VGG-16 prototxt
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VGG-16 prototxt
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net:?"models/vgg16/train_val.prototxt"?? test_iter:?1000?? test_interval:?2500?? base_lr:?0.001?? lr_policy:?"step"?? gamma:?0.1?? stepsize:?50000?? display:?20?? max_iter:?200000?? momentum:?0.9?? weight_decay:?0.0005?? snapshot:?10000?? snapshot_prefix:?"models/vgg16/caffe_vgg16_train"?? solver_mode:?GPU?? net: "models/vgg16/train_val.prototxt"
test_iter: 1000
test_interval: 2500
base_lr: 0.001
lr_policy: "step"
gamma: 0.1
stepsize: 50000
display: 20
max_iter: 200000
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
snapshot_prefix: "models/vgg16/caffe_vgg16_train"
solver_mode: GPUtrain_val.prototxt: [cpp] view plaincopyprint? name:?"VGG16"?? layer?{?? ??name:?"data"?? ??type:?"Data"?? ??top:?"data"?? ??top:?"label"?? ??include?{?? ????phase:?TRAIN?? ??}?? ??#?transform_param?{?? ??#???mirror:?true?? ??#???crop_size:?224?? ??#???mean_file:?"data/ilsvrc12_shrt_256/imagenet_mean.binaryproto"?? ??#?}?? ??transform_param?{?? ????mirror:?true?? ????crop_size:?224?? ????mean_value:?103.939?? ????mean_value:?116.779?? ????mean_value:?123.68?? ??}?? ??data_param?{?? ????source:?"data/ilsvrc12_shrt_256/ilsvrc12_train_leveldb"?? ????batch_size:?64?? ????backend:?LEVELDB?? ??}?? }?? layer?{?? ??name:?"data"?? ??type:?"Data"?? ??top:?"data"?? ??top:?"label"?? ??include?{?? ????phase:?TEST?? ??}?? ??#?transform_param?{?? ??#???mirror:?false?? ??#???crop_size:?224?? ??#???mean_file:?"data/ilsvrc12_shrt_256/imagenet_mean.binaryproto"?? ??#?}?? ??transform_param?{?? ????mirror:?false?? ????crop_size:?224?? ????mean_value:?103.939?? ????mean_value:?116.779?? ????mean_value:?123.68?? ??}?? ??data_param?{?? ????source:?"data/ilsvrc12_shrt_256/ilsvrc12_val_leveldb"?? ????batch_size:?50?? ????backend:?LEVELDB?? ??}?? }?? layer?{?? ??bottom:?"data"?? ??top:?"conv1_1"?? ??name:?"conv1_1"?? ??type:?"Convolution"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?64?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?64?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?128?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?128?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?256?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?256?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?256?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?512?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?512?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?512?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?512?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?512?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??convolution_param?{?? ????num_output:?512?? ????pad:?1?? ????kernel_size:?3?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.01?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??inner_product_param?{?? ????num_output:?4096?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.005?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?0.1?? ????}?? ??}?? }?? 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"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??inner_product_param?{?? ????num_output:?4096?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.005?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?0.1?? ????}?? ??}?? }?? 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?{?? ??bottom:?"fc7"?? ??top:?"fc8"?? ??name:?"fc8"?? ??type:?"InnerProduct"?? ??param?{?? ????lr_mult:?1?? ????decay_mult:?1?? ??}?? ??param?{?? ????lr_mult:?2?? ????decay_mult:?0?? ??}?? ??inner_product_param?{?? ????num_output:?1000?? ????weight_filler?{?? ??????type:?"gaussian"?? ??????std:?0.005?? ????}?? ????bias_filler?{?? ??????type:?"constant"?? ??????value:?0.1?? ????}?? ??}?? }?? layer?{?? ??name:?"accuracy_at_1"?? ??type:?"Accuracy"?? ??bottom:?"fc8"?? ??bottom:?"label"?? ??top:?"accuracy_at_1"?? ??accuracy_param?{?? ????top_k:?1?? ??}?? ??include?{?? ????phase:?TEST?? ??}?? }?? layer?{?? ??name:?"accuracy_at_5"?? ??type:?"Accuracy"?? ??bottom:?"fc8"?? ??bottom:?"label"?? ??top:?"accuracy_at_5"?? ??accuracy_param?{?? ????top_k:?5?? ??}?? ??include?{?? ????phase:?TEST?? ??}?? }?? layer?{?? ??bottom:?"fc8"?? ??bottom:?"label"?? ??top:?"loss"?? ??name:?"loss"?? ??type:?"SoftmaxWithLoss"?? }??
VGG-16 prototxt
標(biāo)簽： CNNcaffe 2016-10-02 14:18 961人閱讀 評論(6) 收藏 舉報 本文章已收錄于： 分類： caffe（15） 作者同類文章X版權(quán)聲明：可以參考 亦可轉(zhuǎn)載 請注明出處
solver.prototxt:
[cpp] view plaincopyprint?總結(jié)
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