Caffe程式碼導讀（5）：對資料集進行Testing
阿新 • • 發佈：2019-02-14
上一篇介紹瞭如何準備資料集，做好準備之後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是資料集（包括訓練資料和測試資料），深度學習模型採用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，那麼在CAFFE_ROOT下執行如下命令：
$ ./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。引數說明如下：
test：表示對訓練好的模型進行Testing，而不是training。其他引數包括train, time, device_query。
-model=XXX：指定模型prototxt檔案，這是一個文字檔案，詳細描述了網路結構和資料集資訊。我用的prototxt內容如下：
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" }
裡面定義的網路結構如下圖所示：
-gpu=0：指定在GPU上執行，GPUID=0。如果你沒有GPU就去掉這個引數，預設在CPU上執行。
執行輸出如下：
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)