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add VGG16-SSDH example
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‎examples/SSDH-VGG16-48/deploy.prototxt‎

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name: "VGG_ILSVRC_16_layers"
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input: "data"
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input_dim: 10
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input_dim: 3
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input_dim: 224
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input_dim: 224
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layer {
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name: "conv1_1"
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type: "Convolution"
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bottom: "data"
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top: "conv1_1"
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convolution_param {
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num_output: 64
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pad: 1
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kernel_size: 3
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}
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}
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layer {
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name: "relu1_1"
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type: "ReLU"
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bottom: "conv1_1"
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top: "conv1_1"
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}
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layer {
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name: "conv1_2"
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type: "Convolution"
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bottom: "conv1_1"
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top: "conv1_2"
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convolution_param {
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num_output: 64
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pad: 1
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kernel_size: 3
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}
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}
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layer {
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name: "relu1_2"
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type: "ReLU"
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bottom: "conv1_2"
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top: "conv1_2"
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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: "conv1_2"
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top: "pool1"
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pooling_param {
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pool: MAX
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kernel_size: 2
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stride: 2
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}
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}
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layer {
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name: "conv2_1"
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type: "Convolution"
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bottom: "pool1"
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top: "conv2_1"
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convolution_param {
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num_output: 128
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pad: 1
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kernel_size: 3
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}
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}
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layer {
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name: "relu2_1"
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type: "ReLU"
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bottom: "conv2_1"
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top: "conv2_1"
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}
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layer {
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name: "conv2_2"
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type: "Convolution"
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bottom: "conv2_1"
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top: "conv2_2"
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convolution_param {
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num_output: 128
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pad: 1
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kernel_size: 3
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}
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}
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layer {
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name: "relu2_2"
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type: "ReLU"
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bottom: "conv2_2"
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top: "conv2_2"
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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: "conv2_2"
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top: "pool2"
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pooling_param {
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pool: MAX
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kernel_size: 2
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stride: 2
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}
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}
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layer {
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name: "conv3_1"
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type: "Convolution"
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bottom: "pool2"
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top: "conv3_1"
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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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}
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}
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layer {
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name: "relu3_1"
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type: "ReLU"
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bottom: "conv3_1"
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top: "conv3_1"
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}
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layer {
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name: "conv3_2"
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type: "Convolution"
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bottom: "conv3_1"
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top: "conv3_2"
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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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}
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}
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layer {
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name: "relu3_2"
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type: "ReLU"
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bottom: "conv3_2"
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top: "conv3_2"
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}
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layer {
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name: "conv3_3"
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type: "Convolution"
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bottom: "conv3_2"
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top: "conv3_3"
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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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}
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}
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layer {
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name: "relu3_3"
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type: "ReLU"
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bottom: "conv3_3"
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top: "conv3_3"
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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: "conv3_3"
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top: "pool3"
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pooling_param {
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pool: MAX
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kernel_size: 2
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stride: 2
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}
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}
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layer {
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name: "conv4_1"
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type: "Convolution"
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bottom: "pool3"
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top: "conv4_1"
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convolution_param {
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num_output: 512
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pad: 1
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kernel_size: 3
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}
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}
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layer {
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name: "relu4_1"
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type: "ReLU"
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bottom: "conv4_1"
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top: "conv4_1"
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}
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layer {
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name: "conv4_2"
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type: "Convolution"
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bottom: "conv4_1"
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top: "conv4_2"
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convolution_param {
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num_output: 512
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pad: 1
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kernel_size: 3
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}
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}
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layer {
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name: "relu4_2"
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type: "ReLU"
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bottom: "conv4_2"
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top: "conv4_2"
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}
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layer {
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name: "conv4_3"
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type: "Convolution"
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bottom: "conv4_2"
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top: "conv4_3"
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convolution_param {
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num_output: 512
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pad: 1
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kernel_size: 3
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}
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}
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layer {
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name: "relu4_3"
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type: "ReLU"
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bottom: "conv4_3"
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top: "conv4_3"
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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: "conv4_3"
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top: "pool4"
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pooling_param {
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pool: MAX
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kernel_size: 2
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stride: 2
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}
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}
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layer {
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name: "conv5_1"
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type: "Convolution"
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bottom: "pool4"
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top: "conv5_1"
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convolution_param {
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num_output: 512
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pad: 1
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kernel_size: 3
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}
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}
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layer {
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name: "relu5_1"
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type: "ReLU"
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bottom: "conv5_1"
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top: "conv5_1"
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}
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layer {
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name: "conv5_2"
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type: "Convolution"
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bottom: "conv5_1"
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top: "conv5_2"
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convolution_param {
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num_output: 512
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pad: 1
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kernel_size: 3
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}
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}
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layer {
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name: "relu5_2"
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type: "ReLU"
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bottom: "conv5_2"
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top: "conv5_2"
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}
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layer {
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name: "conv5_3"
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type: "Convolution"
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bottom: "conv5_2"
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top: "conv5_3"
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convolution_param {
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num_output: 512
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pad: 1
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kernel_size: 3
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}
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}
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layer {
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name: "relu5_3"
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type: "ReLU"
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bottom: "conv5_3"
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top: "conv5_3"
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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: "conv5_3"
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top: "pool5"
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pooling_param {
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pool: MAX
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kernel_size: 2
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stride: 2
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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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inner_product_param {
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num_output: 4096
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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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inner_product_param {
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num_output: 4096
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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: "latent_layer"
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type: "InnerProduct"
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bottom: "fc7"
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top: "latent_layer"
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inner_product_param {
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num_output: 48
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}
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}
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layer {
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name: "encode_neuron"
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bottom: "latent_layer"
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top: "encode_neuron"
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type: "Sigmoid"
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}

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