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Caffe代碼導讀（5）：對數據集進行Testing - 卜居 - 博客頻道 - CSDN.NET ?
http://blog.csdn.net/kkk584520/article/details/41694301
上一篇介紹了如何準備數據集，做好準備之后我們先看怎樣對訓練好的模型進行Testing。
先用手寫體識別例子，MNIST是數據集（包括訓練數據和測試數據），深度學習模型采用LeNet（具體介紹見http://yann.lecun.com/exdb/lenet/），由Yann LeCun教授提出。
如果你編譯好了Caffe，那么在CAFFE_ROOT下運行如下命令：
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$?./build/tools/caffe.bin?test?-model=examples/mnist/lenet_train_test.prototxt?-weights=examples/mnist/lenet_iter_10000.caffemodel?-gpu=0??
$ ./build/tools/caffe.bin test -model=examples/mnist/lenet_train_test.prototxt -weights=examples/mnist/lenet_iter_10000.caffemodel -gpu=0
就可以實現Testing。參數說明如下：
test：表示對訓練好的模型進行Testing，而不是training。其他參數包括train, time, device_query。
-model=XXX：指定模型prototxt文件，這是一個文本文件，詳細描述了網絡結構和數據集信息。我用的prototxt內容如下：
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name:?"LeNet"??layers?{????name:?"mnist"????type:?DATA????top:?"data"????top:?"label"????data_param?{??????source:?"examples/mnist/mnist_train_lmdb"??????backend:?LMDB??????batch_size:?64????}????transform_param?{??????scale:?0.00390625????}????include:?{?phase:?TRAIN?}??}??layers?{????name:?"mnist"????type:?DATA????top:?"data"????top:?"label"????data_param?{??????source:?"examples/mnist/mnist_test_lmdb"??????backend:?LMDB??????batch_size:?100????}????transform_param?{??????scale:?0.00390625????}????include:?{?phase:?TEST?}??}????layers?{????name:?"conv1"????type:?CONVOLUTION????bottom:?"data"????top:?"conv1"????blobs_lr:?1????blobs_lr:?2????convolution_param?{??????num_output:?20??????kernel_size:?5??????stride:?1??????weight_filler?{????????type:?"xavier"??????}??????bias_filler?{????????type:?"constant"??????}????}??}??layers?{????name:?"pool1"????type:?POOLING????bottom:?"conv1"????top:?"pool1"????pooling_param?{??????pool:?MAX??????kernel_size:?2??????stride:?2????}??}??layers?{????name:?"conv2"????type:?CONVOLUTION????bottom:?"pool1"????top:?"conv2"????blobs_lr:?1????blobs_lr:?2????convolution_param?{??????num_output:?50??????kernel_size:?5??????stride:?1??????weight_filler?{????????type:?"xavier"??????}??????bias_filler?{????????type:?"constant"??????}????}??}??layers?{????name:?"pool2"????type:?POOLING????bottom:?"conv2"????top:?"pool2"????pooling_param?{??????pool:?MAX??????kernel_size:?2??????stride:?2????}??}??layers?{????name:?"ip1"????type:?INNER_PRODUCT????bottom:?"pool2"????top:?"ip1"????blobs_lr:?1????blobs_lr:?2????inner_product_param?{??????num_output:?500??????weight_filler?{????????type:?"xavier"??????}??????bias_filler?{????????type:?"constant"??????}????}??}??layers?{????name:?"relu1"????type:?RELU????bottom:?"ip1"????top:?"ip1"??}??layers?{????name:?"ip2"????type:?INNER_PRODUCT????bottom:?"ip1"????top:?"ip2"????blobs_lr:?1????blobs_lr:?2????inner_product_param?{??????num_output:?10??????weight_filler?{????????type:?"xavier"??????}??????bias_filler?{????????type:?"constant"??????}????}??}??layers?{????name:?"accuracy"????type:?ACCURACY????bottom:?"ip2"????bottom:?"label"????top:?"accuracy"????include:?{?phase:?TEST?}??}??layers?{????name:?"loss"????type:?SOFTMAX_LOSS????bottom:?"ip2"????bottom:?"label"????top:?"loss"??}??
name: "LeNet"
layers {name: "mnist"type: DATAtop: "data"top: "label"data_param {source: "examples/mnist/mnist_train_lmdb"backend: LMDBbatch_size: 64}transform_param {scale: 0.00390625}include: { phase: TRAIN }
}
layers {name: "mnist"type: DATAtop: "data"top: "label"data_param {source: "examples/mnist/mnist_test_lmdb"backend: LMDBbatch_size: 100}transform_param {scale: 0.00390625}include: { phase: TEST }
}layers {name: "conv1"type: CONVOLUTIONbottom: "data"top: "conv1"blobs_lr: 1blobs_lr: 2convolution_param {num_output: 20kernel_size: 5stride: 1weight_filler {type: "xavier"}bias_filler {type: "constant"}}
}
layers {name: "pool1"type: POOLINGbottom: "conv1"top: "pool1"pooling_param {pool: MAXkernel_size: 2stride: 2}
}
layers {name: "conv2"type: CONVOLUTIONbottom: "pool1"top: "conv2"blobs_lr: 1blobs_lr: 2convolution_param {num_output: 50kernel_size: 5stride: 1weight_filler {type: "xavier"}bias_filler {type: "constant"}}
}
layers {name: "pool2"type: POOLINGbottom: "conv2"top: "pool2"pooling_param {pool: MAXkernel_size: 2stride: 2}
}
layers {name: "ip1"type: INNER_PRODUCTbottom: "pool2"top: "ip1"blobs_lr: 1blobs_lr: 2inner_product_param {num_output: 500weight_filler {type: "xavier"}bias_filler {type: "constant"}}
}
layers {name: "relu1"type: RELUbottom: "ip1"top: "ip1"
}
layers {name: "ip2"type: INNER_PRODUCTbottom: "ip1"top: "ip2"blobs_lr: 1blobs_lr: 2inner_product_param {num_output: 10weight_filler {type: "xavier"}bias_filler {type: "constant"}}
}
layers {name: "accuracy"type: ACCURACYbottom: "ip2"bottom: "label"top: "accuracy"include: { phase: TEST }
}
layers {name: "loss"type: SOFTMAX_LOSSbottom: "ip2"bottom: "label"top: "loss"
}
里面定義的網絡結構如下圖所示：
-weights=XXX：指定訓練好的caffemodel二進制文件。如果你手頭沒有訓練好的可以下載這個（http://download.csdn.net/detail/kkk584520/8219443）。
-gpu=0：指定在GPU上運行，GPUID=0。如果你沒有GPU就去掉這個參數，默認在CPU上運行。
運行輸出如下：
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I1203?18:47:00.073052??4610?caffe.cpp:134]?Use?GPU?with?device?ID?0??I1203?18:47:00.367065??4610?net.cpp:275]?The?NetState?phase?(1)?differed?from?the?phase?(0)?specified?by?a?rule?in?layer?mnist??I1203?18:47:00.367269??4610?net.cpp:39]?Initializing?net?from?parameters:???name:?"LeNet"??layers?{????top:?"data"????top:?"label"????name:?"mnist"????type:?DATA????data_param?{??????source:?"examples/mnist/mnist_test_lmdb"??????batch_size:?100??????backend:?LMDB????}????include?{??????phase:?TEST????}????transform_param?{??????scale:?0.00390625????}??}??layers?{????bottom:?"data"????top:?"conv1"????name:?"conv1"????type:?CONVOLUTION????blobs_lr:?1????blobs_lr:?2????convolution_param?{??????num_output:?20??????kernel_size:?5??????stride:?1??????weight_filler?{????????type:?"xavier"??????}??????bias_filler?{????????type:?"constant"??????}????}??}??layers?{????bottom:?"conv1"????top:?"pool1"????name:?"pool1"????type:?POOLING????pooling_param?{??????pool:?MAX??????kernel_size:?2??????stride:?2????}??}??layers?{????bottom:?"pool1"????top:?"conv2"????name:?"conv2"????type:?CONVOLUTION????blobs_lr:?1????blobs_lr:?2????convolution_param?{??????num_output:?50??????kernel_size:?5??????stride:?1??????weight_filler?{????????type:?"xavier"??????}??????bias_filler?{????????type:?"constant"??????}????}??}??layers?{????bottom:?"conv2"????top:?"pool2"????name:?"pool2"????type:?POOLING????pooling_param?{??????pool:?MAX??????kernel_size:?2??????stride:?2????}??}??layers?{????bottom:?"pool2"????top:?"ip1"????name:?"ip1"????type:?INNER_PRODUCT????blobs_lr:?1????blobs_lr:?2????inner_product_param?{??????num_output:?500??????weight_filler?{????????type:?"xavier"??????}??????bias_filler?{????????type:?"constant"??????}????}??}??layers?{????bottom:?"ip1"????top:?"ip1"????name:?"relu1"????type:?RELU??}??layers?{????bottom:?"ip1"????top:?"ip2"????name:?"ip2"????type:?INNER_PRODUCT????blobs_lr:?1????blobs_lr:?2????inner_product_param?{??????num_output:?10??????weight_filler?{????????type:?"xavier"??????}??????bias_filler?{????????type:?"constant"??????}????}??}??layers?{????bottom:?"ip2"????bottom:?"label"????top:?"accuracy"????name:?"accuracy"????type:?ACCURACY????include?{??????phase:?TEST????}??}??layers?{????bottom:?"ip2"????bottom:?"label"????top:?"loss"????name:?"loss"????type:?SOFTMAX_LOSS??}??I1203?18:47:00.367391??4610?net.cpp:67]?Creating?Layer?mnist??I1203?18:47:00.367409??4610?net.cpp:356]?mnist?->?data??I1203?18:47:00.367435??4610?net.cpp:356]?mnist?->?label??I1203?18:47:00.367451??4610?net.cpp:96]?Setting?up?mnist??I1203?18:47:00.367571??4610?data_layer.cpp:68]?Opening?lmdb?examples/mnist/mnist_test_lmdb??I1203?18:47:00.367609??4610?data_layer.cpp:128]?output?data?size:?100,1,28,28??I1203?18:47:00.367832??4610?net.cpp:103]?Top?shape:?100?1?28?28?(78400)??I1203?18:47:00.367849??4610?net.cpp:103]?Top?shape:?100?1?1?1?(100)??I1203?18:47:00.367863??4610?net.cpp:67]?Creating?Layer?label_mnist_1_split??I1203?18:47:00.367873??4610?net.cpp:394]?label_mnist_1_split?<-?label??I1203?18:47:00.367892??4610?net.cpp:356]?label_mnist_1_split?->?label_mnist_1_split_0??I1203?18:47:00.367908??4610?net.cpp:356]?label_mnist_1_split?->?label_mnist_1_split_1??I1203?18:47:00.367919??4610?net.cpp:96]?Setting?up?label_mnist_1_split??I1203?18:47:00.367929??4610?net.cpp:103]?Top?shape:?100?1?1?1?(100)??I1203?18:47:00.367938??4610?net.cpp:103]?Top?shape:?100?1?1?1?(100)??I1203?18:47:00.367950??4610?net.cpp:67]?Creating?Layer?conv1??I1203?18:47:00.367959??4610?net.cpp:394]?conv1?<-?data??I1203?18:47:00.367969??4610?net.cpp:356]?conv1?->?conv1??I1203?18:47:00.367982??4610?net.cpp:96]?Setting?up?conv1??I1203?18:47:00.392133??4610?net.cpp:103]?Top?shape:?100?20?24?24?(1152000)??I1203?18:47:00.392204??4610?net.cpp:67]?Creating?Layer?pool1??I1203?18:47:00.392217??4610?net.cpp:394]?pool1?<-?conv1??I1203?18:47:00.392231??4610?net.cpp:356]?pool1?->?pool1??I1203?18:47:00.392247??4610?net.cpp:96]?Setting?up?pool1??I1203?18:47:00.392273??4610?net.cpp:103]?Top?shape:?100?20?12?12?(288000)??I1203?18:47:00.392297??4610?net.cpp:67]?Creating?Layer?conv2??I1203?18:47:00.392307??4610?net.cpp:394]?conv2?<-?pool1??I1203?18:47:00.392318??4610?net.cpp:356]?conv2?->?conv2??I1203?18:47:00.392330??4610?net.cpp:96]?Setting?up?conv2??I1203?18:47:00.392669??4610?net.cpp:103]?Top?shape:?100?50?8?8?(320000)??I1203?18:47:00.392729??4610?net.cpp:67]?Creating?Layer?pool2??I1203?18:47:00.392756??4610?net.cpp:394]?pool2?<-?conv2??I1203?18:47:00.392768??4610?net.cpp:356]?pool2?->?pool2??I1203?18:47:00.392781??4610?net.cpp:96]?Setting?up?pool2??I1203?18:47:00.392793??4610?net.cpp:103]?Top?shape:?100?50?4?4?(80000)??I1203?18:47:00.392810??4610?net.cpp:67]?Creating?Layer?ip1??I1203?18:47:00.392819??4610?net.cpp:394]?ip1?<-?pool2??I1203?18:47:00.392832??4610?net.cpp:356]?ip1?->?ip1??I1203?18:47:00.392844??4610?net.cpp:96]?Setting?up?ip1??I1203?18:47:00.397348??4610?net.cpp:103]?Top?shape:?100?500?1?1?(50000)??I1203?18:47:00.397372??4610?net.cpp:67]?Creating?Layer?relu1??I1203?18:47:00.397382??4610?net.cpp:394]?relu1?<-?ip1??I1203?18:47:00.397394??4610?net.cpp:345]?relu1?->?ip1?(in-place)??I1203?18:47:00.397407??4610?net.cpp:96]?Setting?up?relu1??I1203?18:47:00.397420??4610?net.cpp:103]?Top?shape:?100?500?1?1?(50000)??I1203?18:47:00.397434??4610?net.cpp:67]?Creating?Layer?ip2??I1203?18:47:00.397442??4610?net.cpp:394]?ip2?<-?ip1??I1203?18:47:00.397456??4610?net.cpp:356]?ip2?->?ip2??I1203?18:47:00.397469??4610?net.cpp:96]?Setting?up?ip2??I1203?18:47:00.397532??4610?net.cpp:103]?Top?shape:?100?10?1?1?(1000)??I1203?18:47:00.397547??4610?net.cpp:67]?Creating?Layer?ip2_ip2_0_split??I1203?18:47:00.397557??4610?net.cpp:394]?ip2_ip2_0_split?<-?ip2??I1203?18:47:00.397565??4610?net.cpp:356]?ip2_ip2_0_split?->?ip2_ip2_0_split_0??I1203?18:47:00.397583??4610?net.cpp:356]?ip2_ip2_0_split?->?ip2_ip2_0_split_1??I1203?18:47:00.397593??4610?net.cpp:96]?Setting?up?ip2_ip2_0_split??I1203?18:47:00.397603??4610?net.cpp:103]?Top?shape:?100?10?1?1?(1000)??I1203?18:47:00.397611??4610?net.cpp:103]?Top?shape:?100?10?1?1?(1000)??I1203?18:47:00.397622??4610?net.cpp:67]?Creating?Layer?accuracy??I1203?18:47:00.397631??4610?net.cpp:394]?accuracy?<-?ip2_ip2_0_split_0??I1203?18:47:00.397640??4610?net.cpp:394]?accuracy?<-?label_mnist_1_split_0??I1203?18:47:00.397650??4610?net.cpp:356]?accuracy?->?accuracy??I1203?18:47:00.397661??4610?net.cpp:96]?Setting?up?accuracy??I1203?18:47:00.397673??4610?net.cpp:103]?Top?shape:?1?1?1?1?(1)??I1203?18:47:00.397687??4610?net.cpp:67]?Creating?Layer?loss??I1203?18:47:00.397696??4610?net.cpp:394]?loss?<-?ip2_ip2_0_split_1??I1203?18:47:00.397706??4610?net.cpp:394]?loss?<-?label_mnist_1_split_1??I1203?18:47:00.397714??4610?net.cpp:356]?loss?->?loss??I1203?18:47:00.397725??4610?net.cpp:96]?Setting?up?loss??I1203?18:47:00.397737??4610?net.cpp:103]?Top?shape:?1?1?1?1?(1)??I1203?18:47:00.397745??4610?net.cpp:109]?????with?loss?weight?1??I1203?18:47:00.397776??4610?net.cpp:170]?loss?needs?backward?computation.??I1203?18:47:00.397785??4610?net.cpp:172]?accuracy?does?not?need?backward?computation.??I1203?18:47:00.397794??4610?net.cpp:170]?ip2_ip2_0_split?needs?backward?computation.??I1203?18:47:00.397801??4610?net.cpp:170]?ip2?needs?backward?computation.??I1203?18:47:00.397809??4610?net.cpp:170]?relu1?needs?backward?computation.??I1203?18:47:00.397816??4610?net.cpp:170]?ip1?needs?backward?computation.??I1203?18:47:00.397825??4610?net.cpp:170]?pool2?needs?backward?computation.??I1203?18:47:00.397832??4610?net.cpp:170]?conv2?needs?backward?computation.??I1203?18:47:00.397843??4610?net.cpp:170]?pool1?needs?backward?computation.??I1203?18:47:00.397851??4610?net.cpp:170]?conv1?needs?backward?computation.??I1203?18:47:00.397860??4610?net.cpp:172]?label_mnist_1_split?does?not?need?backward?computation.??I1203?18:47:00.397867??4610?net.cpp:172]?mnist?does?not?need?backward?computation.??I1203?18:47:00.397874??4610?net.cpp:208]?This?network?produces?output?accuracy??I1203?18:47:00.397884??4610?net.cpp:208]?This?network?produces?output?loss??I1203?18:47:00.397905??4610?net.cpp:467]?Collecting?Learning?Rate?and?Weight?Decay.??I1203?18:47:00.397915??4610?net.cpp:219]?Network?initialization?done.??I1203?18:47:00.397923??4610?net.cpp:220]?Memory?required?for?data:?8086808??I1203?18:47:00.432165??4610?caffe.cpp:145]?Running?for?50?iterations.??I1203?18:47:00.435849??4610?caffe.cpp:169]?Batch?0,?accuracy?=?0.99??I1203?18:47:00.435879??4610?caffe.cpp:169]?Batch?0,?loss?=?0.018971??I1203?18:47:00.437434??4610?caffe.cpp:169]?Batch?1,?accuracy?=?0.99??I1203?18:47:00.437471??4610?caffe.cpp:169]?Batch?1,?loss?=?0.0117609??I1203?18:47:00.439000??4610?caffe.cpp:169]?Batch?2,?accuracy?=?1??I1203?18:47:00.439020??4610?caffe.cpp:169]?Batch?2,?loss?=?0.00555977??I1203?18:47:00.440551??4610?caffe.cpp:169]?Batch?3,?accuracy?=?0.99??I1203?18:47:00.440575??4610?caffe.cpp:169]?Batch?3,?loss?=?0.0412139??I1203?18:47:00.442105??4610?caffe.cpp:169]?Batch?4,?accuracy?=?0.99??I1203?18:47:00.442126??4610?caffe.cpp:169]?Batch?4,?loss?=?0.0579313??I1203?18:47:00.443619??4610?caffe.cpp:169]?Batch?5,?accuracy?=?0.99??I1203?18:47:00.443639??4610?caffe.cpp:169]?Batch?5,?loss?=?0.0479742??I1203?18:47:00.445159??4610?caffe.cpp:169]?Batch?6,?accuracy?=?0.98??I1203?18:47:00.445179??4610?caffe.cpp:169]?Batch?6,?loss?=?0.0570176??I1203?18:47:00.446712??4610?caffe.cpp:169]?Batch?7,?accuracy?=?0.99??I1203?18:47:00.446732??4610?caffe.cpp:169]?Batch?7,?loss?=?0.0272363??I1203?18:47:00.448249??4610?caffe.cpp:169]?Batch?8,?accuracy?=?1??I1203?18:47:00.448269??4610?caffe.cpp:169]?Batch?8,?loss?=?0.00680142??I1203?18:47:00.449801??4610?caffe.cpp:169]?Batch?9,?accuracy?=?0.98??I1203?18:47:00.449821??4610?caffe.cpp:169]?Batch?9,?loss?=?0.0288398??I1203?18:47:00.451352??4610?caffe.cpp:169]?Batch?10,?accuracy?=?0.98??I1203?18:47:00.451372??4610?caffe.cpp:169]?Batch?10,?loss?=?0.0603264??I1203?18:47:00.452883??4610?caffe.cpp:169]?Batch?11,?accuracy?=?0.98??I1203?18:47:00.452903??4610?caffe.cpp:169]?Batch?11,?loss?=?0.0524943??I1203?18:47:00.454407??4610?caffe.cpp:169]?Batch?12,?accuracy?=?0.95??I1203?18:47:00.454427??4610?caffe.cpp:169]?Batch?12,?loss?=?0.106648??I1203?18:47:00.455955??4610?caffe.cpp:169]?Batch?13,?accuracy?=?0.98??I1203?18:47:00.455976??4610?caffe.cpp:169]?Batch?13,?loss?=?0.0450225??I1203?18:47:00.457484??4610?caffe.cpp:169]?Batch?14,?accuracy?=?1??I1203?18:47:00.457504??4610?caffe.cpp:169]?Batch?14,?loss?=?0.00531614??I1203?18:47:00.459038??4610?caffe.cpp:169]?Batch?15,?accuracy?=?0.98??I1203?18:47:00.459056??4610?caffe.cpp:169]?Batch?15,?loss?=?0.065209??I1203?18:47:00.460577??4610?caffe.cpp:169]?Batch?16,?accuracy?=?0.98??I1203?18:47:00.460597??4610?caffe.cpp:169]?Batch?16,?loss?=?0.0520317??I1203?18:47:00.462123??4610?caffe.cpp:169]?Batch?17,?accuracy?=?0.99??I1203?18:47:00.462143??4610?caffe.cpp:169]?Batch?17,?loss?=?0.0328681??I1203?18:47:00.463656??4610?caffe.cpp:169]?Batch?18,?accuracy?=?0.99??I1203?18:47:00.463676??4610?caffe.cpp:169]?Batch?18,?loss?=?0.0175973??I1203?18:47:00.465188??4610?caffe.cpp:169]?Batch?19,?accuracy?=?0.97??I1203?18:47:00.465208??4610?caffe.cpp:169]?Batch?19,?loss?=?0.0576884??I1203?18:47:00.466749??4610?caffe.cpp:169]?Batch?20,?accuracy?=?0.97??I1203?18:47:00.466769??4610?caffe.cpp:169]?Batch?20,?loss?=?0.0850501??I1203?18:47:00.468278??4610?caffe.cpp:169]?Batch?21,?accuracy?=?0.98??I1203?18:47:00.468298??4610?caffe.cpp:169]?Batch?21,?loss?=?0.0676049??I1203?18:47:00.469805??4610?caffe.cpp:169]?Batch?22,?accuracy?=?0.99??I1203?18:47:00.469825??4610?caffe.cpp:169]?Batch?22,?loss?=?0.0448538??I1203?18:47:00.471328??4610?caffe.cpp:169]?Batch?23,?accuracy?=?0.97??I1203?18:47:00.471349??4610?caffe.cpp:169]?Batch?23,?loss?=?0.0333992??I1203?18:47:00.487124??4610?caffe.cpp:169]?Batch?24,?accuracy?=?1??I1203?18:47:00.487180??4610?caffe.cpp:169]?Batch?24,?loss?=?0.0281527??I1203?18:47:00.489002??4610?caffe.cpp:169]?Batch?25,?accuracy?=?0.99??I1203?18:47:00.489048??4610?caffe.cpp:169]?Batch?25,?loss?=?0.0545881??I1203?18:47:00.490890??4610?caffe.cpp:169]?Batch?26,?accuracy?=?0.98??I1203?18:47:00.490932??4610?caffe.cpp:169]?Batch?26,?loss?=?0.115576??I1203?18:47:00.492620??4610?caffe.cpp:169]?Batch?27,?accuracy?=?1??I1203?18:47:00.492640??4610?caffe.cpp:169]?Batch?27,?loss?=?0.0149555??I1203?18:47:00.494161??4610?caffe.cpp:169]?Batch?28,?accuracy?=?0.98??I1203?18:47:00.494181??4610?caffe.cpp:169]?Batch?28,?loss?=?0.0398991??I1203?18:47:00.495693??4610?caffe.cpp:169]?Batch?29,?accuracy?=?0.96??I1203?18:47:00.495713??4610?caffe.cpp:169]?Batch?29,?loss?=?0.115862??I1203?18:47:00.497226??4610?caffe.cpp:169]?Batch?30,?accuracy?=?1??I1203?18:47:00.497246??4610?caffe.cpp:169]?Batch?30,?loss?=?0.0116793??I1203?18:47:00.498785??4610?caffe.cpp:169]?Batch?31,?accuracy?=?1??I1203?18:47:00.498817??4610?caffe.cpp:169]?Batch?31,?loss?=?0.00451814??I1203?18:47:00.500329??4610?caffe.cpp:169]?Batch?32,?accuracy?=?0.98??I1203?18:47:00.500349??4610?caffe.cpp:169]?Batch?32,?loss?=?0.0244668??I1203?18:47:00.501878??4610?caffe.cpp:169]?Batch?33,?accuracy?=?1??I1203?18:47:00.501899??4610?caffe.cpp:169]?Batch?33,?loss?=?0.00285445??I1203?18:47:00.503411??4610?caffe.cpp:169]?Batch?34,?accuracy?=?0.98??I1203?18:47:00.503429??4610?caffe.cpp:169]?Batch?34,?loss?=?0.0566256??I1203?18:47:00.504940??4610?caffe.cpp:169]?Batch?35,?accuracy?=?0.95??I1203?18:47:00.504961??4610?caffe.cpp:169]?Batch?35,?loss?=?0.154924??I1203?18:47:00.506500??4610?caffe.cpp:169]?Batch?36,?accuracy?=?1??I1203?18:47:00.506520??4610?caffe.cpp:169]?Batch?36,?loss?=?0.00451233??I1203?18:47:00.508111??4610?caffe.cpp:169]?Batch?37,?accuracy?=?0.97??I1203?18:47:00.508131??4610?caffe.cpp:169]?Batch?37,?loss?=?0.0572309??I1203?18:47:00.509635??4610?caffe.cpp:169]?Batch?38,?accuracy?=?0.99??I1203?18:47:00.509655??4610?caffe.cpp:169]?Batch?38,?loss?=?0.0192229??I1203?18:47:00.511181??4610?caffe.cpp:169]?Batch?39,?accuracy?=?0.99??I1203?18:47:00.511200??4610?caffe.cpp:169]?Batch?39,?loss?=?0.029272??I1203?18:47:00.512725??4610?caffe.cpp:169]?Batch?40,?accuracy?=?0.99??I1203?18:47:00.512745??4610?caffe.cpp:169]?Batch?40,?loss?=?0.0258552??I1203?18:47:00.514317??4610?caffe.cpp:169]?Batch?41,?accuracy?=?0.99??I1203?18:47:00.514338??4610?caffe.cpp:169]?Batch?41,?loss?=?0.0752082??I1203?18:47:00.515854??4610?caffe.cpp:169]?Batch?42,?accuracy?=?1??I1203?18:47:00.515873??4610?caffe.cpp:169]?Batch?42,?loss?=?0.0283319??I1203?18:47:00.517379??4610?caffe.cpp:169]?Batch?43,?accuracy?=?0.99??I1203?18:47:00.517398??4610?caffe.cpp:169]?Batch?43,?loss?=?0.0112394??I1203?18:47:00.518925??4610?caffe.cpp:169]?Batch?44,?accuracy?=?0.98??I1203?18:47:00.518946??4610?caffe.cpp:169]?Batch?44,?loss?=?0.0413653??I1203?18:47:00.520457??4610?caffe.cpp:169]?Batch?45,?accuracy?=?0.98??I1203?18:47:00.520478??4610?caffe.cpp:169]?Batch?45,?loss?=?0.0501227??I1203?18:47:00.521989??4610?caffe.cpp:169]?Batch?46,?accuracy?=?1??I1203?18:47:00.522009??4610?caffe.cpp:169]?Batch?46,?loss?=?0.0114459??I1203?18:47:00.523540??4610?caffe.cpp:169]?Batch?47,?accuracy?=?1??I1203?18:47:00.523561??4610?caffe.cpp:169]?Batch?47,?loss?=?0.0163504??I1203?18:47:00.525075??4610?caffe.cpp:169]?Batch?48,?accuracy?=?0.97??I1203?18:47:00.525095??4610?caffe.cpp:169]?Batch?48,?loss?=?0.0450363??I1203?18:47:00.526633??4610?caffe.cpp:169]?Batch?49,?accuracy?=?1??I1203?18:47:00.526651??4610?caffe.cpp:169]?Batch?49,?loss?=?0.0046898??I1203?18:47:00.526662??4610?caffe.cpp:174]?Loss:?0.041468??I1203?18:47:00.526674??4610?caffe.cpp:186]?accuracy?=?0.9856??I1203?18:47:00.526687??4610?caffe.cpp:186]?loss?=?0.041468?(*?1?=?0.041468?loss)??
I1203 18:47:00.073052 4610 caffe.cpp:134] Use GPU with device ID 0
I1203 18:47:00.367065 4610 net.cpp:275] The NetState phase (1) differed from the phase (0) specified by a rule in layer mnist
I1203 18:47:00.367269 4610 net.cpp:39] Initializing net from parameters:
name: "LeNet"
layers {top: "data"top: "label"name: "mnist"type: DATAdata_param {source: "examples/mnist/mnist_test_lmdb"batch_size: 100backend: LMDB}include {phase: TEST}transform_param {scale: 0.00390625}
}
layers {bottom: "data"top: "conv1"name: "conv1"type: CONVOLUTIONblobs_lr: 1blobs_lr: 2convolution_param {num_output: 20kernel_size: 5stride: 1weight_filler {type: "xavier"}bias_filler {type: "constant"}}
}
layers {bottom: "conv1"top: "pool1"name: "pool1"type: POOLINGpooling_param {pool: MAXkernel_size: 2stride: 2}
}
layers {bottom: "pool1"top: "conv2"name: "conv2"type: CONVOLUTIONblobs_lr: 1blobs_lr: 2convolution_param {num_output: 50kernel_size: 5stride: 1weight_filler {type: "xavier"}bias_filler {type: "constant"}}
}
layers {bottom: "conv2"top: "pool2"name: "pool2"type: POOLINGpooling_param {pool: MAXkernel_size: 2stride: 2}
}
layers {bottom: "pool2"top: "ip1"name: "ip1"type: INNER_PRODUCTblobs_lr: 1blobs_lr: 2inner_product_param {num_output: 500weight_filler {type: "xavier"}bias_filler {type: "constant"}}
}
layers {bottom: "ip1"top: "ip1"name: "relu1"type: RELU
}
layers {bottom: "ip1"top: "ip2"name: "ip2"type: INNER_PRODUCTblobs_lr: 1blobs_lr: 2inner_product_param {num_output: 10weight_filler {type: "xavier"}bias_filler {type: "constant"}}
}
layers {bottom: "ip2"bottom: "label"top: "accuracy"name: "accuracy"type: ACCURACYinclude {phase: TEST}
}
layers {bottom: "ip2"bottom: "label"top: "loss"name: "loss"type: SOFTMAX_LOSS
}
I1203 18:47:00.367391 4610 net.cpp:67] Creating Layer mnist
I1203 18:47:00.367409 4610 net.cpp:356] mnist -> data
I1203 18:47:00.367435 4610 net.cpp:356] mnist -> label
I1203 18:47:00.367451 4610 net.cpp:96] Setting up mnist
I1203 18:47:00.367571 4610 data_layer.cpp:68] Opening lmdb examples/mnist/mnist_test_lmdb
I1203 18:47:00.367609 4610 data_layer.cpp:128] output data size: 100,1,28,28
I1203 18:47:00.367832 4610 net.cpp:103] Top shape: 100 1 28 28 (78400)
I1203 18:47:00.367849 4610 net.cpp:103] Top shape: 100 1 1 1 (100)
I1203 18:47:00.367863 4610 net.cpp:67] Creating Layer label_mnist_1_split
I1203 18:47:00.367873 4610 net.cpp:394] label_mnist_1_split <- label
I1203 18:47:00.367892 4610 net.cpp:356] label_mnist_1_split -> label_mnist_1_split_0
I1203 18:47:00.367908 4610 net.cpp:356] label_mnist_1_split -> label_mnist_1_split_1
I1203 18:47:00.367919 4610 net.cpp:96] Setting up label_mnist_1_split
I1203 18:47:00.367929 4610 net.cpp:103] Top shape: 100 1 1 1 (100)
I1203 18:47:00.367938 4610 net.cpp:103] Top shape: 100 1 1 1 (100)
I1203 18:47:00.367950 4610 net.cpp:67] Creating Layer conv1
I1203 18:47:00.367959 4610 net.cpp:394] conv1 <- data
I1203 18:47:00.367969 4610 net.cpp:356] conv1 -> conv1
I1203 18:47:00.367982 4610 net.cpp:96] Setting up conv1
I1203 18:47:00.392133 4610 net.cpp:103] Top shape: 100 20 24 24 (1152000)
I1203 18:47:00.392204 4610 net.cpp:67] Creating Layer pool1
I1203 18:47:00.392217 4610 net.cpp:394] pool1 <- conv1
I1203 18:47:00.392231 4610 net.cpp:356] pool1 -> pool1
I1203 18:47:00.392247 4610 net.cpp:96] Setting up pool1
I1203 18:47:00.392273 4610 net.cpp:103] Top shape: 100 20 12 12 (288000)
I1203 18:47:00.392297 4610 net.cpp:67] Creating Layer conv2
I1203 18:47:00.392307 4610 net.cpp:394] conv2 <- pool1
I1203 18:47:00.392318 4610 net.cpp:356] conv2 -> conv2
I1203 18:47:00.392330 4610 net.cpp:96] Setting up conv2
I1203 18:47:00.392669 4610 net.cpp:103] Top shape: 100 50 8 8 (320000)
I1203 18:47:00.392729 4610 net.cpp:67] Creating Layer pool2
I1203 18:47:00.392756 4610 net.cpp:394] pool2 <- conv2
I1203 18:47:00.392768 4610 net.cpp:356] pool2 -> pool2
I1203 18:47:00.392781 4610 net.cpp:96] Setting up pool2
I1203 18:47:00.392793 4610 net.cpp:103] Top shape: 100 50 4 4 (80000)
I1203 18:47:00.392810 4610 net.cpp:67] Creating Layer ip1
I1203 18:47:00.392819 4610 net.cpp:394] ip1 <- pool2
I1203 18:47:00.392832 4610 net.cpp:356] ip1 -> ip1
I1203 18:47:00.392844 4610 net.cpp:96] Setting up ip1
I1203 18:47:00.397348 4610 net.cpp:103] Top shape: 100 500 1 1 (50000)
I1203 18:47:00.397372 4610 net.cpp:67] Creating Layer relu1
I1203 18:47:00.397382 4610 net.cpp:394] relu1 <- ip1
I1203 18:47:00.397394 4610 net.cpp:345] relu1 -> ip1 (in-place)
I1203 18:47:00.397407 4610 net.cpp:96] Setting up relu1
I1203 18:47:00.397420 4610 net.cpp:103] Top shape: 100 500 1 1 (50000)
I1203 18:47:00.397434 4610 net.cpp:67] Creating Layer ip2
I1203 18:47:00.397442 4610 net.cpp:394] ip2 <- ip1
I1203 18:47:00.397456 4610 net.cpp:356] ip2 -> ip2
I1203 18:47:00.397469 4610 net.cpp:96] Setting up ip2
I1203 18:47:00.397532 4610 net.cpp:103] Top shape: 100 10 1 1 (1000)
I1203 18:47:00.397547 4610 net.cpp:67] Creating Layer ip2_ip2_0_split
I1203 18:47:00.397557 4610 net.cpp:394] ip2_ip2_0_split <- ip2
I1203 18:47:00.397565 4610 net.cpp:356] ip2_ip2_0_split -> ip2_ip2_0_split_0
I1203 18:47:00.397583 4610 net.cpp:356] ip2_ip2_0_split -> ip2_ip2_0_split_1
I1203 18:47:00.397593 4610 net.cpp:96] Setting up ip2_ip2_0_split
I1203 18:47:00.397603 4610 net.cpp:103] Top shape: 100 10 1 1 (1000)
I1203 18:47:00.397611 4610 net.cpp:103] Top shape: 100 10 1 1 (1000)
I1203 18:47:00.397622 4610 net.cpp:67] Creating Layer accuracy
I1203 18:47:00.397631 4610 net.cpp:394] accuracy <- ip2_ip2_0_split_0
I1203 18:47:00.397640 4610 net.cpp:394] accuracy <- label_mnist_1_split_0
I1203 18:47:00.397650 4610 net.cpp:356] accuracy -> accuracy
I1203 18:47:00.397661 4610 net.cpp:96] Setting up accuracy
I1203 18:47:00.397673 4610 net.cpp:103] Top shape: 1 1 1 1 (1)
I1203 18:47:00.397687 4610 net.cpp:67] Creating Layer loss
I1203 18:47:00.397696 4610 net.cpp:394] loss <- ip2_ip2_0_split_1
I1203 18:47:00.397706 4610 net.cpp:394] loss <- label_mnist_1_split_1
I1203 18:47:00.397714 4610 net.cpp:356] loss -> loss
I1203 18:47:00.397725 4610 net.cpp:96] Setting up loss
I1203 18:47:00.397737 4610 net.cpp:103] Top shape: 1 1 1 1 (1)
I1203 18:47:00.397745 4610 net.cpp:109] with loss weight 1
I1203 18:47:00.397776 4610 net.cpp:170] loss needs backward computation.
I1203 18:47:00.397785 4610 net.cpp:172] accuracy does not need backward computation.
I1203 18:47:00.397794 4610 net.cpp:170] ip2_ip2_0_split needs backward computation.
I1203 18:47:00.397801 4610 net.cpp:170] ip2 needs backward computation.
I1203 18:47:00.397809 4610 net.cpp:170] relu1 needs backward computation.
I1203 18:47:00.397816 4610 net.cpp:170] ip1 needs backward computation.
I1203 18:47:00.397825 4610 net.cpp:170] pool2 needs backward computation.
I1203 18:47:00.397832 4610 net.cpp:170] conv2 needs backward computation.
I1203 18:47:00.397843 4610 net.cpp:170] pool1 needs backward computation.
I1203 18:47:00.397851 4610 net.cpp:170] conv1 needs backward computation.
I1203 18:47:00.397860 4610 net.cpp:172] label_mnist_1_split does not need backward computation.
I1203 18:47:00.397867 4610 net.cpp:172] mnist does not need backward computation.
I1203 18:47:00.397874 4610 net.cpp:208] This network produces output accuracy
I1203 18:47:00.397884 4610 net.cpp:208] This network produces output loss
I1203 18:47:00.397905 4610 net.cpp:467] Collecting Learning Rate and Weight Decay.
I1203 18:47:00.397915 4610 net.cpp:219] Network initialization done.
I1203 18:47:00.397923 4610 net.cpp:220] Memory required for data: 8086808
I1203 18:47:00.432165 4610 caffe.cpp:145] Running for 50 iterations.
I1203 18:47:00.435849 4610 caffe.cpp:169] Batch 0, accuracy = 0.99
I1203 18:47:00.435879 4610 caffe.cpp:169] Batch 0, loss = 0.018971
I1203 18:47:00.437434 4610 caffe.cpp:169] Batch 1, accuracy = 0.99
I1203 18:47:00.437471 4610 caffe.cpp:169] Batch 1, loss = 0.0117609
I1203 18:47:00.439000 4610 caffe.cpp:169] Batch 2, accuracy = 1
I1203 18:47:00.439020 4610 caffe.cpp:169] Batch 2, loss = 0.00555977
I1203 18:47:00.440551 4610 caffe.cpp:169] Batch 3, accuracy = 0.99
I1203 18:47:00.440575 4610 caffe.cpp:169] Batch 3, loss = 0.0412139
I1203 18:47:00.442105 4610 caffe.cpp:169] Batch 4, accuracy = 0.99
I1203 18:47:00.442126 4610 caffe.cpp:169] Batch 4, loss = 0.0579313
I1203 18:47:00.443619 4610 caffe.cpp:169] Batch 5, accuracy = 0.99
I1203 18:47:00.443639 4610 caffe.cpp:169] Batch 5, loss = 0.0479742
I1203 18:47:00.445159 4610 caffe.cpp:169] Batch 6, accuracy = 0.98
I1203 18:47:00.445179 4610 caffe.cpp:169] Batch 6, loss = 0.0570176
I1203 18:47:00.446712 4610 caffe.cpp:169] Batch 7, accuracy = 0.99
I1203 18:47:00.446732 4610 caffe.cpp:169] Batch 7, loss = 0.0272363
I1203 18:47:00.448249 4610 caffe.cpp:169] Batch 8, accuracy = 1
I1203 18:47:00.448269 4610 caffe.cpp:169] Batch 8, loss = 0.00680142
I1203 18:47:00.449801 4610 caffe.cpp:169] Batch 9, accuracy = 0.98
I1203 18:47:00.449821 4610 caffe.cpp:169] Batch 9, loss = 0.0288398
I1203 18:47:00.451352 4610 caffe.cpp:169] Batch 10, accuracy = 0.98
I1203 18:47:00.451372 4610 caffe.cpp:169] Batch 10, loss = 0.0603264
I1203 18:47:00.452883 4610 caffe.cpp:169] Batch 11, accuracy = 0.98
I1203 18:47:00.452903 4610 caffe.cpp:169] Batch 11, loss = 0.0524943
I1203 18:47:00.454407 4610 caffe.cpp:169] Batch 12, accuracy = 0.95
I1203 18:47:00.454427 4610 caffe.cpp:169] Batch 12, loss = 0.106648
I1203 18:47:00.455955 4610 caffe.cpp:169] Batch 13, accuracy = 0.98
I1203 18:47:00.455976 4610 caffe.cpp:169] Batch 13, loss = 0.0450225
I1203 18:47:00.457484 4610 caffe.cpp:169] Batch 14, accuracy = 1
I1203 18:47:00.457504 4610 caffe.cpp:169] Batch 14, loss = 0.00531614
I1203 18:47:00.459038 4610 caffe.cpp:169] Batch 15, accuracy = 0.98
I1203 18:47:00.459056 4610 caffe.cpp:169] Batch 15, loss = 0.065209
I1203 18:47:00.460577 4610 caffe.cpp:169] Batch 16, accuracy = 0.98
I1203 18:47:00.460597 4610 caffe.cpp:169] Batch 16, loss = 0.0520317
I1203 18:47:00.462123 4610 caffe.cpp:169] Batch 17, accuracy = 0.99
I1203 18:47:00.462143 4610 caffe.cpp:169] Batch 17, loss = 0.0328681
I1203 18:47:00.463656 4610 caffe.cpp:169] Batch 18, accuracy = 0.99
I1203 18:47:00.463676 4610 caffe.cpp:169] Batch 18, loss = 0.0175973
I1203 18:47:00.465188 4610 caffe.cpp:169] Batch 19, accuracy = 0.97
I1203 18:47:00.465208 4610 caffe.cpp:169] Batch 19, loss = 0.0576884
I1203 18:47:00.466749 4610 caffe.cpp:169] Batch 20, accuracy = 0.97
I1203 18:47:00.466769 4610 caffe.cpp:169] Batch 20, loss = 0.0850501
I1203 18:47:00.468278 4610 caffe.cpp:169] Batch 21, accuracy = 0.98
I1203 18:47:00.468298 4610 caffe.cpp:169] Batch 21, loss = 0.0676049
I1203 18:47:00.469805 4610 caffe.cpp:169] Batch 22, accuracy = 0.99
I1203 18:47:00.469825 4610 caffe.cpp:169] Batch 22, loss = 0.0448538
I1203 18:47:00.471328 4610 caffe.cpp:169] Batch 23, accuracy = 0.97
I1203 18:47:00.471349 4610 caffe.cpp:169] Batch 23, loss = 0.0333992
I1203 18:47:00.487124 4610 caffe.cpp:169] Batch 24, accuracy = 1
I1203 18:47:00.487180 4610 caffe.cpp:169] Batch 24, loss = 0.0281527
I1203 18:47:00.489002 4610 caffe.cpp:169] Batch 25, accuracy = 0.99
I1203 18:47:00.489048 4610 caffe.cpp:169] Batch 25, loss = 0.0545881
I1203 18:47:00.490890 4610 caffe.cpp:169] Batch 26, accuracy = 0.98
I1203 18:47:00.490932 4610 caffe.cpp:169] Batch 26, loss = 0.115576
I1203 18:47:00.492620 4610 caffe.cpp:169] Batch 27, accuracy = 1
I1203 18:47:00.492640 4610 caffe.cpp:169] Batch 27, loss = 0.0149555
I1203 18:47:00.494161 4610 caffe.cpp:169] Batch 28, accuracy = 0.98
I1203 18:47:00.494181 4610 caffe.cpp:169] Batch 28, loss = 0.0398991
I1203 18:47:00.495693 4610 caffe.cpp:169] Batch 29, accuracy = 0.96
I1203 18:47:00.495713 4610 caffe.cpp:169] Batch 29, loss = 0.115862
I1203 18:47:00.497226 4610 caffe.cpp:169] Batch 30, accuracy = 1
I1203 18:47:00.497246 4610 caffe.cpp:169] Batch 30, loss = 0.0116793
I1203 18:47:00.498785 4610 caffe.cpp:169] Batch 31, accuracy = 1
I1203 18:47:00.498817 4610 caffe.cpp:169] Batch 31, loss = 0.00451814
I1203 18:47:00.500329 4610 caffe.cpp:169] Batch 32, accuracy = 0.98
I1203 18:47:00.500349 4610 caffe.cpp:169] Batch 32, loss = 0.0244668
I1203 18:47:00.501878 4610 caffe.cpp:169] Batch 33, accuracy = 1
I1203 18:47:00.501899 4610 caffe.cpp:169] Batch 33, loss = 0.00285445
I1203 18:47:00.503411 4610 caffe.cpp:169] Batch 34, accuracy = 0.98
I1203 18:47:00.503429 4610 caffe.cpp:169] Batch 34, loss = 0.0566256
I1203 18:47:00.504940 4610 caffe.cpp:169] Batch 35, accuracy = 0.95
I1203 18:47:00.504961 4610 caffe.cpp:169] Batch 35, loss = 0.154924
I1203 18:47:00.506500 4610 caffe.cpp:169] Batch 36, accuracy = 1
I1203 18:47:00.506520 4610 caffe.cpp:169] Batch 36, loss = 0.00451233
I1203 18:47:00.508111 4610 caffe.cpp:169] Batch 37, accuracy = 0.97
I1203 18:47:00.508131 4610 caffe.cpp:169] Batch 37, loss = 0.0572309
I1203 18:47:00.509635 4610 caffe.cpp:169] Batch 38, accuracy = 0.99
I1203 18:47:00.509655 4610 caffe.cpp:169] Batch 38, loss = 0.0192229
I1203 18:47:00.511181 4610 caffe.cpp:169] Batch 39, accuracy = 0.99
I1203 18:47:00.511200 4610 caffe.cpp:169] Batch 39, loss = 0.029272
I1203 18:47:00.512725 4610 caffe.cpp:169] Batch 40, accuracy = 0.99
I1203 18:47:00.512745 4610 caffe.cpp:169] Batch 40, loss = 0.0258552
I1203 18:47:00.514317 4610 caffe.cpp:169] Batch 41, accuracy = 0.99
I1203 18:47:00.514338 4610 caffe.cpp:169] Batch 41, loss = 0.0752082
I1203 18:47:00.515854 4610 caffe.cpp:169] Batch 42, accuracy = 1
I1203 18:47:00.515873 4610 caffe.cpp:169] Batch 42, loss = 0.0283319
I1203 18:47:00.517379 4610 caffe.cpp:169] Batch 43, accuracy = 0.99
I1203 18:47:00.517398 4610 caffe.cpp:169] Batch 43, loss = 0.0112394
I1203 18:47:00.518925 4610 caffe.cpp:169] Batch 44, accuracy = 0.98
I1203 18:47:00.518946 4610 caffe.cpp:169] Batch 44, loss = 0.0413653
I1203 18:47:00.520457 4610 caffe.cpp:169] Batch 45, accuracy = 0.98
I1203 18:47:00.520478 4610 caffe.cpp:169] Batch 45, loss = 0.0501227
I1203 18:47:00.521989 4610 caffe.cpp:169] Batch 46, accuracy = 1
I1203 18:47:00.522009 4610 caffe.cpp:169] Batch 46, loss = 0.0114459
I1203 18:47:00.523540 4610 caffe.cpp:169] Batch 47, accuracy = 1
I1203 18:47:00.523561 4610 caffe.cpp:169] Batch 47, loss = 0.0163504
I1203 18:47:00.525075 4610 caffe.cpp:169] Batch 48, accuracy = 0.97
I1203 18:47:00.525095 4610 caffe.cpp:169] Batch 48, loss = 0.0450363
I1203 18:47:00.526633 4610 caffe.cpp:169] Batch 49, accuracy = 1
I1203 18:47:00.526651 4610 caffe.cpp:169] Batch 49, loss = 0.0046898
I1203 18:47:00.526662 4610 caffe.cpp:174] Loss: 0.041468
I1203 18:47:00.526674 4610 caffe.cpp:186] accuracy = 0.9856
I1203 18:47:00.526687 4610 caffe.cpp:186] loss = 0.041468 (* 1 = 0.041468 loss)
總結
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