20161114——LeNet-5预测数据-第一次跑
2017-01-01 20:15
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| root@ubuntu:/home/caffe/caffe# ./build/tools/caffe.bin test \ > -model examples/mnist/lenet_train_test.prototxt \> -weights examples/mnist/lenet_iter_10000.caffemodel \ > -iterations 100I1113 22:23:18.152611 9264 caffe.cpp:279] Use CPU. I1113 22:23:18.173339 9264 net.cpp:322] The NetState phase (1) differed from the phase (0) specified by a rule in layer mnist I1113 22:23:18.175729 9264 net.cpp:58] Initializing net from parameters: name: "LeNet" state { phase: TEST level: 0 stage: "" } layer { name: "mnist" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { scale: 0.00390625 } data_param { source: "examples/mnist/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" } I1113 22:23:18.176378 9264 layer_factory.hpp:77] Creating layer mnist I1113 22:23:18.178973 9264 net.cpp:100] Creating Layer mnist I1113 22:23:18.179025 9264 net.cpp:408] mnist -> data I1113 22:23:18.179074 9264 net.cpp:408] mnist -> label I1113 22:23:18.179217 9267 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_test_lmdb I1113 22:23:18.180079 9264 data_layer.cpp:41] output data size: 100,1,28,28 I1113 22:23:18.180670 9264 net.cpp:150] Setting up mnist I1113 22:23:18.180714 9264 net.cpp:157] Top shape: 100 1 28 28 (78400) I1113 22:23:18.180728 9264 net.cpp:157] Top shape: 100 (100) I1113 22:23:18.180737 9264 net.cpp:165] Memory required for data: 314000 I1113 22:23:18.180752 9264 layer_factory.hpp:77] Creating layer label_mnist_1_split I1113 22:23:18.180765 9264 net.cpp:100] Creating Layer label_mnist_1_split I1113 22:23:18.180774 9264 net.cpp:434] label_mnist_1_split <- label I1113 22:23:18.180790 9264 net.cpp:408] label_mnist_1_split -> label_mnist_1_split_0 I1113 22:23:18.180807 9264 net.cpp:408] label_mnist_1_split -> label_mnist_1_split_1 I1113 22:23:18.181561 9264 net.cpp:150] Setting up label_mnist_1_split I1113 22:23:18.181596 9264 net.cpp:157] Top shape: 100 (100) I1113 22:23:18.181610 9264 net.cpp:157] Top shape: 100 (100) I1113 22:23:18.181619 9264 net.cpp:165] Memory required for data: 314800 I1113 22:23:18.181630 9264 layer_factory.hpp:77] Creating layer conv1 I1113 22:23:18.181660 9264 net.cpp:100] Creating Layer conv1 I1113 22:23:18.181671 9264 net.cpp:434] conv1 <- data I1113 22:23:18.181685 9264 net.cpp:408] conv1 -> conv1 I1113 22:23:18.182523 9264 net.cpp:150] Setting up conv1 I1113 22:23:18.182587 9264 net.cpp:157] Top shape: 100 20 24 24 (1152000) I1113 22:23:18.182601 9264 net.cpp:165] Memory required for data: 4922800 I1113 22:23:18.183148 9264 layer_factory.hpp:77] Creating layer pool1 I1113 22:23:18.183276 9264 net.cpp:100] Creating Layer pool1 I1113 22:23:18.183292 9264 net.cpp:434] pool1 <- conv1 I1113 22:23:18.183305 9264 net.cpp:408] pool1 -> pool1 I1113 22:23:18.184011 9264 net.cpp:150] Setting up pool1 I1113 22:23:18.184041 9264 net.cpp:157] Top shape: 100 20 12 12 (288000) I1113 22:23:18.184052 9264 net.cpp:165] Memory required for data: 6074800 I1113 22:23:18.184065 9264 layer_factory.hpp:77] Creating layer conv2 I1113 22:23:18.184089 9264 net.cpp:100] Creating Layer conv2 I1113 22:23:18.184118 9264 net.cpp:434] conv2 <- pool1 I1113 22:23:18.184135 9264 net.cpp:408] conv2 -> conv2 I1113 22:23:18.184834 9264 net.cpp:150] Setting up conv2 I1113 22:23:18.185140 9264 net.cpp:157] Top shape: 100 50 8 8 (320000) I1113 22:23:18.185185 9264 net.cpp:165] Memory required for data: 7354800 I1113 22:23:18.185240 9264 layer_factory.hpp:77] Creating layer pool2 I1113 22:23:18.185333 9264 net.cpp:100] Creating Layer pool2 I1113 22:23:18.185431 9264 net.cpp:434] pool2 <- conv2 I1113 22:23:18.185504 9264 net.cpp:408] pool2 -> pool2 I1113 22:23:18.185549 9264 net.cpp:150] Setting up pool2 I1113 22:23:18.185569 9264 net.cpp:157] Top shape: 100 50 4 4 (80000) I1113 22:23:18.185578 9264 net.cpp:165] Memory required for data: 7674800 I1113 22:23:18.185588 9264 layer_factory.hpp:77] Creating layer ip1 I1113 22:23:18.186364 9264 net.cpp:100] Creating Layer ip1 I1113 22:23:18.186419 9264 net.cpp:434] ip1 <- pool2 I1113 22:23:18.186444 9264 net.cpp:408] ip1 -> ip1 I1113 22:23:18.195292 9264 net.cpp:150] Setting up ip1 I1113 22:23:18.195338 9264 net.cpp:157] Top shape: 100 500 (50000) I1113 22:23:18.195349 9264 net.cpp:165] Memory required for data: 7874800 I1113 22:23:18.195384 9264 layer_factory.hpp:77] Creating layer relu1 I1113 22:23:18.195401 9264 net.cpp:100] Creating Layer relu1 I1113 22:23:18.195411 9264 net.cpp:434] relu1 <- ip1 I1113 22:23:18.195423 9 4000 264 net.cpp:395] relu1 -> ip1 (in-place) I1113 22:23:18.196239 9264 net.cpp:150] Setting up relu1 I1113 22:23:18.196269 9264 net.cpp:157] Top shape: 100 500 (50000) I1113 22:23:18.196280 9264 net.cpp:165] Memory required for data: 8074800 I1113 22:23:18.196292 9264 layer_factory.hpp:77] Creating layer ip2 I1113 22:23:18.196313 9264 net.cpp:100] Creating Layer ip2 I1113 22:23:18.196326 9264 net.cpp:434] ip2 <- ip1 I1113 22:23:18.196346 9264 net.cpp:408] ip2 -> ip2 I1113 22:23:18.196473 9264 net.cpp:150] Setting up ip2 I1113 22:23:18.196491 9264 net.cpp:157] Top shape: 100 10 (1000) I1113 22:23:18.196501 9264 net.cpp:165] Memory required for data: 8078800 I1113 22:23:18.196516 9264 layer_factory.hpp:77] Creating layer ip2_ip2_0_split I1113 22:23:18.196532 9264 net.cpp:100] Creating Layer ip2_ip2_0_split I1113 22:23:18.196542 9264 net.cpp:434] ip2_ip2_0_split <- ip2 I1113 22:23:18.196554 9264 net.cpp:408] ip2_ip2_0_split -> ip2_ip2_0_split_0 I1113 22:23:18.196568 9264 net.cpp:408] ip2_ip2_0_split -> ip2_ip2_0_split_1 I1113 22:23:18.196585 9264 net.cpp:150] Setting up ip2_ip2_0_split I1113 22:23:18.196599 9264 net.cpp:157] Top shape: 100 10 (1000) I1113 22:23:18.196610 9264 net.cpp:157] Top shape: 100 10 (1000) I1113 22:23:18.196620 9264 net.cpp:165] Memory required for data: 8086800 I1113 22:23:18.196630 9264 layer_factory.hpp:77] Creating layer accuracy I1113 22:23:18.197474 9264 net.cpp:100] Creating Layer accuracy I1113 22:23:18.197502 9264 net.cpp:434] accuracy <- ip2_ip2_0_split_0 I1113 22:23:18.197517 9264 net.cpp:434] accuracy <- label_mnist_1_split_0 I1113 22:23:18.197532 9264 net.cpp:408] accuracy -> accuracy I1113 22:23:18.197580 9264 net.cpp:150] Setting up accuracy I1113 22:23:18.197602 9264 net.cpp:157] Top shape: (1) I1113 22:23:18.197614 9264 net.cpp:165] Memory required for data: 8086804 I1113 22:23:18.197626 9264 layer_factory.hpp:77] Creating layer loss I1113 22:23:18.198261 9264 net.cpp:100] Creating Layer loss I1113 22:23:18.198290 9264 net.cpp:434] loss <- ip2_ip2_0_split_1 I1113 22:23:18.198304 9264 net.cpp:434] loss <- label_mnist_1_split_1 I1113 22:23:18.198345 9264 net.cpp:408] loss -> loss I1113 22:23:18.199340 9264 layer_factory.hpp:77] Creating layer loss I1113 22:23:18.199407 9264 net.cpp:150] Setting up loss I1113 22:23:18.199426 9264 net.cpp:157] Top shape: (1) I1113 22:23:18.199436 9264 net.cpp:160] with loss weight 1 I1113 22:23:18.200038 9264 net.cpp:165] Memory required for data: 8086808 I1113 22:23:18.200050 9264 net.cpp:226] loss needs backward computation. I1113 22:23:18.200058 9264 net.cpp:228] accuracy does not need backward computation. I1113 22:23:18.200067 9264 net.cpp:226] ip2_ip2_0_split needs backward computation. I1113 22:23:18.200073 9264 net.cpp:226] ip2 needs backward computation. I1113 22:23:18.200079 9264 net.cpp:226] relu1 needs backward computation. I1113 22:23:18.200086 9264 net.cpp:226] ip1 needs backward computation. I1113 22:23:18.200093 9264 net.cpp:226] pool2 needs backward computation. I1113 22:23:18.200099 9264 net.cpp:226] conv2 needs backward computation. I1113 22:23:18.200105 9264 net.cpp:226] pool1 needs backward computation. I1113 22:23:18.200112 9264 net.cpp:226] conv1 needs backward computation. I1113 22:23:18.200119 9264 net.cpp:228] label_mnist_1_split does not need backward computation. I1113 22:23:18.200126 9264 net.cpp:228] mnist does not need backward computation. I1113 22:23:18.200135 9264 net.cpp:270] This network produces output accuracy I1113 22:23:18.200147 9264 net.cpp:270] This network produces output loss I1113 22:23:18.200181 9264 net.cpp:283] Network initialization done. I1113 22:23:18.213127 9264 caffe.cpp:285] Running for 100 iterations. I1113 22:23:18.716567 9264 caffe.cpp:308] Batch 0, accuracy = 1 I1113 22:23:18.716647 9264 caffe.cpp:308] Batch 0, loss = 0.00938862 I1113 22:23:19.142791 9264 caffe.cpp:308] Batch 1, accuracy = 1 I1113 22:23:19.142884 9264 caffe.cpp:308] Batch 1, loss = 0.00550929 I1113 22:23:19.606091 9264 caffe.cpp:308] Batch 2, accuracy = 0.99 I1113 22:23:19.606170 9264 caffe.cpp:308] Batch 2, loss = 0.0508872 I1113 22:23:20.054841 9264 caffe.cpp:308] Batch 3, accuracy = 0.99 I1113 22:23:20.054941 9264 caffe.cpp:308] Batch 3, loss = 0.0376309 I1113 22:23:20.485735 9264 caffe.cpp:308] Batch 4, accuracy = 0.99 I1113 22:23:20.485807 9264 caffe.cpp:308] Batch 4, loss = 0.077192 I1113 22:23:20.962743 9264 caffe.cpp:308] Batch 5, accuracy = 0.99 I1113 22:23:20.962816 9264 caffe.cpp:308] Batch 5, loss = 0.0515025 I1113 22:23:21.388486 9264 caffe.cpp:308] Batch 6, accuracy = 0.98 I1113 22:23:21.388574 9264 caffe.cpp:308] Batch 6, loss = 0.0931183 I1113 22:23:21.815683 9264 caffe.cpp:308] Batch 7, accuracy = 0.99 I1113 22:23:21.815806 9264 caffe.cpp:308] Batch 7, loss = 0.041062 I1113 22:23:22.237282 9264 caffe.cpp:308] Batch 8, accuracy = 1 I1113 22:23:22.237368 9264 caffe.cpp:308] Batch 8, loss = 0.00694906 I1113 22:23:22.686130 9264 caffe.cpp:308] Batch 9, accuracy = 0.99 I1113 22:23:22.686210 9264 caffe.cpp:308] Batch 9, loss = 0.0272126 I1113 22:23:23.110630 9264 caffe.cpp:308] Batch 10, accuracy = 0.98 I1113 22:23:23.110712 9264 caffe.cpp:308] Batch 10, loss = 0.0507764 I1113 22:23:23.545310 9264 caffe.cpp:308] Batch 11, accuracy = 0.98 I1113 22:23:23.545372 9264 caffe.cpp:308] Batch 11, loss = 0.0363619 I1113 22:23:23.970454 9264 caffe.cpp:308] Batch 12, accuracy = 0.95 I1113 22:23:23.970535 9264 caffe.cpp:308] Batch 12, loss = 0.139559 I1113 22:23:24.413075 9264 caffe.cpp:308] Batch 13, accuracy = 0.98 I1113 22:23:24.413163 9264 caffe.cpp:308] Batch 13, loss = 0.0643891 I1113 22:23:24.836765 9264 caffe.cpp:308] Batch 14, accuracy = 0.99 I1113 22:23:24.836839 9264 caffe.cpp:308] Batch 14, loss = 0.0208035 I1113 22:23:25.255944 9264 caffe.cpp:308] Batch 15, accuracy = 0.99 I1113 22:23:25.256047 9264 caffe.cpp:308] Batch 15, loss = 0.0302129 I1113 22:23:25.695009 9264 caffe.cpp:308] Batch 16, accuracy = 0.98 I1113 22:23:25.695098 9264 caffe.cpp:308] Batch 16, loss = 0.0369838 I1113 22:23:26.124233 9264 caffe.cpp:308] Batch 17, accuracy = 1 I1113 22:23:26.124357 9264 caffe.cpp:308] Batch 17, loss = 0.0165045 I1113 22:23:26.555377 9264 caffe.cpp:308] Batch 18, accuracy = 1 I1113 22:23:26.555450 9264 caffe.cpp:308] Batch 18, loss = 0.0110177 I1113 22:23:26.963515 9264 caffe.cpp:308] Batch 19, accuracy = 0.99 I1113 22:23:26.963590 9264 caffe.cpp:308] Batch 19, loss = 0.0634679 I1113 22:23:27.390830 9264 caffe.cpp:308] Batch 20, accuracy = 0.98 I1113 22:23:27.390894 9264 caffe.cpp:308] Batch 20, loss = 0.08095 I1113 22:23:27.818614 9264 caffe.cpp:308] Batch 21, accuracy = 0.97 I1113 22:23:27.818688 9264 caffe.cpp:308] Batch 21, loss = 0.0524366 I1113 22:23:28.243798 9264 caffe.cpp:308] Batch 22, accuracy = 0.99 I1113 22:23:28.243883 9264 caffe.cpp:308] Batch 22, loss = 0.041327 I1113 22:23:28.676961 9264 caffe.cpp:308] Batch 23, accuracy = 0.99 I1113 22:23:28.677062 9264 caffe.cpp:308] Batch 23, loss = 0.0261502 I1113 22:23:29.112872 9264 caffe.cpp:308] Batch 24, accuracy = 0.98 I1113 22:23:29.112973 9264 caffe.cpp:308] Batch 24, loss = 0.0512742 I1113 22:23:29.541849 9264 caffe.cpp:308] Batch 25, accuracy = 0.99 I1113 22:23:29.541923 9264 caffe.cpp:308] Batch 25, loss = 0.0888764 I1113 22:23:29.961591 9264 caffe.cpp:308] Batch 26, accuracy = 0.99 I1113 22:23:29.961663 9264 caffe.cpp:308] Batch 26, loss = 0.108548 I1113 22:23:30.394340 9264 caffe.cpp:308] Batch 27, accuracy = 1 I1113 22:23:30.394412 9264 caffe.cpp:308] Batch 27, loss = 0.0103665 I1113 22:23:30.818648 9264 caffe.cpp:308] Batch 28, accuracy = 0.99 I1113 22:23:30.818737 9264 caffe.cpp:308] Batch 28, loss = 0.0305791 I1113 22:23:31.259657 9264 caffe.cpp:308] Batch 29, accuracy = 0.97 I1113 22:23:31.259748 9264 caffe.cpp:308] Batch 29, loss = 0.108499 I1113 22:23:31.703888 9264 caffe.cpp:308] Batch 30, accuracy = 1 I1113 22:23:31.703981 9264 caffe.cpp:308] Batch 30, loss = 0.0138441 I1113 22:23:32.149801 9264 caffe.cpp:308] Batch 31, accuracy = 1 I1113 22:23:32.149922 9264 caffe.cpp:308] Batch 31, loss = 0.00274972 I1113 22:23:32.585639 9264 caffe.cpp:308] Batch 32, accuracy = 0.99 I1113 22:23:32.585721 9264 caffe.cpp:308] Batch 32, loss = 0.014872 I1113 22:23:33.010319 9264 caffe.cpp:308] Batch 33, accuracy = 1 I1113 22:23:33.010391 9264 caffe.cpp:308] Batch 33, loss = 0.00557933 I1113 22:23:33.434504 9264 caffe.cpp:308] Batch 34, accuracy = 0.99 I1113 22:23:33.434592 9264 caffe.cpp:308] Batch 34, loss = 0.0435118 I1113 22:23:33.862126 9264 caffe.cpp:308] Batch 35, accuracy = 0.95 I1113 22:23:33.862200 9264 caffe.cpp:308] Batch 35, loss = 0.141068 I1113 22:23:34.296036 9264 caffe.cpp:308] Batch 36, accuracy = 1 I1113 22:23:34.296131 9264 caffe.cpp:308] Batch 36, loss = 0.00427433 I1113 22:23:34.745066 9264 caffe.cpp:308] Batch 37, accuracy = 0.98 I1113 22:23:34.745139 9264 caffe.cpp:308] Batch 37, loss = 0.0834443 I1113 22:23:35.169420 9264 caffe.cpp:308] Batch 38, accuracy = 0.99 I1113 22:23:35.169481 9264 caffe.cpp:308] Batch 38, loss = 0.0193586 I1113 22:23:35.584350 9264 caffe.cpp:308] Batch 39, accuracy = 0.98 I1113 22:23:35.584434 9264 caffe.cpp:308] Batch 39, loss = 0.03763 I1113 22:23:36.018128 9264 caffe.cpp:308] Batch 40, accuracy = 0.99 I1113 22:23:36.018245 9264 caffe.cpp:308] Batch 40, loss = 0.0272643 I1113 22:23:36.435228 9264 caffe.cpp:308] Batch 41, accuracy = 0.97 I1113 22:23:36.435304 9264 caffe.cpp:308] Batch 41, loss = 0.0821054 I1113 22:23:36.854449 9264 caffe.cpp:308] Batch 42, accuracy = 0.99 I1113 22:23:36.854549 9264 caffe.cpp:308] Batch 42, loss = 0.0260138 I1113 22:23:37.285959 9264 caffe.cpp:308] Batch 43, accuracy = 1 I1113 22:23:37.286029 9264 caffe.cpp:308] Batch 43, loss = 0.0122153 I1113 22:23:37.721631 9264 caffe.cpp:308] Batch 44, accuracy = 0.99 I1113 22:23:37.721696 9264 caffe.cpp:308] Batch 44, loss = 0.0179641 I1113 22:23:38.153909 9264 caffe.cpp:308] Batch 45, accuracy = 0.99 I1113 22:23:38.153973 9264 caffe.cpp:308] Batch 45, loss = 0.0411753 I1113 22:23:38.592052 9264 caffe.cpp:308] Batch 46, accuracy = 1 I1113 22:23:38.592134 9264 caffe.cpp:308] Batch 46, loss = 0.00871828 I1113 22:23:39.017051 9264 caffe.cpp:308] Batch 47, accuracy = 0.99 I1113 22:23:39.017124 9264 caffe.cpp:308] Batch 47, loss = 0.019142 I1113 22:23:39.444370 9264 caffe.cpp:308] Batch 48, accuracy = 0.97 I1113 22:23:39.444437 9264 caffe.cpp:308] Batch 48, loss = 0.113587 I1113 22:23:39.861088 9264 caffe.cpp:308] Batch 49, accuracy = 1 I1113 22:23:39.861165 9264 caffe.cpp:308] Batch 49, loss = 0.00563929 I1113 22:23:40.282407 9264 caffe.cpp:308] Batch 50, accuracy = 1 I1113 22:23:40.282487 9264 caffe.cpp:308] Batch 50, loss = 0.000420446 I1113 22:23:40.722947 9264 caffe.cpp:308] Batch 51, accuracy = 0.99 I1113 22:23:40.723053 9264 caffe.cpp:308] Batch 51, loss = 0.00879608 I1113 22:23:41.158830 9264 caffe.cpp:308] Batch 52, accuracy = 0.99 I1113 22:23:41.158905 9264 caffe.cpp:308] Batch 52, loss = 0.0259591 I1113 22:23:41.591497 9264 caffe.cpp:308] Batch 53, accuracy = 1 I1113 22:23:41.591567 9264 caffe.cpp:308] Batch 53, loss = 0.00159477 I1113 22:23:42.023552 9264 caffe.cpp:308] Batch 54, accuracy = 1 I1113 22:23:42.023633 9264 caffe.cpp:308] Batch 54, loss = 0.00324901 I1113 22:23:42.444705 9264 caffe.cpp:308] Batch 55, accuracy = 1 I1113 22:23:42.444790 9264 caffe.cpp:308] Batch 55, loss = 0.000148405 I1113 22:23:42.876534 9264 caffe.cpp:308] Batch 56, accuracy = 1 I1113 22:23:42.876618 9264 caffe.cpp:308] Batch 56, loss = 0.0105164 I1113 22:23:43.315635 9264 caffe.cpp:308] Batch 57, accuracy = 1 I1113 22:23:43.315773 9264 caffe.cpp:308] Batch 57, loss = 0.00503844 I1113 22:23:43.763896 9264 caffe.cpp:308] Batch 58, accuracy = 0.99 I1113 22:23:43.763972 9264 caffe.cpp:308] Batch 58, loss = 0.0108849 I1113 22:23:44.192829 9264 caffe.cpp:308] Batch 59, accuracy = 0.98 I1113 22:23:44.192906 9264 caffe.cpp:308] Batch 59, loss = 0.0836057 I1113 22:23:44.622547 9264 caffe.cpp:308] Batch 60, accuracy = 1 I1113 22:23:44.622627 9264 caffe.cpp:308] Batch 60, loss = 0.00554896 I1113 22:23:45.061022 9264 caffe.cpp:308] Batch 61, accuracy = 1 I1113 22:23:45.061094 9264 caffe.cpp:308] Batch 61, loss = 0.00197286 I1113 22:23:45.490329 9264 caffe.cpp:308] Batch 62, accuracy = 1 I1113 22:23:45.490404 9264 caffe.cpp:308] Batch 62, loss = 4.45838e-05 I1113 22:23:45.924926 9264 caffe.cpp:308] Batch 63, accuracy = 1 I1113 22:23:45.925246 9264 caffe.cpp:308] Batch 63, loss = 8.86665e-05 I1113 22:23:46.351568 9264 caffe.cpp:308] Batch 64, accuracy = 1 I1113 22:23:46.351652 9264 caffe.cpp:308] Batch 64, loss = 0.00062603 I1113 22:23:46.793261 9264 caffe.cpp:308] Batch 65, accuracy = 0.94 I1113 22:23:46.793334 9264 caffe.cpp:308] Batch 65, loss = 0.113657 I1113 22:23:47.218242 9264 caffe.cpp:308] Batch 66, accuracy = 0.98 I1113 22:23:47.218310 9264 caffe.cpp:308] Batch 66, loss = 0.0446646 I1113 22:23:47.645225 9264 caffe.cpp:308] Batch 67, accuracy = 0.99 I1113 22:23:47.645298 9264 caffe.cpp:308] Batch 67, loss = 0.0358956 I1113 22:23:48.068356 9264 caffe.cpp:308] Batch 68, accuracy = 0.99 I1113 22:23:48.068570 9264 caffe.cpp:308] Batch 68, loss = 0.0121253 I1113 22:23:48.509443 9264 caffe.cpp:308] Batch 69, accuracy = 1 I1113 22:23:48.509609 9264 caffe.cpp:308] Batch 69, loss = 0.00187437 I1113 22:23:48.938766 9264 caffe.cpp:308] Batch 70, accuracy = 1 I1113 22:23:48.938841 9264 caffe.cpp:308] Batch 70, loss = 0.000640704 I1113 22:23:49.364161 9264 caffe.cpp:308] Batch 71, accuracy = 1 I1113 22:23:49.364253 9264 caffe.cpp:308] Batch 71, loss = 0.000420613 I1113 22:23:49.821877 9264 caffe.cpp:308] Batch 72, accuracy = 1 I1113 22:23:49.821961 9264 caffe.cpp:308] Batch 72, loss = 0.00922661 I1113 22:23:50.275329 9264 caffe.cpp:308] Batch 73, accuracy = 1 I1113 22:23:50.275413 9264 caffe.cpp:308] Batch 73, loss = 0.000654965 I1113 22:23:50.710718 9264 caffe.cpp:308] Batch 74, accuracy = 1 I1113 22:23:50.710805 9264 caffe.cpp:308] Batch 74, loss = 0.00321759 I1113 22:23:51.151079 9264 caffe.cpp:308] Batch 75, accuracy = 1 I1113 22:23:51.151149 9264 caffe.cpp:308] Batch 75, loss = 0.00241048 I1113 22:23:51.577095 9264 caffe.cpp:308] Batch 76, accuracy = 1 I1113 22:23:51.577168 9264 caffe.cpp:308] Batch 76, loss = 0.000133293 I1113 22:23:52.017668 9264 caffe.cpp:308] Batch 77, accuracy = 1 I1113 22:23:52.017741 9264 caffe.cpp:308] Batch 77, loss = 0.000138202 I1113 22:23:52.448783 9264 caffe.cpp:308] Batch 78, accuracy = 1 I1113 22:23:52.448870 9264 caffe.cpp:308] Batch 78, loss = 0.00147909 I1113 22:23:52.893913 9264 caffe.cpp:308] Batch 79, accuracy = 1 I1113 22:23:52.893987 9264 caffe.cpp:308] Batch 79, loss = 0.00162909 I1113 22:23:53.331879 9264 caffe.cpp:308] Batch 80, accuracy = 0.99 I1113 2 a889 2:23:53.332003 9264 caffe.cpp:308] Batch 80, loss = 0.0239026 I1113 22:23:53.772898 9264 caffe.cpp:308] Batch 81, accuracy = 1 I1113 22:23:53.772971 9264 caffe.cpp:308] Batch 81, loss = 0.00128124 I1113 22:23:54.223368 9264 caffe.cpp:308] Batch 82, accuracy = 1 I1113 22:23:54.223448 9264 caffe.cpp:308] Batch 82, loss = 0.0012403 I1113 22:23:54.652359 9264 caffe.cpp:308] Batch 83, accuracy = 1 I1113 22:23:54.652432 9264 caffe.cpp:308] Batch 83, loss = 0.0126122 I1113 22:23:55.076644 9264 caffe.cpp:308] Batch 84, accuracy = 0.99 I1113 22:23:55.076748 9264 caffe.cpp:308] Batch 84, loss = 0.0249088 I1113 22:23:55.525315 9264 caffe.cpp:308] Batch 85, accuracy = 0.99 I1113 22:23:55.525436 9264 caffe.cpp:308] Batch 85, loss = 0.0145371 I1113 22:23:55.963310 9264 caffe.cpp:308] Batch 86, accuracy = 1 I1113 22:23:55.963379 9264 caffe.cpp:308] Batch 86, loss = 8.19215e-05 I1113 22:23:56.396705 9264 caffe.cpp:308] Batch 87, accuracy = 1 I1113 22:23:56.396802 9264 caffe.cpp:308] Batch 87, loss = 0.000117623 I1113 22:23:56.826771 9264 caffe.cpp:308] Batch 88, accuracy = 1 I1113 22:23:56.826851 9264 caffe.cpp:308] Batch 88, loss = 5.30659e-05 I1113 22:23:57.260174 9264 caffe.cpp:308] Batch 89, accuracy = 1 I1113 22:23:57.260283 9264 caffe.cpp:308] Batch 89, loss = 4.73997e-05 I1113 22:23:57.692572 9264 caffe.cpp:308] Batch 90, accuracy = 0.97 I1113 22:23:57.692658 9264 caffe.cpp:308] Batch 90, loss = 0.082495 I1113 22:23:58.121317 9264 caffe.cpp:308] Batch 91, accuracy = 1 I1113 22:23:58.121409 9264 caffe.cpp:308] Batch 91, loss = 3.68753e-05 I1113 22:23:58.558925 9264 caffe.cpp:308] Batch 92, accuracy = 1 I1113 22:23:58.559033 9264 caffe.cpp:308] Batch 92, loss = 9.49426e-05 I1113 22:23:58.984719 9264 caffe.cpp:308] Batch 93, accuracy = 1 I1113 22:23:58.984807 9264 caffe.cpp:308] Batch 93, loss = 0.00213211 I1113 22:23:59.403184 9264 caffe.cpp:308] Batch 94, accuracy = 1 I1113 22:23:59.403254 9264 caffe.cpp:308] Batch 94, loss = 0.000319923 I1113 22:23:59.828168 9264 caffe.cpp:308] Batch 95, accuracy = 1 I1113 22:23:59.828251 9264 caffe.cpp:308] Batch 95, loss = 0.00170309 I1113 22:24:00.258958 9264 caffe.cpp:308] Batch 96, accuracy = 0.98 I1113 22:24:00.259034 9264 caffe.cpp:308] Batch 96, loss = 0.0682994 I1113 22:24:00.676637 9264 caffe.cpp:308] Batch 97, accuracy = 0.97 I1113 22:24:00.676724 9264 caffe.cpp:308] Batch 97, loss = 0.0928666 I1113 22:24:01.116845 9264 caffe.cpp:308] Batch 98, accuracy = 1 I1113 22:24:01.116920 9264 caffe.cpp:308] Batch 98, loss = 0.00344261 I1113 22:24:01.559696 9264 caffe.cpp:308] Batch 99, accuracy = 0.99 I1113 22:24:01.559839 9264 caffe.cpp:308] Batch 99, loss = 0.0130951 I1113 22:24:01.559859 9264 caffe.cpp:313] Loss: 0.0291962 I1113 22:24:01.559878 9264 caffe.cpp:325] accuracy = 0.9911 I1113 22:24:01.559896 9264 caffe.cpp:325] loss = 0.0291962 (* 1 = 0.0291962 loss) root@ubuntu:/home/caffe/caffe#  |
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