Last active
November 10, 2016 23:53
-
-
Save kyamagu/12bbc2b298725f24765e38ef2cbe2c7f to your computer and use it in GitHub Desktop.
Caffe baseline model with AlexNet for CelebA dataset
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| name: "celeba_alexnet_independent" | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| mirror: true | |
| mean_file: "data/celeba/mean.binaryproto" | |
| } | |
| data_param { | |
| source: "data/celeba/train-images.lmdb" | |
| batch_size: 128 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "labels" | |
| type: "Data" | |
| top: "labels" | |
| include { | |
| phase: TRAIN | |
| } | |
| data_param { | |
| source: "data/celeba/train-labels.lmdb" | |
| batch_size: 128 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| mirror: true | |
| mean_file: "data/celeba/mean.binaryproto" | |
| } | |
| data_param { | |
| source: "data/celeba/val-images.lmdb" | |
| batch_size: 128 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "labels" | |
| type: "Data" | |
| top: "labels" | |
| include { | |
| phase: TEST | |
| } | |
| data_param { | |
| source: "data/celeba/val-labels.lmdb" | |
| batch_size: 128 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 0 | |
| kernel_size: 11 | |
| stride: 4 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "norm1" | |
| type: "LRN" | |
| bottom: "conv1" | |
| top: "norm1" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "norm1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 2 | |
| kernel_size: 5 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "norm2" | |
| type: "LRN" | |
| bottom: "conv2" | |
| top: "norm2" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "norm2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3" | |
| type: "ReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "conv3" | |
| top: "conv4" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "conv5" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "conv5" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5" | |
| type: "ReLU" | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "conv5" | |
| top: "pool5" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "fc6r" | |
| type: "InnerProduct" | |
| bottom: "pool5" | |
| top: "fc6r" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6" | |
| type: "ReLU" | |
| bottom: "fc6r" | |
| top: "fc6r" | |
| } | |
| layer { | |
| name: "drop6" | |
| type: "Dropout" | |
| bottom: "fc6r" | |
| top: "fc6r" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc7r" | |
| type: "InnerProduct" | |
| bottom: "fc6r" | |
| top: "fc7r" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7" | |
| type: "ReLU" | |
| bottom: "fc7r" | |
| top: "fc7r" | |
| } | |
| layer { | |
| name: "drop7" | |
| type: "Dropout" | |
| bottom: "fc7r" | |
| top: "fc7r" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "score0" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score0" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score1" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score2" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score3" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score4" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score5" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score6" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score7" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score7" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score8" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score8" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score9" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score9" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score10" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score10" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score11" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score11" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score12" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score12" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score13" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score13" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score14" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score14" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score15" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score15" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score16" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score16" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score17" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score17" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score18" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score18" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score19" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score19" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score20" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score20" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score21" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score21" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score22" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score22" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score23" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score23" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score24" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score24" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score25" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score25" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score26" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score26" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score27" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score27" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score28" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score28" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score29" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score29" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score30" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score30" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score31" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score31" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score32" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score32" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score33" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score33" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score34" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score34" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score35" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score35" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score36" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score36" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score37" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score37" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score38" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score38" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score39" | |
| type: "InnerProduct" | |
| bottom: "fc7r" | |
| top: "score39" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape1" | |
| type: "Reshape" | |
| bottom: "score0" | |
| top: "Reshape1" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape2" | |
| type: "Reshape" | |
| bottom: "score1" | |
| top: "Reshape2" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape3" | |
| type: "Reshape" | |
| bottom: "score2" | |
| top: "Reshape3" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape4" | |
| type: "Reshape" | |
| bottom: "score3" | |
| top: "Reshape4" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape5" | |
| type: "Reshape" | |
| bottom: "score4" | |
| top: "Reshape5" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape6" | |
| type: "Reshape" | |
| bottom: "score5" | |
| top: "Reshape6" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape7" | |
| type: "Reshape" | |
| bottom: "score6" | |
| top: "Reshape7" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape8" | |
| type: "Reshape" | |
| bottom: "score7" | |
| top: "Reshape8" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape9" | |
| type: "Reshape" | |
| bottom: "score8" | |
| top: "Reshape9" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape10" | |
| type: "Reshape" | |
| bottom: "score9" | |
| top: "Reshape10" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape11" | |
| type: "Reshape" | |
| bottom: "score10" | |
| top: "Reshape11" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape12" | |
| type: "Reshape" | |
| bottom: "score11" | |
| top: "Reshape12" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape13" | |
| type: "Reshape" | |
| bottom: "score12" | |
| top: "Reshape13" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape14" | |
| type: "Reshape" | |
| bottom: "score13" | |
| top: "Reshape14" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape15" | |
| type: "Reshape" | |
| bottom: "score14" | |
| top: "Reshape15" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape16" | |
| type: "Reshape" | |
| bottom: "score15" | |
| top: "Reshape16" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape17" | |
| type: "Reshape" | |
| bottom: "score16" | |
| top: "Reshape17" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape18" | |
| type: "Reshape" | |
| bottom: "score17" | |
| top: "Reshape18" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape19" | |
| type: "Reshape" | |
| bottom: "score18" | |
| top: "Reshape19" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape20" | |
| type: "Reshape" | |
| bottom: "score19" | |
| top: "Reshape20" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape21" | |
| type: "Reshape" | |
| bottom: "score20" | |
| top: "Reshape21" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape22" | |
| type: "Reshape" | |
| bottom: "score21" | |
| top: "Reshape22" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape23" | |
| type: "Reshape" | |
| bottom: "score22" | |
| top: "Reshape23" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape24" | |
| type: "Reshape" | |
| bottom: "score23" | |
| top: "Reshape24" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape25" | |
| type: "Reshape" | |
| bottom: "score24" | |
| top: "Reshape25" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape26" | |
| type: "Reshape" | |
| bottom: "score25" | |
| top: "Reshape26" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape27" | |
| type: "Reshape" | |
| bottom: "score26" | |
| top: "Reshape27" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape28" | |
| type: "Reshape" | |
| bottom: "score27" | |
| top: "Reshape28" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape29" | |
| type: "Reshape" | |
| bottom: "score28" | |
| top: "Reshape29" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape30" | |
| type: "Reshape" | |
| bottom: "score29" | |
| top: "Reshape30" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape31" | |
| type: "Reshape" | |
| bottom: "score30" | |
| top: "Reshape31" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape32" | |
| type: "Reshape" | |
| bottom: "score31" | |
| top: "Reshape32" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape33" | |
| type: "Reshape" | |
| bottom: "score32" | |
| top: "Reshape33" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape34" | |
| type: "Reshape" | |
| bottom: "score33" | |
| top: "Reshape34" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape35" | |
| type: "Reshape" | |
| bottom: "score34" | |
| top: "Reshape35" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape36" | |
| type: "Reshape" | |
| bottom: "score35" | |
| top: "Reshape36" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape37" | |
| type: "Reshape" | |
| bottom: "score36" | |
| top: "Reshape37" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape38" | |
| type: "Reshape" | |
| bottom: "score37" | |
| top: "Reshape38" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape39" | |
| type: "Reshape" | |
| bottom: "score38" | |
| top: "Reshape39" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Reshape40" | |
| type: "Reshape" | |
| bottom: "score39" | |
| top: "Reshape40" | |
| reshape_param { | |
| shape { | |
| dim: 128 | |
| dim: 2 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "marginal" | |
| type: "Concat" | |
| bottom: "Reshape1" | |
| bottom: "Reshape2" | |
| bottom: "Reshape3" | |
| bottom: "Reshape4" | |
| bottom: "Reshape5" | |
| bottom: "Reshape6" | |
| bottom: "Reshape7" | |
| bottom: "Reshape8" | |
| bottom: "Reshape9" | |
| bottom: "Reshape10" | |
| bottom: "Reshape11" | |
| bottom: "Reshape12" | |
| bottom: "Reshape13" | |
| bottom: "Reshape14" | |
| bottom: "Reshape15" | |
| bottom: "Reshape16" | |
| bottom: "Reshape17" | |
| bottom: "Reshape18" | |
| bottom: "Reshape19" | |
| bottom: "Reshape20" | |
| bottom: "Reshape21" | |
| bottom: "Reshape22" | |
| bottom: "Reshape23" | |
| bottom: "Reshape24" | |
| bottom: "Reshape25" | |
| bottom: "Reshape26" | |
| bottom: "Reshape27" | |
| bottom: "Reshape28" | |
| bottom: "Reshape29" | |
| bottom: "Reshape30" | |
| bottom: "Reshape31" | |
| bottom: "Reshape32" | |
| bottom: "Reshape33" | |
| bottom: "Reshape34" | |
| bottom: "Reshape35" | |
| bottom: "Reshape36" | |
| bottom: "Reshape37" | |
| bottom: "Reshape38" | |
| bottom: "Reshape39" | |
| bottom: "Reshape40" | |
| top: "marginal" | |
| concat_param { | |
| axis: 2 | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "marginal" | |
| bottom: "labels" | |
| top: "loss" | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Hi,
I'm new to deep learning. I want to fine-tune a large number of layers. How should I do that? should I change the lr_mult in those layers? should I change them to some numbers larger that 1? What about decay_lr? why all of decay_lr are equal to zero? I just changed last layer's name and the number of outputs and also set the lr_mult to 10. I'm so confused can you lease help me?