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May 9, 2018 08:18
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xchen ResNet-18
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| name: "ResNet-18" | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| scale: 0.00390625 | |
| mean_file: "/u02/xchen/char/ImageData/char/gray/gray135mean.binaryproto" | |
| # crop_size: 128 | |
| } | |
| data_param { | |
| source: "/u02/xchen/char/ImageData/char/gray/lmdb135train-2/" | |
| batch_size: 256 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| scale: 0.00390625 | |
| mean_file: "/u02/xchen/char/ImageData/char/gray/gray135mean.binaryproto" | |
| # crop_size: 128 | |
| } | |
| data_param { | |
| source: "/u02/xchen/char/ImageData/char/gray/lmdb135val/" | |
| batch_size: 1 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| bottom: "data" | |
| top: "conv1" | |
| name: "conv1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 24 | |
| kernel_size: 5 | |
| pad: 2 | |
| stride: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "conv1_relu" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "pool1" | |
| name: "pool1" | |
| type: "Pooling" | |
| pooling_param { | |
| kernel_size: 3 | |
| stride: 3 | |
| pool: MAX | |
| } | |
| } | |
| ########################## | |
| ######first shortcut###### | |
| ########################## | |
| layer { | |
| bottom: "pool1" | |
| top: "res2a_branch1" | |
| name: "res2a_branch1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 48 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 1 | |
| bias_term: false | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "res2a_branch2a" | |
| name: "res2a_branch2a" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res2a_branch2a" | |
| top: "res2a_branch2b" | |
| name: "res2a_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 48 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| bias_term: false | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "res2a_branch1" | |
| bottom: "res2a_branch2b" | |
| top: "res2a" | |
| name: "res2a" | |
| type: "Eltwise" | |
| eltwise_param { | |
| operation: MAX | |
| } | |
| } | |
| ########################## | |
| ######second shortcut##### | |
| ########################## | |
| layer { | |
| bottom: "res2a" | |
| top: "res3a_branch1" | |
| name: "res3a_branch1" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| pad: 0 | |
| stride: 2 | |
| bias_term: false | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "res2a" | |
| top: "res3a_branch2a" | |
| name: "res3a_branch2a" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "res3a_branch2a" | |
| top: "res3a_branch2b" | |
| name: "res3a_branch2b" | |
| type: "Convolution" | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 2 | |
| bias_term: false | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "res3a_branch1" | |
| bottom: "res3a_branch2b" | |
| top: "res3a" | |
| name: "res3a" | |
| type: "Eltwise" | |
| eltwise_param { | |
| operation: MAX | |
| } | |
| } | |
| layer { | |
| bottom: "res3a" | |
| top: "pool5" | |
| name: "pool5" | |
| type: "Pooling" | |
| pooling_param { | |
| kernel_size: 3 | |
| stride: 3 | |
| pool: MAX | |
| } | |
| } | |
| layer { | |
| bottom: "pool5" | |
| top: "fc1" | |
| name: "fc1" | |
| type: "InnerProduct" | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer{ | |
| name: "slice6" | |
| type: "Slice" | |
| slice_param { | |
| slice_dim: 1 | |
| } | |
| bottom: "fc1" | |
| top: "slice1_1" | |
| top: "slice1_2" | |
| } | |
| layer{ | |
| name: "eltwise1" | |
| type: "Eltwise" | |
| bottom: "slice1_1" | |
| bottom: "slice1_2" | |
| top: "eltwise1" | |
| eltwise_param { | |
| operation: MAX | |
| } | |
| } | |
| layer{ | |
| name: "dropout1" | |
| type: "Dropout" | |
| bottom: "eltwise1" | |
| top: "dropout1" | |
| dropout_param { | |
| dropout_ratio: 0.7 | |
| } | |
| } | |
| layer { | |
| bottom: "dropout1" | |
| top: "fc7" | |
| name: "fc7" | |
| type: "InnerProduct" | |
| inner_product_param { | |
| num_output: 7906 | |
| } | |
| } | |
| #layer { | |
| # bottom: "fc7" | |
| # top: "prob" | |
| # name: "prob" | |
| # type: "Softmax" | |
| # } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc7" | |
| bottom: "label" | |
| top: "loss" | |
| } | |
| layer { | |
| name: "accuracy" | |
| type: "Accuracy" | |
| bottom: "fc7" | |
| bottom: "label" | |
| top: "accuracy" | |
| include { | |
| phase: TEST | |
| } | |
| } |
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