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December 15, 2017 05:25
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deepID1_train_test.prototxt
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| ############################# DATA Layer ############################# | |
| name: "face_train_val" | |
| layer { | |
| top: "data_1" | |
| top: "label_1" | |
| name: "data_1" | |
| type: "Data" | |
| data_param { | |
| source: "examples/deepid1/DeepID1_train_lmdb" | |
| backend:LMDB | |
| batch_size: 64 | |
| } | |
| transform_param { | |
| mean_file: "examples/deepid1/DeepID1_mean.proto" | |
| mirror: true | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| layer { | |
| top: "data_1" | |
| top: "label_1" | |
| name: "data_1" | |
| type: "Data" | |
| data_param { | |
| source: "examples/deepid1/DeepID1_test_lmdb" | |
| backend:LMDB | |
| batch_size: 64 | |
| } | |
| transform_param { | |
| mean_file: "examples/deepid1/DeepID1_mean.proto" | |
| mirror: true | |
| } | |
| include: { | |
| phase: TEST | |
| } | |
| } | |
| ############################# CONV NET 1 ############################# | |
| layer { | |
| name: "conv1_1" | |
| type: "Convolution" | |
| bottom: "data_1" | |
| top: "conv1_1" | |
| param { | |
| name: "conv1_w" | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv1_b" | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 20 | |
| kernel_size: 4 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_1" | |
| type: "ReLU" | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| } | |
| layer { | |
| name: "norm1_1" | |
| type: "LRN" | |
| bottom: "conv1_1" | |
| top: "norm1_1" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "pool1_1" | |
| type: "Pooling" | |
| bottom: "norm1_1" | |
| top: "pool1_1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1" | |
| type: "Convolution" | |
| bottom: "pool1_1" | |
| top: "conv2_1" | |
| param { | |
| name: "conv2_w" | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv2_b" | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 40 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_1" | |
| type: "ReLU" | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| } | |
| layer { | |
| name: "norm2_1" | |
| type: "LRN" | |
| bottom: "conv2_1" | |
| top: "norm2_1" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "pool2_1" | |
| type: "Pooling" | |
| bottom: "norm2_1" | |
| top: "pool2_1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Convolution" | |
| bottom: "pool2_1" | |
| top: "conv3_1" | |
| param { | |
| name: "conv3_w" | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv3_b" | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 60 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool3_1" | |
| type: "Pooling" | |
| bottom: "conv3_1" | |
| top: "pool3_1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Convolution" | |
| bottom: "pool3_1" | |
| top: "conv4_1" | |
| param { | |
| name: "conv4_w" | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv4_b" | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 80 | |
| kernel_size: 2 | |
| stride: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer{ | |
| name:"flatten_pool3_1" | |
| type:"Flatten" | |
| bottom:"pool3_1" | |
| top:"flatten_pool3_1" | |
| } | |
| layer{ | |
| name:"flatten_conv4_1" | |
| type:"Flatten" | |
| bottom:"conv4_1" | |
| top:"flatten_conv4_1" | |
| } | |
| layer{ | |
| name:"contact_conv" | |
| type:"Concat" | |
| bottom:"flatten_conv4_1" | |
| bottom:"flatten_pool3_1" | |
| top:"contact_conv" | |
| } | |
| layer { | |
| name: "deepid_1" | |
| type: "InnerProduct" | |
| bottom: "contact_conv" | |
| top: "deepid_1" | |
| param { | |
| name: "fc6_w" | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "fc6_b" | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 160 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| #layer { | |
| # name: "relu6_1" | |
| # type: "ReLU" | |
| # bottom: "deepid_1" | |
| # top: "deepid_1" | |
| #} | |
| layer { | |
| name: "drop6_1" | |
| type: "Dropout" | |
| bottom: "deepid_1" | |
| top: "deepid_1" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc8_1" | |
| type: "InnerProduct" | |
| bottom: "deepid_1" | |
| top: "fc8_1" | |
| param { | |
| name: "fc8_w" | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "fc8_b" | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 9953 #9953 #for ms-celeb-1m | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "accuracy_1" | |
| type: "Accuracy" | |
| bottom: "fc8_1" | |
| bottom: "label_1" | |
| top: "accuracy_1" | |
| include: { phase: TEST } | |
| } | |
| layer { | |
| name: "loss_1" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc8_1" | |
| bottom: "label_1" | |
| top: "loss_1" | |
| #loss_weight: 0.5 | |
| } |
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