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Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,546 @@ name: "VGG_ILSVRC_16_layers" layer { name: "train-data" type: "Data" top: "data" top: "label" include { stage: "train" } transform_param { mirror: true crop_size: 224 } data_param { batch_size: 16 } } layer { name: "val-data" type: "Data" top: "data" top: "label" include { stage: "val" } transform_param { crop_size: 224 } data_param { batch_size: 16 } } layer { name: "conv1_1" type: "Convolution" bottom: "data" top: "conv1_1" convolution_param { num_output: 64 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu1_1" type: "ReLU" bottom: "conv1_1" top: "conv1_1" } layer { name: "conv1_2" type: "Convolution" bottom: "conv1_1" top: "conv1_2" convolution_param { num_output: 64 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu1_2" type: "ReLU" bottom: "conv1_2" top: "conv1_2" } layer { name: "pool1" type: "Pooling" bottom: "conv1_2" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2_1" type: "Convolution" bottom: "pool1" top: "conv2_1" convolution_param { num_output: 128 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu2_1" type: "ReLU" bottom: "conv2_1" top: "conv2_1" } layer { name: "conv2_2" type: "Convolution" bottom: "conv2_1" top: "conv2_2" convolution_param { num_output: 128 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu2_2" type: "ReLU" bottom: "conv2_2" top: "conv2_2" } layer { name: "pool2" type: "Pooling" bottom: "conv2_2" top: "pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv3_1" type: "Convolution" bottom: "pool2" top: "conv3_1" convolution_param { num_output: 256 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu3_1" type: "ReLU" bottom: "conv3_1" top: "conv3_1" } layer { name: "conv3_2" type: "Convolution" bottom: "conv3_1" top: "conv3_2" convolution_param { num_output: 256 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu3_2" type: "ReLU" bottom: "conv3_2" top: "conv3_2" } layer { name: "conv3_3" type: "Convolution" bottom: "conv3_2" top: "conv3_3" convolution_param { num_output: 256 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu3_3" type: "ReLU" bottom: "conv3_3" top: "conv3_3" } layer { name: "pool3" type: "Pooling" bottom: "conv3_3" top: "pool3" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv4_1" type: "Convolution" bottom: "pool3" top: "conv4_1" convolution_param { num_output: 512 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu4_1" type: "ReLU" bottom: "conv4_1" top: "conv4_1" } layer { name: "conv4_2" type: "Convolution" bottom: "conv4_1" top: "conv4_2" convolution_param { num_output: 512 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu4_2" type: "ReLU" bottom: "conv4_2" top: "conv4_2" } layer { name: "conv4_3" type: "Convolution" bottom: "conv4_2" top: "conv4_3" convolution_param { num_output: 512 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu4_3" type: "ReLU" bottom: "conv4_3" top: "conv4_3" } layer { name: "pool4" type: "Pooling" bottom: "conv4_3" top: "pool4" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv5_1" type: "Convolution" bottom: "pool4" top: "conv5_1" convolution_param { num_output: 512 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu5_1" type: "ReLU" bottom: "conv5_1" top: "conv5_1" } layer { name: "conv5_2" type: "Convolution" bottom: "conv5_1" top: "conv5_2" convolution_param { num_output: 512 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu5_2" type: "ReLU" bottom: "conv5_2" top: "conv5_2" } layer { name: "conv5_3" type: "Convolution" bottom: "conv5_2" top: "conv5_3" convolution_param { num_output: 512 kernel_size: 3 pad: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.0 } } } layer { name: "relu5_3" type: "ReLU" bottom: "conv5_3" top: "conv5_3" } layer { name: "pool5" type: "Pooling" bottom: "conv5_3" top: "pool5" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "fc6" type: "InnerProduct" bottom: "pool5" top: "fc6" inner_product_param { num_output: 4096 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.1 } } layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" } layer { name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param { dropout_ratio: 0.5 } } layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "fc7" inner_product_param { num_output: 4096 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.1 } } layer { name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7" } layer { name: "drop7" type: "Dropout" bottom: "fc7" top: "fc7" dropout_param { dropout_ratio: 0.5 } } layer { name: "fc8" type: "InnerProduct" bottom: "fc7" top: "fc8" inner_product_param { num_output: 2 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.1 } } layer { name: "accuracy" type: "Accuracy" bottom: "fc8" bottom: "label" top: "accuracy" include { stage: "val" } } layer { name: "loss" type: "SoftmaxWithLoss" bottom: "fc8" bottom: "label" top: "loss" exclude { stage: "deploy" } } layer { name: "softmax" type: "Softmax" bottom: "fc8" top: "softmax" include { stage: "deploy" } }