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March 2, 2018 22:31
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| name: "VGG_ILSVRC_16_layers" | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| crop_size: 224 | |
| mean_value: 104 | |
| mean_value: 117 | |
| mean_value: 123 | |
| mirror: true | |
| } | |
| data_param { | |
| source: "data_set/train_lmdb" | |
| batch_size: 128 | |
| backend: LMDB | |
| } | |
| top: "data" | |
| top: "label" | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| crop_size: 224 | |
| mean_value: 104 | |
| mean_value: 117 | |
| mean_value: 123 | |
| mirror: false | |
| } | |
| data_param { | |
| source: "data_set/val_lmdb" | |
| batch_size: 32 | |
| backend: LMDB | |
| } | |
| top: "data" | |
| top: "label" | |
| } | |
| layer { | |
| bottom: "data" | |
| top: "conv1_1" | |
| name: "conv1_1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| name: "relu1_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| name: "conv1_2" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| name: "relu1_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "pool1" | |
| name: "pool1" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "conv2_1" | |
| name: "conv2_1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| name: "relu2_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| name: "conv2_2" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| name: "relu2_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "pool2" | |
| name: "pool2" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| name: "conv3_1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| name: "relu3_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| name: "conv3_2" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| name: "relu3_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| name: "conv3_3" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| name: "relu3_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "pool3" | |
| name: "pool3" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| name: "conv4_1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| name: "relu4_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| name: "conv4_2" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| name: "relu4_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| name: "conv4_3" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| name: "relu4_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "pool4" | |
| name: "pool4" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| name: "conv5_1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| name: "relu5_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| name: "conv5_2" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| name: "relu5_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| name: "conv5_3" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| name: "relu5_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "pool5" | |
| name: "pool5" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool5" | |
| top: "fc6" | |
| name: "fc6" | |
| type: "InnerProduct" | |
| inner_product_param { | |
| num_output: 4096 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "fc6" | |
| top: "fc6" | |
| name: "relu6" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "fc6" | |
| top: "fc6" | |
| name: "drop6" | |
| type: "Dropout" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| bottom: "fc6" | |
| top: "fc7" | |
| name: "fc7" | |
| type: "InnerProduct" | |
| inner_product_param { | |
| num_output: 4096 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| bottom: "fc7" | |
| top: "fc7" | |
| name: "relu7" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "fc7" | |
| top: "fc7" | |
| name: "drop7" | |
| type: "Dropout" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc8" | |
| bottom: "fc7" | |
| top: "fc8" | |
| type: "InnerProduct" | |
| inner_product_param { | |
| num_output: 1000 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc8" | |
| bottom: "label" | |
| top: "loss/loss" | |
| } | |
| layer { | |
| name: "accuracy/top1" | |
| type: "Accuracy" | |
| bottom: "fc8" | |
| bottom: "label" | |
| top: "accuracy@1" | |
| include: { phase: TEST } | |
| accuracy_param { | |
| top_k: 1 | |
| } | |
| } | |
| layer { | |
| name: "accuracy/top5" | |
| type: "Accuracy" | |
| bottom: "fc8" | |
| bottom: "label" | |
| top: "accuracy@5" | |
| include: { phase: TEST } | |
| accuracy_param { | |
| top_k: 5 | |
| } | |
| } |
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