input: "data" input_shape { dim: 1 dim: 1 dim: 256 dim: 256 } layer { name: "conv0" type: "Convolution" bottom: "data" top: "conv0" param { lr_mult: 0.0 } convolution_param { num_output: 30 bias_term: false pad: 2 kernel_size: 5 stride: 1 weight_filler { type: "srm" } } } layer { name: "conv1" type: "Convolution" bottom: "conv0" top: "conv1" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 30 bias_term: false pad: 2 kernel_size: 5 stride: 1 weight_filler { type: "xavier" } } } layer { name: "abs" type: "AbsVal" bottom: "conv1" top: "abs" } layer { name: "batchnormalization1" type: "BatchNorm" bottom: "abs" top: "batchnormalization1" param { lr_mult: 1.00001 decay_mult: 0.0 } param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 1.0 } batch_norm_param { scale_filler { type: "constant" value: 1.0 } bias_filler { type: "xavier" } } } layer { name: "scale1" type: "Scale" bottom: "batchnormalization1" top: "scale1" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 1.0 } scale_param { bias_term: true } } layer { name: "truncation1" type: "Trunc" bottom: "scale1" top: "truncation1" } layer { name: "conv2" type: "Convolution" bottom: "truncation1" top: "conv2" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 30 bias_term: false pad: 2 kernel_size: 5 stride: 1 weight_filler { type: "xavier" } } } layer { name: "batchnormalization2" type: "BatchNorm" bottom: "conv2" top: "batchnormalization2" param { lr_mult: 1.00001 decay_mult: 0.0 } param { lr_mult: 1.00001 decay_mult: 0.0 } batch_norm_param { scale_filler { type: "constant" value: 1.0 } bias_filler { type: "xavier" } } } layer { name: "scale2" type: "Scale" bottom: "batchnormalization2" top: "scale2" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 1.0 } scale_param { bias_term: true } } layer { name: "truncation2" type: "Trunc" bottom: "scale2" top: "truncation2" trunc_param { scale: 2.0 } } layer { name: "pool2" type: "Pooling" bottom: "truncation2" top: "pool2" pooling_param { pool: AVE kernel_size: 5 stride: 2 pad: 1 } } layer { name: "conv3" type: "Convolution" bottom: "pool2" top: "conv3" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 32 bias_term: false pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } } } layer { name: "batchnormalization3" type: "BatchNorm" bottom: "conv3" top: "batchnormalization3" param { lr_mult: 1.00001 decay_mult: 0.0 } param { lr_mult: 1.00001 decay_mult: 0.0 } batch_norm_param { scale_filler { type: "constant" value: 1.0 } bias_filler { type: "constant" value: 0.001 } } } layer { name: "scale3" type: "Scale" bottom: "batchnormalization3" top: "scale3" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 1.0 } scale_param { bias_term: true } } layer { name: "relu1" type: "ReLU" bottom: "scale3" top: "scale3" } layer { name: "pool3" type: "Pooling" bottom: "scale3" top: "pool3" pooling_param { pool: AVE kernel_size: 5 stride: 2 pad: 1 } } layer { name: "conv4" type: "Convolution" bottom: "pool3" top: "conv4" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 64 bias_term: false pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } } } layer { name: "batchnormalization4" type: "BatchNorm" bottom: "conv4" top: "batchnormalization4" param { lr_mult: 1.00001 decay_mult: 0.0 } param { lr_mult: 1.00001 decay_mult: 0.0 } batch_norm_param { scale_filler { type: "constant" value: 1.0 } bias_filler { type: "constant" value: 0.001 } } } layer { name: "scale4" type: "Scale" bottom: "batchnormalization4" top: "scale4" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 1.0 } scale_param { bias_term: true } } layer { name: "relu2" type: "ReLU" bottom: "scale4" top: "scale4" } layer { name: "pool4" type: "Pooling" bottom: "scale4" top: "pool4" pooling_param { pool: AVE kernel_size: 5 stride: 2 pad: 1 } } layer { name: "conv5" type: "Convolution" bottom: "pool4" top: "conv5" param { lr_mult: 1.0 decay_mult: 1.0 } convolution_param { num_output: 128 bias_term: false pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } } } layer { name: "batchnormalization5" type: "BatchNorm" bottom: "conv5" top: "batchnormalization5" param { lr_mult: 1.00001 decay_mult: 0.0 } param { lr_mult: 1.00001 decay_mult: 0.0 } batch_norm_param { scale_filler { type: "constant" value: 1.0 } bias_filler { type: "constant" value: 0.001 } } } layer { name: "scale5" type: "Scale" bottom: "batchnormalization5" top: "scale5" param { lr_mult: 1.0 decay_mult: 1.0 } param { lr_mult: 2.0 decay_mult: 1.0 } scale_param { bias_term: true } } layer { name: "relu3" type: "ReLU" bottom: "scale5" top: "scale5" } layer { name: "pool5" type: "Pooling" bottom: "scale5" top: "pool5" pooling_param { pool: AVE kernel_size: 32 stride: 1 } } layer { name: "fc1" type: "InnerProduct" bottom: "pool5" top: "fc1" param { lr_mult: 1.0 } param { lr_mult: 2.0 } inner_product_param { num_output: 256 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "relu4" type: "ReLU" bottom: "fc1" top: "fc1" } layer { name: "fc2" type: "InnerProduct" bottom: "fc1" top: "fc2" param { lr_mult: 1.0 } param { lr_mult: 2.0 } inner_product_param { num_output: 1024 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "relu5" type: "ReLU" bottom: "fc2" top: "fc2" } layer { name: "fc3" type: "InnerProduct" bottom: "fc2" top: "fc3" param { lr_mult: 1.0 } param { lr_mult: 2.0 } inner_product_param { num_output: 2 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "softmax" type: "Softmax" bottom: "fc3" top: "softmax" }