深度學(xué)習(xí)框架caffe的學(xué)習(xí)

1、深度學(xué)習(xí)框架caffe的介紹Robothy1.1caffe產(chǎn)生提出了一個(gè)很好的概率框架來表達(dá)人的行為但是因?yàn)閳D像上提取的特征比較弱，可以外推的結(jié)果比較有限。NVIDIA贈送GPU賈揚(yáng)清1.2Caffe的特點(diǎn)上手快上手快跨平臺跨平臺社區(qū)好社區(qū)好開放性開放性速度快速度快2.1硬件配置與操作系統(tǒng)處理器：Intel(R) Core(TM) i5-3230M CPU 2.60GHz 內(nèi)存：4GB 操作系統(tǒng)：Ubuntu14.04 64bit磁盤：安裝磁盤空間為100GB2.2依賴庫與工具依賴包：lprotobuf leveldb snappy opencv hdf5-serial protobuf-co

2、mpiler boost工具：google-glog、google-gflag、git2.3 核心軟件的安裝lmdbOpenCVCaffegit clone3.1 mnist數(shù)據(jù)集手寫數(shù)字圖片60000張訓(xùn)練庫圖片10000張測試庫圖片./get_mnist.sh3.2mnist數(shù)據(jù)集的格式轉(zhuǎn)換3.3全局參數(shù)配置（lenet_totxt）3.4 LeNet網(wǎng)絡(luò)（lenet_train_totxt）layer name: mnist type: Data transform_param scale: 0.00390625 data_param source:

3、mnist_train_lmdb backend: LMDB batch_size: 64 top: data top: labellayer name: conv1 type: Convolution 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 bottom: data top: conv13.5 訓(xùn)練與測試I0614 00:40:08.29108

4、2 7064 sgd_solver.cpp:106 Iteration 100, lr = 0.00992565I0614 00:40:17.023165 7064 solver.cpp:228 Iteration 200, loss = 0.143513I0614 00:40:17.023233 7064 solver.cpp:244 Train net output #0: loss = 0.143513 (* 1 = 0.143513 loss)I0614 00:40:17.023252 7064 sgd_solver.cpp:106 Iteration 200, lr = 0.0098

5、5258I0614 00:40:25.631428 7064 solver.cpp:228 Iteration 300, loss = 0.162749I0614 00:40:25.631542 7064 solver.cpp:244 Train net output #0: loss = 0.162749 (* 1 = 0.162749 loss)I0614 00:40:25.631561 7064 sgd_solver.cpp:106 Iteration 300, lr = 0.00978075I0614 00:40:34.705716 7064 solver.cpp:228 Iteration 400, loss = 0.0588523I0614 00:40:34.705777 7064 solver.cpp:244 Train net output #0: loss = 0.0588523 (* 1 = 0.0588523 loss)mnist訓(xùn)練時(shí)間約20min4 caffe的應(yīng)用實(shí)例識別一張動物圖片采用已訓(xùn)練好模型： ImageNet d o g測試對象：Model Zoo測試結(jié)果：博美犬威爾士柯基犬碟耳長毛玩賞小狗吉娃娃（一種產(chǎn)于墨西哥的狗）未成功安裝GPU驅(qū)動程序，模型的訓(xùn)練是通過CPU進(jìn)行運(yùn)算未深入研究caf