【画像認識】「Caffe」をWindowsで使う!MNIST実践編
はじめに
前回はWindows用に環境構築した「Caffe」をMNISTで試そうということで、
MNISTのデータの準備について書きました。(以下参照)
準備は完了したので、今回は実際に動かしてみます。
実行方法
動かす実行ファイルは前々回にビルドして作成した「caffe.exe」を使用します。
※以降、カレントディレクトリは「caffe.exe」が格納されたフォルダとします。
まず、カレントディレクトリに
caffe-masterフォルダ直下のexamplesフォルダをコピーします。
続いて、前回作成した、以下のフォルダを「カレントディレクトリ\examples\mnist」直下にコピーします。
- mnist_test_lmdb
- mnist_train_lmdb
そして、
コマンドプロンプトを立ち上げて、
カレントディレクトリに移動し、以下を実行すると学習と(恐らく)評価も処理されます。
caffe.exe train --solver=examples\mnist\lenet_solver.prototxt
実行した結果は以下のようになります。
#出力ログを全部載せてみました。
テストした結果(精度)は99.11%となりました。
精度は出力ログの下から4行目に書かれています。
Test net output #0: accuracy = 0.9911
E:\99_tmp\caffe>caffe.exe train --solver=examples\mnist\lenet_solver.prototxt I0609 21:58:55.126268 2348 caffe.cpp:186] Using GPUs 0 I0609 21:58:55.404929 2348 caffe.cpp:191] GPU 0: GeForce GTX 745 I0609 21:58:55.596211 2348 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead. I0609 21:58:55.597213 2348 solver.cpp:48] Initializing solver from parameters: test_iter: 100 test_interval: 500 base_lr: 0.01 display: 100 max_iter: 10000 lr_policy: "inv" gamma: 0.0001 power: 0.75 momentum: 0.9 weight_decay: 0.0005 snapshot: 5000 snapshot_prefix: "examples/mnist/lenet" solver_mode: GPU device_id: 0 net: "examples/mnist/lenet_train_test.prototxt" I0609 21:58:55.601205 2348 solver.cpp:91] Creating training net from net file: examples/mnist/lenet_train_test.prototxt I0609 21:58:55.602207 2348 net.cpp:313] The NetState phase (0) differed from the phase (1) specified by a rule in layer mnist I0609 21:58:55.603210 2348 net.cpp:313] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy I0609 21:58:55.604213 2348 net.cpp:49] Initializing net from parameters: name: "LeNet" state { phase: TRAIN } layer { name: "mnist" type: "Data" top: "data" top: "label" include { phase: TRAIN } transform_param { scale: 0.00390625 } data_param { source: "examples/mnist/data/mnist_train_lmdb" batch_size: 64 backend: LMDB } } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 20 kernel_size: 5 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 50 kernel_size: 5 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "ip1" type: "InnerProduct" bottom: "pool2" top: "ip1" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 500 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "relu1" type: "ReLU" bottom: "ip1" top: "ip1" } layer { name: "ip2" type: "InnerProduct" bottom: "ip1" top: "ip2" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 10 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "loss" type: "SoftmaxWithLoss" bottom: "ip2" bottom: "label" top: "loss" } I0609 21:58:55.616746 2348 layer_factory.hpp:77] Creating layer mnist I0609 21:58:55.617748 2348 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead. I0609 21:58:55.619688 2348 net.cpp:91] Creating Layer mnist I0609 21:58:55.620190 2348 net.cpp:399] mnist -> data I0609 21:58:55.621192 2348 net.cpp:399] mnist -> label I0609 21:58:55.620694 5676 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead. I0609 21:58:55.623699 5676 db_lmdb.cpp:40] Opened lmdb examples/mnist/data/mnist_train_lmdb I0609 21:58:58.823786 2348 data_layer.cpp:41] output data size: 64,1,28,28 I0609 21:58:58.826282 2348 net.cpp:141] Setting up mnist I0609 21:58:58.827285 2348 net.cpp:148] Top shape: 64 1 28 28 (50176) I0609 21:58:58.828287 2348 net.cpp:148] Top shape: 64 (64) I0609 21:58:58.827285 13328 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead. I0609 21:58:58.828789 2348 net.cpp:156] Memory required for data: 200960 I0609 21:58:58.831279 2348 layer_factory.hpp:77] Creating layer conv1 I0609 21:58:58.832279 2348 net.cpp:91] Creating Layer conv1 I0609 21:58:58.833281 2348 net.cpp:425] conv1 <- data I0609 21:58:58.833783 2348 net.cpp:399] conv1 -> conv1 I0609 21:58:59.551329 2348 net.cpp:141] Setting up conv1 I0609 21:58:59.551831 2348 net.cpp:148] Top shape: 64 20 24 24 (737280) I0609 21:58:59.552834 2348 net.cpp:156] Memory required for data: 3150080 I0609 21:58:59.553334 2348 layer_factory.hpp:77] Creating layer pool1 I0609 21:58:59.554337 2348 net.cpp:91] Creating Layer pool1 I0609 21:58:59.554839 2348 net.cpp:425] pool1 <- conv1 I0609 21:58:59.555344 2348 net.cpp:399] pool1 -> pool1 I0609 21:58:59.555841 2348 net.cpp:141] Setting up pool1 I0609 21:58:59.556844 2348 net.cpp:148] Top shape: 64 20 12 12 (184320) I0609 21:58:59.557345 2348 net.cpp:156] Memory required for data: 3887360 I0609 21:58:59.558348 2348 layer_factory.hpp:77] Creating layer conv2 I0609 21:58:59.558850 2348 net.cpp:91] Creating Layer conv2 I0609 21:58:59.559833 2348 net.cpp:425] conv2 <- pool1 I0609 21:58:59.560335 2348 net.cpp:399] conv2 -> conv2 I0609 21:58:59.562841 2348 net.cpp:141] Setting up conv2 I0609 21:58:59.563344 2348 net.cpp:148] Top shape: 64 50 8 8 (204800) I0609 21:58:59.564345 2348 net.cpp:156] Memory required for data: 4706560 I0609 21:58:59.564847 2348 layer_factory.hpp:77] Creating layer pool2 I0609 21:58:59.565850 2348 net.cpp:91] Creating Layer pool2 I0609 21:58:59.566351 2348 net.cpp:425] pool2 <- conv2 I0609 21:58:59.566853 2348 net.cpp:399] pool2 -> pool2 I0609 21:58:59.567862 2348 net.cpp:141] Setting up pool2 I0609 21:58:59.568356 2348 net.cpp:148] Top shape: 64 50 4 4 (51200) I0609 21:58:59.568356 2348 net.cpp:156] Memory required for data: 4911360 I0609 21:58:59.569713 2348 layer_factory.hpp:77] Creating layer ip1 I0609 21:58:59.570718 2348 net.cpp:91] Creating Layer ip1 I0609 21:58:59.571219 2348 net.cpp:425] ip1 <- pool2 I0609 21:58:59.571720 2348 net.cpp:399] ip1 -> ip1 I0609 21:58:59.575229 2348 net.cpp:141] Setting up ip1 I0609 21:58:59.575731 2348 net.cpp:148] Top shape: 64 500 (32000) I0609 21:58:59.576232 2348 net.cpp:156] Memory required for data: 5039360 I0609 21:58:59.576733 2348 layer_factory.hpp:77] Creating layer relu1 I0609 21:58:59.577736 2348 net.cpp:91] Creating Layer relu1 I0609 21:58:59.578238 2348 net.cpp:425] relu1 <- ip1 I0609 21:58:59.579241 2348 net.cpp:386] relu1 -> ip1 (in-place) I0609 21:58:59.580744 2348 net.cpp:141] Setting up relu1 I0609 21:58:59.580744 2348 net.cpp:148] Top shape: 64 500 (32000) I0609 21:58:59.581746 2348 net.cpp:156] Memory required for data: 5167360 I0609 21:58:59.582752 2348 layer_factory.hpp:77] Creating layer ip2 I0609 21:58:59.583250 2348 net.cpp:91] Creating Layer ip2 I0609 21:58:59.583752 2348 net.cpp:425] ip2 <- ip1 I0609 21:58:59.584264 2348 net.cpp:399] ip2 -> ip2 I0609 21:58:59.585757 2348 net.cpp:141] Setting up ip2 I0609 21:58:59.586258 2348 net.cpp:148] Top shape: 64 10 (640) I0609 21:58:59.586771 2348 net.cpp:156] Memory required for data: 5169920 I0609 21:58:59.587764 2348 layer_factory.hpp:77] Creating layer loss I0609 21:58:59.588264 2348 net.cpp:91] Creating Layer loss I0609 21:58:59.589267 2348 net.cpp:425] loss <- ip2 I0609 21:58:59.589768 2348 net.cpp:425] loss <- label I0609 21:58:59.590270 2348 net.cpp:399] loss -> loss I0609 21:58:59.590771 2348 layer_factory.hpp:77] Creating layer loss I0609 21:58:59.591773 2348 net.cpp:141] Setting up loss I0609 21:58:59.592274 2348 net.cpp:148] Top shape: (1) I0609 21:58:59.593277 2348 net.cpp:151] with loss weight 1 I0609 21:58:59.593780 2348 net.cpp:156] Memory required for data: 5169924 I0609 21:58:59.594781 2348 net.cpp:217] loss needs backward computation. I0609 21:58:59.595283 2348 net.cpp:217] ip2 needs backward computation. I0609 21:58:59.595788 2348 net.cpp:217] relu1 needs backward computation. I0609 21:58:59.596787 2348 net.cpp:217] ip1 needs backward computation. I0609 21:58:59.597288 2348 net.cpp:217] pool2 needs backward computation. I0609 21:58:59.598290 2348 net.cpp:217] conv2 needs backward computation. I0609 21:58:59.598290 2348 net.cpp:217] pool1 needs backward computation. I0609 21:58:59.600255 2348 net.cpp:217] conv1 needs backward computation. I0609 21:58:59.600757 2348 net.cpp:219] mnist does not need backward computation. I0609 21:58:59.601759 2348 net.cpp:261] This network produces output loss I0609 21:58:59.602260 2348 net.cpp:274] Network initialization done. I0609 21:58:59.603263 2348 solver.cpp:181] Creating test net (#0) specified by net file: examples/mnist/lenet_train_test.prototxt I0609 21:58:59.604266 2348 net.cpp:313] The NetState phase (1) differed from the phase (0) specified by a rule in layer mnist I0609 21:58:59.605268 2348 net.cpp:49] Initializing net from parameters: name: "LeNet" state { phase: TEST } layer { name: "mnist" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { scale: 0.00390625 } data_param { source: "examples/mnist/data/mnist_test_lmdb" batch_size: 100 backend: LMDB } } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 20 kernel_size: 5 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 50 kernel_size: 5 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "ip1" type: "InnerProduct" bottom: "pool2" top: "ip1" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 500 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "relu1" type: "ReLU" bottom: "ip1" top: "ip1" } layer { name: "ip2" type: "InnerProduct" bottom: "ip1" top: "ip2" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 10 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "accuracy" type: "Accuracy" bottom: "ip2" bottom: "label" top: "accuracy" include { phase: TEST } } layer { name: "loss" type: "SoftmaxWithLoss" bottom: "ip2" bottom: "label" top: "loss" } I0609 21:58:59.617784 2348 layer_factory.hpp:77] Creating layer mnist I0609 21:58:59.618788 2348 net.cpp:91] Creating Layer mnist I0609 21:58:59.621775 2348 net.cpp:399] mnist -> data I0609 21:58:59.622778 2348 net.cpp:399] mnist -> label I0609 21:58:59.622287 12064 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead. I0609 21:58:59.624783 12064 db_lmdb.cpp:40] Opened lmdb examples/mnist/data/mnist_test_lmdb I0609 21:58:59.625785 2348 data_layer.cpp:41] output data size: 100,1,28,28 I0609 21:58:59.627791 2348 net.cpp:141] Setting up mnist I0609 21:58:59.628293 2348 net.cpp:148] Top shape: 100 1 28 28 (78400) I0609 21:58:59.628293 16168 common.cpp:36] System entropy source not available, using fallback algorithm to generate seed instead. I0609 21:58:59.628293 2348 net.cpp:148] Top shape: 100 (100) I0609 21:58:59.631743 2348 net.cpp:156] Memory required for data: 314000 I0609 21:58:59.633249 2348 layer_factory.hpp:77] Creating layer label_mnist_1_split I0609 21:58:59.634250 2348 net.cpp:91] Creating Layer label_mnist_1_split I0609 21:58:59.634752 2348 net.cpp:425] label_mnist_1_split <- label I0609 21:58:59.635253 2348 net.cpp:399] label_mnist_1_split -> label_mnist_1_split_0 I0609 21:58:59.636255 2348 net.cpp:399] label_mnist_1_split -> label_mnist_1_split_1 I0609 21:58:59.636757 2348 net.cpp:141] Setting up label_mnist_1_split I0609 21:58:59.637259 2348 net.cpp:148] Top shape: 100 (100) I0609 21:58:59.637760 2348 net.cpp:148] Top shape: 100 (100) I0609 21:58:59.638262 2348 net.cpp:156] Memory required for data: 314800 I0609 21:58:59.638262 2348 layer_factory.hpp:77] Creating layer conv1 I0609 21:58:59.639732 2348 net.cpp:91] Creating Layer conv1 I0609 21:58:59.640235 2348 net.cpp:425] conv1 <- data I0609 21:58:59.640736 2348 net.cpp:399] conv1 -> conv1 I0609 21:58:59.642741 2348 net.cpp:141] Setting up conv1 I0609 21:58:59.643244 2348 net.cpp:148] Top shape: 100 20 24 24 (1152000) I0609 21:58:59.643744 2348 net.cpp:156] Memory required for data: 4922800 I0609 21:58:59.644747 2348 layer_factory.hpp:77] Creating layer pool1 I0609 21:58:59.645248 2348 net.cpp:91] Creating Layer pool1 I0609 21:58:59.646250 2348 net.cpp:425] pool1 <- conv1 I0609 21:58:59.646752 2348 net.cpp:399] pool1 -> pool1 I0609 21:58:59.647254 2348 net.cpp:141] Setting up pool1 I0609 21:58:59.647755 2348 net.cpp:148] Top shape: 100 20 12 12 (288000) I0609 21:58:59.649691 2348 net.cpp:156] Memory required for data: 6074800 I0609 21:58:59.650194 2348 layer_factory.hpp:77] Creating layer conv2 I0609 21:58:59.651197 2348 net.cpp:91] Creating Layer conv2 I0609 21:58:59.651698 2348 net.cpp:425] conv2 <- pool1 I0609 21:58:59.652199 2348 net.cpp:399] conv2 -> conv2 I0609 21:58:59.654706 2348 net.cpp:141] Setting up conv2 I0609 21:58:59.655207 2348 net.cpp:148] Top shape: 100 50 8 8 (320000) I0609 21:58:59.655709 2348 net.cpp:156] Memory required for data: 7354800 I0609 21:58:59.656711 2348 layer_factory.hpp:77] Creating layer pool2 I0609 21:58:59.657213 2348 net.cpp:91] Creating Layer pool2 I0609 21:58:59.657714 2348 net.cpp:425] pool2 <- conv2 I0609 21:58:59.658215 2348 net.cpp:399] pool2 -> pool2 I0609 21:58:59.658717 2348 net.cpp:141] Setting up pool2 I0609 21:58:59.659699 2348 net.cpp:148] Top shape: 100 50 4 4 (80000) I0609 21:58:59.660202 2348 net.cpp:156] Memory required for data: 7674800 I0609 21:58:59.660717 2348 layer_factory.hpp:77] Creating layer ip1 I0609 21:58:59.661706 2348 net.cpp:91] Creating Layer ip1 I0609 21:58:59.662207 2348 net.cpp:425] ip1 <- pool2 I0609 21:58:59.663209 2348 net.cpp:399] ip1 -> ip1 I0609 21:58:59.666218 2348 net.cpp:141] Setting up ip1 I0609 21:58:59.666719 2348 net.cpp:148] Top shape: 100 500 (50000) I0609 21:58:59.667722 2348 net.cpp:156] Memory required for data: 7874800 I0609 21:58:59.668725 2348 layer_factory.hpp:77] Creating layer relu1 I0609 21:58:59.668725 2348 net.cpp:91] Creating Layer relu1 I0609 21:58:59.670668 2348 net.cpp:425] relu1 <- ip1 I0609 21:58:59.671670 2348 net.cpp:386] relu1 -> ip1 (in-place) I0609 21:58:59.673177 2348 net.cpp:141] Setting up relu1 I0609 21:58:59.673676 2348 net.cpp:148] Top shape: 100 500 (50000) I0609 21:58:59.674679 2348 net.cpp:156] Memory required for data: 8074800 I0609 21:58:59.675180 2348 layer_factory.hpp:77] Creating layer ip2 I0609 21:58:59.676182 2348 net.cpp:91] Creating Layer ip2 I0609 21:58:59.676684 2348 net.cpp:425] ip2 <- ip1 I0609 21:58:59.677197 2348 net.cpp:399] ip2 -> ip2 I0609 21:58:59.678189 2348 net.cpp:141] Setting up ip2 I0609 21:58:59.678689 2348 net.cpp:148] Top shape: 100 10 (1000) I0609 21:58:59.679190 2348 net.cpp:156] Memory required for data: 8078800 I0609 21:58:59.679692 2348 layer_factory.hpp:77] Creating layer ip2_ip2_0_split I0609 21:58:59.680694 2348 net.cpp:91] Creating Layer ip2_ip2_0_split I0609 21:58:59.681196 2348 net.cpp:425] ip2_ip2_0_split <- ip2 I0609 21:58:59.682199 2348 net.cpp:399] ip2_ip2_0_split -> ip2_ip2_0_split_0 I0609 21:58:59.682700 2348 net.cpp:399] ip2_ip2_0_split -> ip2_ip2_0_split_1 I0609 21:58:59.683702 2348 net.cpp:141] Setting up ip2_ip2_0_split I0609 21:58:59.684204 2348 net.cpp:148] Top shape: 100 10 (1000) I0609 21:58:59.684705 2348 net.cpp:148] Top shape: 100 10 (1000) I0609 21:58:59.685206 2348 net.cpp:156] Memory required for data: 8086800 I0609 21:58:59.686209 2348 layer_factory.hpp:77] Creating layer accuracy I0609 21:58:59.686710 2348 net.cpp:91] Creating Layer accuracy I0609 21:58:59.687211 2348 net.cpp:425] accuracy <- ip2_ip2_0_split_0 I0609 21:58:59.688215 2348 net.cpp:425] accuracy <- label_mnist_1_split_0 I0609 21:58:59.688716 2348 net.cpp:399] accuracy -> accuracy I0609 21:58:59.689719 2348 net.cpp:141] Setting up accuracy I0609 21:58:59.690220 2348 net.cpp:148] Top shape: (1) I0609 21:58:59.690721 2348 net.cpp:156] Memory required for data: 8086804 I0609 21:58:59.691725 2348 layer_factory.hpp:77] Creating layer loss I0609 21:58:59.692225 2348 net.cpp:91] Creating Layer loss I0609 21:58:59.693228 2348 net.cpp:425] loss <- ip2_ip2_0_split_1 I0609 21:58:59.693730 2348 net.cpp:425] loss <- label_mnist_1_split_1 I0609 21:58:59.694231 2348 net.cpp:399] loss -> loss I0609 21:58:59.695233 2348 layer_factory.hpp:77] Creating layer loss I0609 21:58:59.696238 2348 net.cpp:141] Setting up loss I0609 21:58:59.696738 2348 net.cpp:148] Top shape: (1) I0609 21:58:59.697239 2348 net.cpp:151] with loss weight 1 I0609 21:58:59.697741 2348 net.cpp:156] Memory required for data: 8086808 I0609 21:58:59.698745 2348 net.cpp:217] loss needs backward computation. I0609 21:58:59.698745 2348 net.cpp:219] accuracy does not need backward computation. I0609 21:58:59.700230 2348 net.cpp:217] ip2_ip2_0_split needs backward computation. I0609 21:58:59.700731 2348 net.cpp:217] ip2 needs backward computation. I0609 21:58:59.701735 2348 net.cpp:217] relu1 needs backward computation. I0609 21:58:59.702235 2348 net.cpp:217] ip1 needs backward computation. I0609 21:58:59.702741 2348 net.cpp:217] pool2 needs backward computation. I0609 21:58:59.703739 2348 net.cpp:217] conv2 needs backward computation. I0609 21:58:59.704241 2348 net.cpp:217] pool1 needs backward computation. I0609 21:58:59.705242 2348 net.cpp:217] conv1 needs backward computation. I0609 21:58:59.706245 2348 net.cpp:219] label_mnist_1_split does not need backward computation. I0609 21:58:59.707249 2348 net.cpp:219] mnist does not need backward computation. I0609 21:58:59.707751 2348 net.cpp:261] This network produces output accuracy I0609 21:58:59.708753 2348 net.cpp:261] This network produces output loss I0609 21:58:59.709735 2348 net.cpp:274] Network initialization done. I0609 21:58:59.710247 2348 solver.cpp:60] Solver scaffolding done. I0609 21:58:59.711746 2348 caffe.cpp:220] Starting Optimization I0609 21:58:59.712244 2348 solver.cpp:279] Solving LeNet I0609 21:58:59.712746 2348 solver.cpp:280] Learning Rate Policy: inv I0609 21:58:59.715257 2348 solver.cpp:337] Iteration 0, Testing net (#0) I0609 21:59:00.121788 2348 solver.cpp:404] Test net output #0: accuracy = 0.0815 I0609 21:59:00.122314 2348 solver.cpp:404] Test net output #1: loss = 2.40893 (* 1 = 2.40893 loss) I0609 21:59:00.132297 2348 solver.cpp:228] Iteration 0, loss = 2.44759 I0609 21:59:00.132798 2348 solver.cpp:244] Train net output #0: loss = 2.44759 (* 1 = 2.44759 loss) I0609 21:59:00.133800 2348 sgd_solver.cpp:106] Iteration 0, lr = 0.01 I0609 21:59:01.148155 2348 solver.cpp:228] Iteration 100, loss = 0.251657 I0609 21:59:01.148669 2348 solver.cpp:244] Train net output #0: loss = 0.251657 (* 1 = 0.251657 loss) I0609 21:59:01.150401 2348 sgd_solver.cpp:106] Iteration 100, lr = 0.00992565 I0609 21:59:02.165963 2348 solver.cpp:228] Iteration 200, loss = 0.169378 I0609 21:59:02.166465 2348 solver.cpp:244] Train net output #0: loss = 0.169378 (* 1 = 0.169378 loss) I0609 21:59:02.167490 2348 sgd_solver.cpp:106] Iteration 200, lr = 0.00985258 I0609 21:59:03.187507 2348 solver.cpp:228] Iteration 300, loss = 0.19771 I0609 21:59:03.188009 2348 solver.cpp:244] Train net output #0: loss = 0.19771 (* 1 = 0.19771 loss) I0609 21:59:03.189012 2348 sgd_solver.cpp:106] Iteration 300, lr = 0.00978075 I0609 21:59:04.209780 2348 solver.cpp:228] Iteration 400, loss = 0.0709589 I0609 21:59:04.210305 2348 solver.cpp:244] Train net output #0: loss = 0.0709588 (* 1 = 0.0709588 loss) I0609 21:59:04.211309 2348 sgd_solver.cpp:106] Iteration 400, lr = 0.00971013 I0609 21:59:05.221642 2348 solver.cpp:337] Iteration 500, Testing net (#0) I0609 21:59:05.616386 2348 solver.cpp:404] Test net output #0: accuracy = 0.9703 I0609 21:59:05.617415 2348 solver.cpp:404] Test net output #1: loss = 0.0891924 (* 1 = 0.0891924 loss) I0609 21:59:05.620885 2348 solver.cpp:228] Iteration 500, loss = 0.0975274 I0609 21:59:05.621381 2348 solver.cpp:244] Train net output #0: loss = 0.0975274 (* 1 = 0.0975274 loss) I0609 21:59:05.622406 2348 sgd_solver.cpp:106] Iteration 500, lr = 0.00964069 I0609 21:59:06.647480 2348 solver.cpp:228] Iteration 600, loss = 0.0815395 I0609 21:59:06.648496 2348 solver.cpp:244] Train net output #0: loss = 0.0815395 (* 1 = 0.0815395 loss) I0609 21:59:06.650513 2348 sgd_solver.cpp:106] Iteration 600, lr = 0.0095724 I0609 21:59:07.672715 2348 solver.cpp:228] Iteration 700, loss = 0.143028 I0609 21:59:07.673744 2348 solver.cpp:244] Train net output #0: loss = 0.143028 (* 1 = 0.143028 loss) I0609 21:59:07.674721 2348 sgd_solver.cpp:106] Iteration 700, lr = 0.00950522 I0609 21:59:08.695850 2348 solver.cpp:228] Iteration 800, loss = 0.191974 I0609 21:59:08.696853 2348 solver.cpp:244] Train net output #0: loss = 0.191974 (* 1 = 0.191974 loss) I0609 21:59:08.698863 2348 sgd_solver.cpp:106] Iteration 800, lr = 0.00943913 I0609 21:59:09.723435 2348 solver.cpp:228] Iteration 900, loss = 0.164836 I0609 21:59:09.724433 2348 solver.cpp:244] Train net output #0: loss = 0.164836 (* 1 = 0.164836 loss) I0609 21:59:09.725436 2348 sgd_solver.cpp:106] Iteration 900, lr = 0.00937411 I0609 21:59:10.738751 2348 solver.cpp:337] Iteration 1000, Testing net (#0) I0609 21:59:11.137553 2348 solver.cpp:404] Test net output #0: accuracy = 0.9815 I0609 21:59:11.138049 2348 solver.cpp:404] Test net output #1: loss = 0.0602186 (* 1 = 0.0602186 loss) I0609 21:59:11.141587 2348 solver.cpp:228] Iteration 1000, loss = 0.105756 I0609 21:59:11.142087 2348 solver.cpp:244] Train net output #0: loss = 0.105756 (* 1 = 0.105756 loss) I0609 21:59:11.143090 2348 sgd_solver.cpp:106] Iteration 1000, lr = 0.00931012 I0609 21:59:12.167500 2348 solver.cpp:228] Iteration 1100, loss = 0.00832521 I0609 21:59:12.168500 2348 solver.cpp:244] Train net output #0: loss = 0.0083252 (* 1 = 0.0083252 loss) I0609 21:59:12.169531 2348 sgd_solver.cpp:106] Iteration 1100, lr = 0.00924715 I0609 21:59:13.195940 2348 solver.cpp:228] Iteration 1200, loss = 0.0214419 I0609 21:59:13.196943 2348 solver.cpp:244] Train net output #0: loss = 0.0214419 (* 1 = 0.0214419 loss) I0609 21:59:13.197944 2348 sgd_solver.cpp:106] Iteration 1200, lr = 0.00918515 I0609 21:59:14.228776 2348 solver.cpp:228] Iteration 1300, loss = 0.0214616 I0609 21:59:14.229806 2348 solver.cpp:244] Train net output #0: loss = 0.0214616 (* 1 = 0.0214616 loss) I0609 21:59:14.231287 2348 sgd_solver.cpp:106] Iteration 1300, lr = 0.00912412 I0609 21:59:15.256608 2348 solver.cpp:228] Iteration 1400, loss = 0.00489997 I0609 21:59:15.257612 2348 solver.cpp:244] Train net output #0: loss = 0.00489997 (* 1 = 0.00489997 loss) I0609 21:59:15.258613 2348 sgd_solver.cpp:106] Iteration 1400, lr = 0.00906403 I0609 21:59:16.276813 2348 solver.cpp:337] Iteration 1500, Testing net (#0) I0609 21:59:16.680711 2348 solver.cpp:404] Test net output #0: accuracy = 0.9856 I0609 21:59:16.681715 2348 solver.cpp:404] Test net output #1: loss = 0.0481414 (* 1 = 0.0481414 loss) I0609 21:59:16.685223 2348 solver.cpp:228] Iteration 1500, loss = 0.0776493 I0609 21:59:16.686233 2348 solver.cpp:244] Train net output #0: loss = 0.0776493 (* 1 = 0.0776493 loss) I0609 21:59:16.687228 2348 sgd_solver.cpp:106] Iteration 1500, lr = 0.00900485 I0609 21:59:17.710029 2348 solver.cpp:228] Iteration 1600, loss = 0.104206 I0609 21:59:17.711032 2348 solver.cpp:244] Train net output #0: loss = 0.104206 (* 1 = 0.104206 loss) I0609 21:59:17.712563 2348 sgd_solver.cpp:106] Iteration 1600, lr = 0.00894657 I0609 21:59:18.737736 2348 solver.cpp:228] Iteration 1700, loss = 0.027304 I0609 21:59:18.738772 2348 solver.cpp:244] Train net output #0: loss = 0.027304 (* 1 = 0.027304 loss) I0609 21:59:18.739742 2348 sgd_solver.cpp:106] Iteration 1700, lr = 0.00888916 I0609 21:59:19.766360 2348 solver.cpp:228] Iteration 1800, loss = 0.023843 I0609 21:59:19.766862 2348 solver.cpp:244] Train net output #0: loss = 0.023843 (* 1 = 0.023843 loss) I0609 21:59:19.768401 2348 sgd_solver.cpp:106] Iteration 1800, lr = 0.0088326 I0609 21:59:20.791230 2348 solver.cpp:228] Iteration 1900, loss = 0.137871 I0609 21:59:20.792232 2348 solver.cpp:244] Train net output #0: loss = 0.137871 (* 1 = 0.137871 loss) I0609 21:59:20.793262 2348 sgd_solver.cpp:106] Iteration 1900, lr = 0.00877687 I0609 21:59:21.805945 2348 solver.cpp:337] Iteration 2000, Testing net (#0) I0609 21:59:22.198869 2348 solver.cpp:404] Test net output #0: accuracy = 0.9858 I0609 21:59:22.198869 2348 solver.cpp:404] Test net output #1: loss = 0.042713 (* 1 = 0.042713 loss) I0609 21:59:22.203277 2348 solver.cpp:228] Iteration 2000, loss = 0.0157902 I0609 21:59:22.203778 2348 solver.cpp:244] Train net output #0: loss = 0.0157902 (* 1 = 0.0157902 loss) I0609 21:59:22.204782 2348 sgd_solver.cpp:106] Iteration 2000, lr = 0.00872196 I0609 21:59:23.225020 2348 solver.cpp:228] Iteration 2100, loss = 0.0210831 I0609 21:59:23.226011 2348 solver.cpp:244] Train net output #0: loss = 0.0210831 (* 1 = 0.0210831 loss) I0609 21:59:23.227027 2348 sgd_solver.cpp:106] Iteration 2100, lr = 0.00866784 I0609 21:59:24.245699 2348 solver.cpp:228] Iteration 2200, loss = 0.0173401 I0609 21:59:24.246728 2348 solver.cpp:244] Train net output #0: loss = 0.0173401 (* 1 = 0.0173401 loss) I0609 21:59:24.248229 2348 sgd_solver.cpp:106] Iteration 2200, lr = 0.0086145 I0609 21:59:25.268486 2348 solver.cpp:228] Iteration 2300, loss = 0.092744 I0609 21:59:25.269476 2348 solver.cpp:244] Train net output #0: loss = 0.092744 (* 1 = 0.092744 loss) I0609 21:59:25.270503 2348 sgd_solver.cpp:106] Iteration 2300, lr = 0.00856192 I0609 21:59:26.290300 2348 solver.cpp:228] Iteration 2400, loss = 0.0131563 I0609 21:59:26.291321 2348 solver.cpp:244] Train net output #0: loss = 0.0131563 (* 1 = 0.0131563 loss) I0609 21:59:26.292809 2348 sgd_solver.cpp:106] Iteration 2400, lr = 0.00851008 I0609 21:59:27.302772 2348 solver.cpp:337] Iteration 2500, Testing net (#0) I0609 21:59:27.698292 2348 solver.cpp:404] Test net output #0: accuracy = 0.9863 I0609 21:59:27.698792 2348 solver.cpp:404] Test net output #1: loss = 0.0437391 (* 1 = 0.0437391 loss) I0609 21:59:27.702771 2348 solver.cpp:228] Iteration 2500, loss = 0.0611808 I0609 21:59:27.703295 2348 solver.cpp:244] Train net output #0: loss = 0.0611808 (* 1 = 0.0611808 loss) I0609 21:59:27.704299 2348 sgd_solver.cpp:106] Iteration 2500, lr = 0.00845897 I0609 21:59:28.729406 2348 solver.cpp:228] Iteration 2600, loss = 0.0616705 I0609 21:59:28.730437 2348 solver.cpp:244] Train net output #0: loss = 0.0616705 (* 1 = 0.0616705 loss) I0609 21:59:28.731914 2348 sgd_solver.cpp:106] Iteration 2600, lr = 0.00840857 I0609 21:59:29.756451 2348 solver.cpp:228] Iteration 2700, loss = 0.0355545 I0609 21:59:29.757477 2348 solver.cpp:244] Train net output #0: loss = 0.0355545 (* 1 = 0.0355545 loss) I0609 21:59:29.758982 2348 sgd_solver.cpp:106] Iteration 2700, lr = 0.00835886 I0609 21:59:30.784987 2348 solver.cpp:228] Iteration 2800, loss = 0.00188944 I0609 21:59:30.786484 2348 solver.cpp:244] Train net output #0: loss = 0.0018894 (* 1 = 0.0018894 loss) I0609 21:59:30.787995 2348 sgd_solver.cpp:106] Iteration 2800, lr = 0.00830984 I0609 21:59:31.807837 2348 solver.cpp:228] Iteration 2900, loss = 0.0199334 I0609 21:59:31.808862 2348 solver.cpp:244] Train net output #0: loss = 0.0199334 (* 1 = 0.0199334 loss) I0609 21:59:31.810272 2348 sgd_solver.cpp:106] Iteration 2900, lr = 0.00826148 I0609 21:59:32.850883 2348 solver.cpp:337] Iteration 3000, Testing net (#0) I0609 21:59:33.250350 2348 solver.cpp:404] Test net output #0: accuracy = 0.9864 I0609 21:59:33.251353 2348 solver.cpp:404] Test net output #1: loss = 0.0400717 (* 1 = 0.0400717 loss) I0609 21:59:33.255363 2348 solver.cpp:228] Iteration 3000, loss = 0.00727182 I0609 21:59:33.255890 2348 solver.cpp:244] Train net output #0: loss = 0.0072718 (* 1 = 0.0072718 loss) I0609 21:59:33.256867 2348 sgd_solver.cpp:106] Iteration 3000, lr = 0.00821377 I0609 21:59:34.318763 2348 solver.cpp:228] Iteration 3100, loss = 0.0180837 I0609 21:59:34.319263 2348 solver.cpp:244] Train net output #0: loss = 0.0180837 (* 1 = 0.0180837 loss) I0609 21:59:34.321131 2348 sgd_solver.cpp:106] Iteration 3100, lr = 0.0081667 I0609 21:59:35.356736 2348 solver.cpp:228] Iteration 3200, loss = 0.00759276 I0609 21:59:35.357760 2348 solver.cpp:244] Train net output #0: loss = 0.00759278 (* 1 = 0.00759278 loss) I0609 21:59:35.358741 2348 sgd_solver.cpp:106] Iteration 3200, lr = 0.00812025 I0609 21:59:36.393894 2348 solver.cpp:228] Iteration 3300, loss = 0.032726 I0609 21:59:36.394395 2348 solver.cpp:244] Train net output #0: loss = 0.032726 (* 1 = 0.032726 loss) I0609 21:59:36.395925 2348 sgd_solver.cpp:106] Iteration 3300, lr = 0.00807442 I0609 21:59:37.450358 2348 solver.cpp:228] Iteration 3400, loss = 0.00748132 I0609 21:59:37.450861 2348 solver.cpp:244] Train net output #0: loss = 0.00748132 (* 1 = 0.00748132 loss) I0609 21:59:37.451864 2348 sgd_solver.cpp:106] Iteration 3400, lr = 0.00802918 I0609 21:59:38.472447 2348 solver.cpp:337] Iteration 3500, Testing net (#0) I0609 21:59:38.880427 2348 solver.cpp:404] Test net output #0: accuracy = 0.9864 I0609 21:59:38.881430 2348 solver.cpp:404] Test net output #1: loss = 0.0396068 (* 1 = 0.0396068 loss) I0609 21:59:38.885442 2348 solver.cpp:228] Iteration 3500, loss = 0.00684103 I0609 21:59:38.886445 2348 solver.cpp:244] Train net output #0: loss = 0.00684103 (* 1 = 0.00684103 loss) I0609 21:59:38.888450 2348 sgd_solver.cpp:106] Iteration 3500, lr = 0.00798454 I0609 21:59:39.934547 2348 solver.cpp:228] Iteration 3600, loss = 0.0373588 I0609 21:59:39.935590 2348 solver.cpp:244] Train net output #0: loss = 0.0373588 (* 1 = 0.0373588 loss) I0609 21:59:39.936553 2348 sgd_solver.cpp:106] Iteration 3600, lr = 0.00794046 I0609 21:59:40.960068 2348 solver.cpp:228] Iteration 3700, loss = 0.0152083 I0609 21:59:40.961071 2348 solver.cpp:244] Train net output #0: loss = 0.0152083 (* 1 = 0.0152083 loss) I0609 21:59:40.962097 2348 sgd_solver.cpp:106] Iteration 3700, lr = 0.00789695 I0609 21:59:42.000985 2348 solver.cpp:228] Iteration 3800, loss = 0.00452279 I0609 21:59:42.001987 2348 solver.cpp:244] Train net output #0: loss = 0.00452277 (* 1 = 0.00452277 loss) I0609 21:59:42.002990 2348 sgd_solver.cpp:106] Iteration 3800, lr = 0.007854 I0609 21:59:43.033639 2348 solver.cpp:228] Iteration 3900, loss = 0.0299793 I0609 21:59:43.034148 2348 solver.cpp:244] Train net output #0: loss = 0.0299792 (* 1 = 0.0299792 loss) I0609 21:59:43.035667 2348 sgd_solver.cpp:106] Iteration 3900, lr = 0.00781158 I0609 21:59:44.058666 2348 solver.cpp:337] Iteration 4000, Testing net (#0) I0609 21:59:44.474100 2348 solver.cpp:404] Test net output #0: accuracy = 0.9893 I0609 21:59:44.475113 2348 solver.cpp:404] Test net output #1: loss = 0.0324524 (* 1 = 0.0324524 loss) I0609 21:59:44.478615 2348 solver.cpp:228] Iteration 4000, loss = 0.0161738 I0609 21:59:44.479115 2348 solver.cpp:244] Train net output #0: loss = 0.0161738 (* 1 = 0.0161738 loss) I0609 21:59:44.479619 2348 sgd_solver.cpp:106] Iteration 4000, lr = 0.00776969 I0609 21:59:45.522294 2348 solver.cpp:228] Iteration 4100, loss = 0.0233402 I0609 21:59:45.523298 2348 solver.cpp:244] Train net output #0: loss = 0.0233402 (* 1 = 0.0233402 loss) I0609 21:59:45.524301 2348 sgd_solver.cpp:106] Iteration 4100, lr = 0.00772833 I0609 21:59:46.557157 2348 solver.cpp:228] Iteration 4200, loss = 0.0079118 I0609 21:59:46.558182 2348 solver.cpp:244] Train net output #0: loss = 0.00791177 (* 1 = 0.00791177 loss) I0609 21:59:46.559185 2348 sgd_solver.cpp:106] Iteration 4200, lr = 0.00768748 I0609 21:59:47.588677 2348 solver.cpp:228] Iteration 4300, loss = 0.0314846 I0609 21:59:47.589680 2348 solver.cpp:244] Train net output #0: loss = 0.0314845 (* 1 = 0.0314845 loss) I0609 21:59:47.591207 2348 sgd_solver.cpp:106] Iteration 4300, lr = 0.00764712 I0609 21:59:48.616732 2348 solver.cpp:228] Iteration 4400, loss = 0.0195216 I0609 21:59:48.617235 2348 solver.cpp:244] Train net output #0: loss = 0.0195215 (* 1 = 0.0195215 loss) I0609 21:59:48.618263 2348 sgd_solver.cpp:106] Iteration 4400, lr = 0.00760726 I0609 21:59:49.642638 2348 solver.cpp:337] Iteration 4500, Testing net (#0) I0609 21:59:50.047365 2348 solver.cpp:404] Test net output #0: accuracy = 0.9878 I0609 21:59:50.047868 2348 solver.cpp:404] Test net output #1: loss = 0.0374535 (* 1 = 0.0374535 loss) I0609 21:59:50.051880 2348 solver.cpp:228] Iteration 4500, loss = 0.00524942 I0609 21:59:50.052379 2348 solver.cpp:244] Train net output #0: loss = 0.00524939 (* 1 = 0.00524939 loss) I0609 21:59:50.053405 2348 sgd_solver.cpp:106] Iteration 4500, lr = 0.00756788 I0609 21:59:51.075937 2348 solver.cpp:228] Iteration 4600, loss = 0.0127813 I0609 21:59:51.076992 2348 solver.cpp:244] Train net output #0: loss = 0.0127812 (* 1 = 0.0127812 loss) I0609 21:59:51.078007 2348 sgd_solver.cpp:106] Iteration 4600, lr = 0.00752897 I0609 21:59:52.104898 2348 solver.cpp:228] Iteration 4700, loss = 0.00635904 I0609 21:59:52.105911 2348 solver.cpp:244] Train net output #0: loss = 0.00635898 (* 1 = 0.00635898 loss) I0609 21:59:52.107391 2348 sgd_solver.cpp:106] Iteration 4700, lr = 0.00749052 I0609 21:59:53.135903 2348 solver.cpp:228] Iteration 4800, loss = 0.0132648 I0609 21:59:53.136395 2348 solver.cpp:244] Train net output #0: loss = 0.0132648 (* 1 = 0.0132648 loss) I0609 21:59:53.137398 2348 sgd_solver.cpp:106] Iteration 4800, lr = 0.00745253 I0609 21:59:54.159410 2348 solver.cpp:228] Iteration 4900, loss = 0.0026975 I0609 21:59:54.159912 2348 solver.cpp:244] Train net output #0: loss = 0.00269743 (* 1 = 0.00269743 loss) I0609 21:59:54.161918 2348 sgd_solver.cpp:106] Iteration 4900, lr = 0.00741498 I0609 21:59:55.177345 2348 solver.cpp:454] Snapshotting to binary proto file examples/mnist/lenet_iter_5000.caffemodel I0609 21:59:55.197401 2348 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_5000.solverstate I0609 21:59:55.203416 2348 solver.cpp:337] Iteration 5000, Testing net (#0) I0609 21:59:55.590181 2348 solver.cpp:404] Test net output #0: accuracy = 0.989 I0609 21:59:55.591202 2348 solver.cpp:404] Test net output #1: loss = 0.0324299 (* 1 = 0.0324299 loss) I0609 21:59:55.595188 2348 solver.cpp:228] Iteration 5000, loss = 0.0379492 I0609 21:59:55.595721 2348 solver.cpp:244] Train net output #0: loss = 0.0379491 (* 1 = 0.0379491 loss) I0609 21:59:55.596724 2348 sgd_solver.cpp:106] Iteration 5000, lr = 0.00737788 I0609 21:59:56.624313 2348 solver.cpp:228] Iteration 5100, loss = 0.018057 I0609 21:59:56.625315 2348 solver.cpp:244] Train net output #0: loss = 0.018057 (* 1 = 0.018057 loss) I0609 21:59:56.626318 2348 sgd_solver.cpp:106] Iteration 5100, lr = 0.0073412 I0609 21:59:57.653751 2348 solver.cpp:228] Iteration 5200, loss = 0.00522353 I0609 21:59:57.654777 2348 solver.cpp:244] Train net output #0: loss = 0.00522349 (* 1 = 0.00522349 loss) I0609 21:59:57.655779 2348 sgd_solver.cpp:106] Iteration 5200, lr = 0.00730495 I0609 21:59:58.682821 2348 solver.cpp:228] Iteration 5300, loss = 0.00132337 I0609 21:59:58.683823 2348 solver.cpp:244] Train net output #0: loss = 0.00132333 (* 1 = 0.00132333 loss) I0609 21:59:58.685328 2348 sgd_solver.cpp:106] Iteration 5300, lr = 0.00726911 I0609 21:59:59.714751 2348 solver.cpp:228] Iteration 5400, loss = 0.00640987 I0609 21:59:59.715754 2348 solver.cpp:244] Train net output #0: loss = 0.00640985 (* 1 = 0.00640985 loss) I0609 21:59:59.717280 2348 sgd_solver.cpp:106] Iteration 5400, lr = 0.00723368 I0609 22:00:00.736883 2348 solver.cpp:337] Iteration 5500, Testing net (#0) I0609 22:00:01.129827 2348 solver.cpp:404] Test net output #0: accuracy = 0.9888 I0609 22:00:01.130858 2348 solver.cpp:404] Test net output #1: loss = 0.0355597 (* 1 = 0.0355597 loss) I0609 22:00:01.134837 2348 solver.cpp:228] Iteration 5500, loss = 0.00897519 I0609 22:00:01.135354 2348 solver.cpp:244] Train net output #0: loss = 0.00897517 (* 1 = 0.00897517 loss) I0609 22:00:01.135864 2348 sgd_solver.cpp:106] Iteration 5500, lr = 0.00719865 I0609 22:00:02.165493 2348 solver.cpp:228] Iteration 5600, loss = 0.00140956 I0609 22:00:02.166020 2348 solver.cpp:244] Train net output #0: loss = 0.00140954 (* 1 = 0.00140954 loss) I0609 22:00:02.166996 2348 sgd_solver.cpp:106] Iteration 5600, lr = 0.00716402 I0609 22:00:03.193999 2348 solver.cpp:228] Iteration 5700, loss = 0.00917903 I0609 22:00:03.195029 2348 solver.cpp:244] Train net output #0: loss = 0.00917901 (* 1 = 0.00917901 loss) I0609 22:00:03.196033 2348 sgd_solver.cpp:106] Iteration 5700, lr = 0.00712977 I0609 22:00:04.224375 2348 solver.cpp:228] Iteration 5800, loss = 0.0258 I0609 22:00:04.225404 2348 solver.cpp:244] Train net output #0: loss = 0.0258 (* 1 = 0.0258 loss) I0609 22:00:04.226403 2348 sgd_solver.cpp:106] Iteration 5800, lr = 0.0070959 I0609 22:00:05.249439 2348 solver.cpp:228] Iteration 5900, loss = 0.00583253 I0609 22:00:05.251418 2348 solver.cpp:244] Train net output #0: loss = 0.00583252 (* 1 = 0.00583252 loss) I0609 22:00:05.252424 2348 sgd_solver.cpp:106] Iteration 5900, lr = 0.0070624 I0609 22:00:06.271405 2348 solver.cpp:337] Iteration 6000, Testing net (#0) I0609 22:00:06.664938 2348 solver.cpp:404] Test net output #0: accuracy = 0.9903 I0609 22:00:06.665462 2348 solver.cpp:404] Test net output #1: loss = 0.02914 (* 1 = 0.02914 loss) I0609 22:00:06.668948 2348 solver.cpp:228] Iteration 6000, loss = 0.00317923 I0609 22:00:06.669474 2348 solver.cpp:244] Train net output #0: loss = 0.00317922 (* 1 = 0.00317922 loss) I0609 22:00:06.669474 2348 sgd_solver.cpp:106] Iteration 6000, lr = 0.00702927 I0609 22:00:07.726944 2348 solver.cpp:228] Iteration 6100, loss = 0.00174165 I0609 22:00:07.727445 2348 solver.cpp:244] Train net output #0: loss = 0.00174163 (* 1 = 0.00174163 loss) I0609 22:00:07.728948 2348 sgd_solver.cpp:106] Iteration 6100, lr = 0.0069965 I0609 22:00:08.808486 2348 solver.cpp:228] Iteration 6200, loss = 0.00775676 I0609 22:00:08.809015 2348 solver.cpp:244] Train net output #0: loss = 0.00775674 (* 1 = 0.00775674 loss) I0609 22:00:08.809990 2348 sgd_solver.cpp:106] Iteration 6200, lr = 0.00696408 I0609 22:00:09.867480 2348 solver.cpp:228] Iteration 6300, loss = 0.00732531 I0609 22:00:09.868479 2348 solver.cpp:244] Train net output #0: loss = 0.00732529 (* 1 = 0.00732529 loss) I0609 22:00:09.869983 2348 sgd_solver.cpp:106] Iteration 6300, lr = 0.00693201 I0609 22:00:10.910008 2348 solver.cpp:228] Iteration 6400, loss = 0.00703314 I0609 22:00:10.911516 2348 solver.cpp:244] Train net output #0: loss = 0.00703313 (* 1 = 0.00703313 loss) I0609 22:00:10.913007 2348 sgd_solver.cpp:106] Iteration 6400, lr = 0.00690029 I0609 22:00:11.943835 2348 solver.cpp:337] Iteration 6500, Testing net (#0) I0609 22:00:12.348021 2348 solver.cpp:404] Test net output #0: accuracy = 0.9893 I0609 22:00:12.348515 2348 solver.cpp:404] Test net output #1: loss = 0.0328561 (* 1 = 0.0328561 loss) I0609 22:00:12.353009 2348 solver.cpp:228] Iteration 6500, loss = 0.00939411 I0609 22:00:12.353512 2348 solver.cpp:244] Train net output #0: loss = 0.00939411 (* 1 = 0.00939411 loss) I0609 22:00:12.355015 2348 sgd_solver.cpp:106] Iteration 6500, lr = 0.0068689 I0609 22:00:13.408545 2348 solver.cpp:228] Iteration 6600, loss = 0.016508 I0609 22:00:13.409046 2348 solver.cpp:244] Train net output #0: loss = 0.016508 (* 1 = 0.016508 loss) I0609 22:00:13.409548 2348 sgd_solver.cpp:106] Iteration 6600, lr = 0.00683784 I0609 22:00:14.451058 2348 solver.cpp:228] Iteration 6700, loss = 0.0076011 I0609 22:00:14.452060 2348 solver.cpp:244] Train net output #0: loss = 0.0076011 (* 1 = 0.0076011 loss) I0609 22:00:14.452585 2348 sgd_solver.cpp:106] Iteration 6700, lr = 0.00680711 I0609 22:00:15.485703 2348 solver.cpp:228] Iteration 6800, loss = 0.00344433 I0609 22:00:15.486706 2348 solver.cpp:244] Train net output #0: loss = 0.00344433 (* 1 = 0.00344433 loss) I0609 22:00:15.488713 2348 sgd_solver.cpp:106] Iteration 6800, lr = 0.0067767 I0609 22:00:16.521100 2348 solver.cpp:228] Iteration 6900, loss = 0.00540986 I0609 22:00:16.522101 2348 solver.cpp:244] Train net output #0: loss = 0.00540986 (* 1 = 0.00540986 loss) I0609 22:00:16.523131 2348 sgd_solver.cpp:106] Iteration 6900, lr = 0.0067466 I0609 22:00:17.547116 2348 solver.cpp:337] Iteration 7000, Testing net (#0) I0609 22:00:17.943941 2348 solver.cpp:404] Test net output #0: accuracy = 0.9902 I0609 22:00:17.944941 2348 solver.cpp:404] Test net output #1: loss = 0.0297431 (* 1 = 0.0297431 loss) I0609 22:00:17.948951 2348 solver.cpp:228] Iteration 7000, loss = 0.00382005 I0609 22:00:17.949452 2348 solver.cpp:244] Train net output #0: loss = 0.00382005 (* 1 = 0.00382005 loss) I0609 22:00:17.949954 2348 sgd_solver.cpp:106] Iteration 7000, lr = 0.00671681 I0609 22:00:18.987884 2348 solver.cpp:228] Iteration 7100, loss = 0.0125011 I0609 22:00:18.988886 2348 solver.cpp:244] Train net output #0: loss = 0.0125011 (* 1 = 0.0125011 loss) I0609 22:00:18.989917 2348 sgd_solver.cpp:106] Iteration 7100, lr = 0.00668733 I0609 22:00:20.030164 2348 solver.cpp:228] Iteration 7200, loss = 0.00423594 I0609 22:00:20.031647 2348 solver.cpp:244] Train net output #0: loss = 0.00423592 (* 1 = 0.00423592 loss) I0609 22:00:20.033653 2348 sgd_solver.cpp:106] Iteration 7200, lr = 0.00665815 I0609 22:00:21.080216 2348 solver.cpp:228] Iteration 7300, loss = 0.016576 I0609 22:00:21.081199 2348 solver.cpp:244] Train net output #0: loss = 0.016576 (* 1 = 0.016576 loss) I0609 22:00:21.082736 2348 sgd_solver.cpp:106] Iteration 7300, lr = 0.00662927 I0609 22:00:22.122757 2348 solver.cpp:228] Iteration 7400, loss = 0.00299412 I0609 22:00:22.123258 2348 solver.cpp:244] Train net output #0: loss = 0.00299412 (* 1 = 0.00299412 loss) I0609 22:00:22.124785 2348 sgd_solver.cpp:106] Iteration 7400, lr = 0.00660067 I0609 22:00:23.151199 2348 solver.cpp:337] Iteration 7500, Testing net (#0) I0609 22:00:23.551210 2348 solver.cpp:404] Test net output #0: accuracy = 0.9894 I0609 22:00:23.552215 2348 solver.cpp:404] Test net output #1: loss = 0.0335477 (* 1 = 0.0335477 loss) I0609 22:00:23.555724 2348 solver.cpp:228] Iteration 7500, loss = 0.00158821 I0609 22:00:23.556226 2348 solver.cpp:244] Train net output #0: loss = 0.00158823 (* 1 = 0.00158823 loss) I0609 22:00:23.557756 2348 sgd_solver.cpp:106] Iteration 7500, lr = 0.00657236 I0609 22:00:24.594072 2348 solver.cpp:228] Iteration 7600, loss = 0.00663249 I0609 22:00:24.595088 2348 solver.cpp:244] Train net output #0: loss = 0.00663251 (* 1 = 0.00663251 loss) I0609 22:00:24.596068 2348 sgd_solver.cpp:106] Iteration 7600, lr = 0.00654433 I0609 22:00:25.632128 2348 solver.cpp:228] Iteration 7700, loss = 0.0273988 I0609 22:00:25.633129 2348 solver.cpp:244] Train net output #0: loss = 0.0273988 (* 1 = 0.0273988 loss) I0609 22:00:25.634132 2348 sgd_solver.cpp:106] Iteration 7700, lr = 0.00651658 I0609 22:00:26.674262 2348 solver.cpp:228] Iteration 7800, loss = 0.00437337 I0609 22:00:26.674789 2348 solver.cpp:244] Train net output #0: loss = 0.0043734 (* 1 = 0.0043734 loss) I0609 22:00:26.675792 2348 sgd_solver.cpp:106] Iteration 7800, lr = 0.00648911 I0609 22:00:27.714426 2348 solver.cpp:228] Iteration 7900, loss = 0.00459187 I0609 22:00:27.715453 2348 solver.cpp:244] Train net output #0: loss = 0.0045919 (* 1 = 0.0045919 loss) I0609 22:00:27.717434 2348 sgd_solver.cpp:106] Iteration 7900, lr = 0.0064619 I0609 22:00:28.744298 2348 solver.cpp:337] Iteration 8000, Testing net (#0) I0609 22:00:29.134865 2348 solver.cpp:404] Test net output #0: accuracy = 0.9892 I0609 22:00:29.135869 2348 solver.cpp:404] Test net output #1: loss = 0.0302072 (* 1 = 0.0302072 loss) I0609 22:00:29.139376 2348 solver.cpp:228] Iteration 8000, loss = 0.00470957 I0609 22:00:29.139878 2348 solver.cpp:244] Train net output #0: loss = 0.00470959 (* 1 = 0.00470959 loss) I0609 22:00:29.139878 2348 sgd_solver.cpp:106] Iteration 8000, lr = 0.00643496 I0609 22:00:30.168918 2348 solver.cpp:228] Iteration 8100, loss = 0.0204394 I0609 22:00:30.169944 2348 solver.cpp:244] Train net output #0: loss = 0.0204394 (* 1 = 0.0204394 loss) I0609 22:00:30.169944 2348 sgd_solver.cpp:106] Iteration 8100, lr = 0.00640827 I0609 22:00:31.195981 2348 solver.cpp:228] Iteration 8200, loss = 0.00728066 I0609 22:00:31.196984 2348 solver.cpp:244] Train net output #0: loss = 0.00728066 (* 1 = 0.00728066 loss) I0609 22:00:31.198485 2348 sgd_solver.cpp:106] Iteration 8200, lr = 0.00638185 I0609 22:00:32.224184 2348 solver.cpp:228] Iteration 8300, loss = 0.0334625 I0609 22:00:32.225174 2348 solver.cpp:244] Train net output #0: loss = 0.0334625 (* 1 = 0.0334625 loss) I0609 22:00:32.226177 2348 sgd_solver.cpp:106] Iteration 8300, lr = 0.00635568 I0609 22:00:33.248881 2348 solver.cpp:228] Iteration 8400, loss = 0.00712529 I0609 22:00:33.249883 2348 solver.cpp:244] Train net output #0: loss = 0.00712529 (* 1 = 0.00712529 loss) I0609 22:00:33.251371 2348 sgd_solver.cpp:106] Iteration 8400, lr = 0.00632975 I0609 22:00:34.266192 2348 solver.cpp:337] Iteration 8500, Testing net (#0) I0609 22:00:34.670419 2348 solver.cpp:404] Test net output #0: accuracy = 0.9896 I0609 22:00:34.671445 2348 solver.cpp:404] Test net output #1: loss = 0.0309825 (* 1 = 0.0309825 loss) I0609 22:00:34.675432 2348 solver.cpp:228] Iteration 8500, loss = 0.00653579 I0609 22:00:34.675935 2348 solver.cpp:244] Train net output #0: loss = 0.0065358 (* 1 = 0.0065358 loss) I0609 22:00:34.676944 2348 sgd_solver.cpp:106] Iteration 8500, lr = 0.00630407 I0609 22:00:35.699957 2348 solver.cpp:228] Iteration 8600, loss = 0.00119654 I0609 22:00:35.701396 2348 solver.cpp:244] Train net output #0: loss = 0.00119654 (* 1 = 0.00119654 loss) I0609 22:00:35.703403 2348 sgd_solver.cpp:106] Iteration 8600, lr = 0.00627864 I0609 22:00:36.726616 2348 solver.cpp:228] Iteration 8700, loss = 0.00364678 I0609 22:00:36.727633 2348 solver.cpp:244] Train net output #0: loss = 0.00364679 (* 1 = 0.00364679 loss) I0609 22:00:36.728613 2348 sgd_solver.cpp:106] Iteration 8700, lr = 0.00625344 I0609 22:00:37.751993 2348 solver.cpp:228] Iteration 8800, loss = 0.0016851 I0609 22:00:37.752996 2348 solver.cpp:244] Train net output #0: loss = 0.00168511 (* 1 = 0.00168511 loss) I0609 22:00:37.754510 2348 sgd_solver.cpp:106] Iteration 8800, lr = 0.00622847 I0609 22:00:38.781461 2348 solver.cpp:228] Iteration 8900, loss = 0.000729938 I0609 22:00:38.782488 2348 solver.cpp:244] Train net output #0: loss = 0.000729947 (* 1 = 0.000729947 loss) I0609 22:00:38.783972 2348 sgd_solver.cpp:106] Iteration 8900, lr = 0.00620374 I0609 22:00:39.797982 2348 solver.cpp:337] Iteration 9000, Testing net (#0) I0609 22:00:40.195657 2348 solver.cpp:404] Test net output #0: accuracy = 0.9899 I0609 22:00:40.196158 2348 solver.cpp:404] Test net output #1: loss = 0.0301065 (* 1 = 0.0301065 loss) I0609 22:00:40.201172 2348 solver.cpp:228] Iteration 9000, loss = 0.0112834 I0609 22:00:40.202204 2348 solver.cpp:244] Train net output #0: loss = 0.0112834 (* 1 = 0.0112834 loss) I0609 22:00:40.203207 2348 sgd_solver.cpp:106] Iteration 9000, lr = 0.00617924 I0609 22:00:41.237516 2348 solver.cpp:228] Iteration 9100, loss = 0.00836366 I0609 22:00:41.238016 2348 solver.cpp:244] Train net output #0: loss = 0.00836367 (* 1 = 0.00836367 loss) I0609 22:00:41.239549 2348 sgd_solver.cpp:106] Iteration 9100, lr = 0.00615496 I0609 22:00:42.279135 2348 solver.cpp:228] Iteration 9200, loss = 0.00204895 I0609 22:00:42.280139 2348 solver.cpp:244] Train net output #0: loss = 0.00204896 (* 1 = 0.00204896 loss) I0609 22:00:42.281595 2348 sgd_solver.cpp:106] Iteration 9200, lr = 0.0061309 I0609 22:00:43.314584 2348 solver.cpp:228] Iteration 9300, loss = 0.00582986 I0609 22:00:43.315587 2348 solver.cpp:244] Train net output #0: loss = 0.00582986 (* 1 = 0.00582986 loss) I0609 22:00:43.317090 2348 sgd_solver.cpp:106] Iteration 9300, lr = 0.00610706 I0609 22:00:44.349597 2348 solver.cpp:228] Iteration 9400, loss = 0.0217738 I0609 22:00:44.350112 2348 solver.cpp:244] Train net output #0: loss = 0.0217738 (* 1 = 0.0217738 loss) I0609 22:00:44.351552 2348 sgd_solver.cpp:106] Iteration 9400, lr = 0.00608343 I0609 22:00:45.373124 2348 solver.cpp:337] Iteration 9500, Testing net (#0) I0609 22:00:45.770292 2348 solver.cpp:404] Test net output #0: accuracy = 0.9888 I0609 22:00:45.770292 2348 solver.cpp:404] Test net output #1: loss = 0.0370887 (* 1 = 0.0370887 loss) I0609 22:00:45.775274 2348 solver.cpp:228] Iteration 9500, loss = 0.00312861 I0609 22:00:45.775801 2348 solver.cpp:244] Train net output #0: loss = 0.00312861 (* 1 = 0.00312861 loss) I0609 22:00:45.776777 2348 sgd_solver.cpp:106] Iteration 9500, lr = 0.00606002 I0609 22:00:46.804632 2348 solver.cpp:228] Iteration 9600, loss = 0.00250086 I0609 22:00:46.805649 2348 solver.cpp:244] Train net output #0: loss = 0.00250085 (* 1 = 0.00250085 loss) I0609 22:00:46.806653 2348 sgd_solver.cpp:106] Iteration 9600, lr = 0.00603682 I0609 22:00:47.845692 2348 solver.cpp:228] Iteration 9700, loss = 0.0032868 I0609 22:00:47.846194 2348 solver.cpp:244] Train net output #0: loss = 0.0032868 (* 1 = 0.0032868 loss) I0609 22:00:47.848222 2348 sgd_solver.cpp:106] Iteration 9700, lr = 0.00601382 I0609 22:00:48.884227 2348 solver.cpp:228] Iteration 9800, loss = 0.0146238 I0609 22:00:48.885254 2348 solver.cpp:244] Train net output #0: loss = 0.0146238 (* 1 = 0.0146238 loss) I0609 22:00:48.887243 2348 sgd_solver.cpp:106] Iteration 9800, lr = 0.00599102 I0609 22:00:49.915205 2348 solver.cpp:228] Iteration 9900, loss = 0.00428488 I0609 22:00:49.916237 2348 solver.cpp:244] Train net output #0: loss = 0.00428487 (* 1 = 0.00428487 loss) I0609 22:00:49.917237 2348 sgd_solver.cpp:106] Iteration 9900, lr = 0.00596843 I0609 22:00:50.934748 2348 solver.cpp:454] Snapshotting to binary proto file examples/mnist/lenet_iter_10000.caffemodel I0609 22:00:50.965857 2348 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_10000.solverstate I0609 22:00:50.975267 2348 solver.cpp:317] Iteration 10000, loss = 0.0040412 I0609 22:00:50.975769 2348 solver.cpp:337] Iteration 10000, Testing net (#0) I0609 22:00:51.370457 2348 solver.cpp:404] Test net output #0: accuracy = 0.9911 I0609 22:00:51.370457 2348 solver.cpp:404] Test net output #1: loss = 0.0288451 (* 1 = 0.0288451 loss) I0609 22:00:51.372915 2348 solver.cpp:322] Optimization Done. I0609 22:00:51.373416 2348 caffe.cpp:223] Optimization Done.
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これから他の画像でも試していきたいと思います。
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