name: "face_48" input: "data" input_shape { dim:1 dim:3 dim:48 dim:48 } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 16 kernel_size: 3 stride: 1 weight_filler { type: "gaussian" std: 0.0001 } bias_filler { type: "constant" } } } layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "relu1" type: "ReLU" bottom: "pool1" top: "pool1" } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 32 kernel_size: 3 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "relu2" type: "ReLU" bottom: "pool2" top: "pool2" } layer { name: "conv3" type: "Convolution" bottom: "pool2" top: "conv3" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 64 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "pool3" type: "Pooling" bottom: "conv3" top: "pool3" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "relu3" type: "ReLU" bottom: "pool3" top: "pool3" } layer { name: "conv4" type: "Convolution" bottom: "pool3" top: "conv4" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 128 kernel_size: 2 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4" } layer { name: "fc5" type: "InnerProduct" bottom: "conv4" top: "fc5" inner_product_param { num_output: 256 weight_filler { type: "gaussian" std: 0.1 } bias_filler { type: "constant" } } } layer { name: "relu5" type: "ReLU" bottom: "fc5" top: "fc5" } layer { name: "fc6" type: "InnerProduct" bottom: "fc5" top: "fc6" inner_product_param { num_output: 2 weight_filler { type: "gaussian" std: 0.1 } bias_filler { type: "constant" } } } layer { name:"prob" type:"Softmax" bottom:"fc6" top:"prob" }