Convolution"
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bottom: "pool2"
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top: "conv2d_1"
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convolution_param {
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num_output: 256
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bias_term: true
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pad_h: 0
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pad_w: 0
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kernel_h: 1
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kernel_w: 5
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stride_h: 1
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stride_w: 1
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}
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}
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layer {
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name: "batch_normalization_1"
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type: "BatchNorm"
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bottom: "conv2d_1"
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top: "batch_normalization_1"
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batch_norm_param {
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moving_average_fraction: 0.99
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eps: 0.001
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}
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}
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layer {
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name: "batch_normalization_1_scale"
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type: "Scale"
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bottom: "batch_normalization_1"
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top: "batch_normalization_1"
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "activation_1"
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type: "ReLU"
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bottom: "batch_normalization_1"
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top: "batch_normalization_1"
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}
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layer {
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name: "conv2d_2"
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type: "Convolution"
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bottom: "batch_normalization_1"
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top: "conv2d_2"
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convolution_param {
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num_output: 256
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bias_term: true
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pad_h: 3
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pad_w: 0
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kernel_h: 7
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kernel_w: 1
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stride_h: 1
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stride_w: 1
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}
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}
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layer {
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name: "conv2d_3"
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type: "Convolution"
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bottom: "batch_normalization_1"
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top: "conv2d_3"
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convolution_param {
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num_output: 256
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bias_term: true
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pad_h: 2
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pad_w: 0
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kernel_h: 5
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kernel_w: 1
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stride_h: 1
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stride_w: 1
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}
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}
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layer {
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name: "conv2d_4"
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type: "Convolution"
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bottom: "batch_normalization_1"
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top: "conv2d_4"
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convolution_param {
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num_output: 256
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bias_term: true
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pad_h: 1
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pad_w: 0
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kernel_h: 3
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kernel_w: 1
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stride_h: 1
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stride_w: 1
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}
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}
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layer {
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name: "conv2d_5"
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type: "Convolution"
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bottom: "batch_normalization_1"
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top: "conv2d_5"
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convolution_param {
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num_output: 256
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bias_term: true
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pad_h: 0
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pad_w: 0
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kernel_h: 1
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kernel_w: 1
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stride_h: 1
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stride_w: 1
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}
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}
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layer {
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name: "batch_normalization_2"
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type: "BatchNorm"
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bottom: "conv2d_2"
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top: "batch_normalization_2"
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batch_norm_param {
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moving_average_fraction: 0.99
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eps: 0.001
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}
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}
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layer {
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name: "batch_normalization_2_scale"
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type: "Scale"
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bottom: "batch_normalization_2"
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top: "batch_normalization_2"
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "batch_normalization_3"
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type: "BatchNorm"
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bottom: "conv2d_3"
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top: "batch_normalization_3"
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batch_norm_param {
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moving_average_fraction: 0.99
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eps: 0.001
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}
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}
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layer {
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name: "batch_normalization_3_scale"
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type: "Scale"
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bottom: "batch_normalization_3"
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top: "batch_normalization_3"
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "batch_normalization_4"
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type: "BatchNorm"
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bottom: "conv2d_4"
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top: "batch_normalization_4"
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batch_norm_param {
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moving_average_fraction: 0.99
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eps: 0.001
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}
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}
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layer {
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name: "batch_normalization_4_scale"
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type: "Scale"
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bottom: "batch_normalization_4"
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top: "batch_normalization_4"
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "batch_normalization_5"
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type: "BatchNorm"
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bottom: "conv2d_5"
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top: "batch_normalization_5"
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batch_norm_param {
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moving_average_fraction: 0.99
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eps: 0.001
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}
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}
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layer {
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name: "batch_normalization_5_scale"
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type: "Scale"
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bottom: "batch_normalization_5"
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top: "batch_normalization_5"
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "activation_2"
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type: "ReLU"
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bottom: "batch_normalization_2"
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top: "batch_normalization_2"
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}
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layer {
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name: "activation_3"
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type: "ReLU"
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bottom: "batch_normalization_3"
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top: "batch_normalization_3"
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}
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layer {
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name: "activation_4"
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type: "ReLU"
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bottom: "batch_normalization_4"
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top: "batch_normalization_4"
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}
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layer {
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name: "activation_5"
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type: "ReLU"
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bottom: "batch_normalization_5"
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top: "batch_normalization_5"
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}
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layer {
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name: "concatenate_1"
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type: "Concat"
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bottom: "batch_normalization_2"
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bottom: "batch_normalization_3"
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bottom: "batch_normalization_4"
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bottom: "batch_normalization_5"
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top: "concatenate_1"
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concat_param {
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axis: 1
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}
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}
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layer {
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name: "conv_1024_11"
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type: "Convolution"
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bottom: "concatenate_1"
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top: "conv_1024_11"
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convolution_param {
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num_output: 1024
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bias_term: true
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pad_h: 0
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pad_w: 0
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kernel_h: 1
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kernel_w: 1
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stride_h: 1
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stride_w: 1
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}
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}
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layer {
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name: "batch_normalization_6"
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type: "BatchNorm"
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bottom: "conv_1024_11"
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top: "batch_normalization_6"
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batch_norm_param {
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moving_average_fraction: 0.99
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eps: 0.001
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}
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}
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layer {
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name: "batch_normalization_6_scale"
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type: "Scale"
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bottom: "batch_normalization_6"
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top: "batch_normalization_6"
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "activation_6"
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type: "ReLU"
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bottom: "batch_normalization_6"
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top: "batch_normalization_6"
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}
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layer {
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name: "conv_class_11"
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type: "Convolution"
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bottom: "batch_normalization_6"
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top: "conv_class_11"
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convolution_param {
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num_output: 84
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bias_term: true
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pad_h: 0
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pad_w: 0
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kernel_h: 1
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kernel_w: 1
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stride_h: 1
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stride_w: 1
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}
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}
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layer {
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name: "prob"
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type: "Softmax"
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bottom: "conv_class_11"
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top: "prob"
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}
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