Windows安装mxnet

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安装MXNET

由于公司需要，近期需要快速精通mxnet，接下来的几个星期会陆续更新关于mxnet的笔记，提供参考和备忘。第一篇介绍mxnet的安装，mxnet的安装过程十分蛋疼，个人也是摸索了许久才安装成功，期间也是遇到了各种奇奇怪怪的坑，为了避免新人少走弯路，遂将经验总结于此。

windows上的安装

本人机器配置为Win10 + Cuda 7.5，后续的安装以此为准。 1.mxnet需要VS2013支持C++ 11特性在Visual C++ Compiler Nov 2013 CTP下载C++ 11版本的编译器，接着将C:\Program Files (x86)\Microsoft Visual C++ Compiler Nov 2013 CTP下所有同名目录中的文件覆盖到C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC下所有同名目录下对应的文件（覆盖前记得备份） 2.从github克隆源码 git clone --recursive https://github.com/dmlc/mxnet 这里提醒注意一定不要忘记--recursive参数，因为mxnet依赖于DMLC通用工具包，--recursive参数可以自动加载mshadow等依赖。 3.用Cmake生成项目工程文件，并编译项目打开cmake，Where is the source code栏里打开刚才下好的mxnet源代码目录，Where to build the binaries栏里指定生成工程文件和编译结果的路径，这里我填的是C:/mxnet/build，如图所示：接着点击configure，生成配置。

然后我们点击generate，生成.sln项目文件找到生成的工程文件mxnet.sln，用vs2013打开

最后，我们在项目mxnet上点击右键->生成，开始编译。

经过漫长的等待后，mxnet终于编译完成。

编译完成后，在C:\mxnet\build\Release目录下会生成三个文件：libmxnet.dll，libmxnet.exp，libmxnet.lib。 4.安装mxnet的python接口接下来我们到mxnet的源代码目录：G:\OpenSource\mxnet\python，运行

python setup.py install

来安装mxnet的python包。

我们将libmxnet.dll 接着，导入mxnet的时候发生了如下的错误：

通过调试发现问题出在打开libmxnet.dll的时候，问题应该出在没有导入依赖的dll文件，但蛋疼的是我也不知道它到底依赖哪一些dll文件。 5.安装依赖通过一番搜索，我找到一个名为dependency walker的软件，用它打开libmxnet.dll，我们看到还缺少的dll文件有哪些（图中的问号）

这些均能dll在mxnet的release tab下找到，下载完成后将其解压到mxnet的pthon安装目录C:\Anaconda2\Lib\site-packages\mxnet-0.7.0-py2.7.egg\mxnet下。将这些文件放入目录后，我们测试一下能不能导入

mxnet version is:0.7.0

上面的代码中，我们需要手动的载入mxnet依赖的动态链接库才能导入，目前还不清楚为什么它不会自动载入，这个问题留待以后解决，目前可以先把上段代码加入到mxnet的初始化代码中。接着我们跑一跑examples/image-classification/train_mnist这个例子

import ctypes ctypes._dlopen(r"C:\Anaconda2\Lib\site-packages\mxnet-0.7.0-py2.7.egg\mxnet\cudart64_75.dll") ctypes._dlopen(r"C:\Anaconda2\Lib\site-packages\mxnet-0.7.0-py2.7.egg\mxnet\cublas64_75.dll") ctypes._dlopen(r"cudnn64_5.dll") ctypes._dlopen(r"C:\Anaconda2\Lib\site-packages\mxnet-0.7.0-py2.7.egg\mxnet\libopenblas.dll") ctypes._dlopen(r"C:\Anaconda2\Lib\site-packages\mxnet-0.7.0-py2.7.egg\mxnet\opencv_world300.dll") ctypes._dlopen(r"C:\Anaconda2\Lib\site-packages\mxnet-0.7.0-py2.7.egg\mxnet\opencv_core2413.dll") ctypes._dlopen(r"vcomp120.dll") ctypes._dlopen(r"kernel32.dll") import mxnet as mx import argparse import os, sys import loggingdef _download(data_dir): if not os.path.isdir(data_dir): os.system("mkdir " + data_dir) os.chdir(data_dir) if (not os.path.exists('train-images-idx3-ubyte')) or \ (not os.path.exists('train-labels-idx1-ubyte')) or \ (not os.path.exists('t10k-images-idx3-ubyte')) or \ (not os.path.exists('t10k-labels-idx1-ubyte')): os.system("wget http://data.dmlc.ml/mxnet/data/mnist.zip") os.system("unzip -u mnist.zip; rm mnist.zip") os.chdir("..")def get_loc(data, attr={'lr_mult':'0.01'}): """ the localisation network in lenet-stn, it will increase acc about more than 1%, when num-epoch >=15 """ loc = mx.symbol.Convolution(data=data, num_filter=30, kernel=(5, 5), stride=(2,2)) loc = mx.symbol.Activation(data = loc, act_type='relu') loc = mx.symbol.Pooling(data=loc, kernel=(2, 2), stride=(2, 2), pool_type='max') loc = mx.symbol.Convolution(data=loc, num_filter=60, kernel=(3, 3), stride=(1,1), pad=(1, 1)) loc = mx.symbol.Activation(data = loc, act_type='relu') loc = mx.symbol.Pooling(data=loc, global_pool=True, kernel=(2, 2), pool_type='avg') loc = mx.symbol.Flatten(data=loc) loc = mx.symbol.FullyConnected(data=loc, num_hidden=6, name="stn_loc", attr=attr) return locdef get_mlp(): """ multi-layer perceptron """ data = mx.symbol.Variable('data') fc1 = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128) act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu") fc2 = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64) act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type="relu") fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10) mlp = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax') return mlpdef get_lenet(add_stn=False): """ LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE (1998) """ data = mx.symbol.Variable('data') if(add_stn): data = mx.sym.SpatialTransformer(data=data, loc=get_loc(data), target_shape = (28,28), transform_type="affine", sampler_type="bilinear") # first conv conv1 = mx.symbol.Convolution(data=data, kernel=(5,5), num_filter=20) tanh1 = mx.symbol.Activation(data=conv1, act_type="tanh") pool1 = mx.symbol.Pooling(data=tanh1, pool_type="max", kernel=(2,2), stride=(2,2)) # second conv conv2 = mx.symbol.Convolution(data=pool1, kernel=(5,5), num_filter=50) tanh2 = mx.symbol.Activation(data=conv2, act_type="tanh") pool2 = mx.symbol.Pooling(data=tanh2, pool_type="max", kernel=(2,2), stride=(2,2)) # first fullc flatten = mx.symbol.Flatten(data=pool2) fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500) tanh3 = mx.symbol.Activation(data=fc1, act_type="tanh") # second fullc fc2 = mx.symbol.FullyConnected(data=tanh3, num_hidden=10) # loss lenet = mx.symbol.SoftmaxOutput(data=fc2, name='softmax') return lenetdef get_iterator(data_shape): def get_iterator_impl(args, kv): data_dir = args.data_dir """ if '://' not in args.data_dir: _download(args.data_dir) """ flat = False if len(data_shape) == 3 else True train = mx.io.MNISTIter( image = data_dir + "train-images-idx3-ubyte", label = data_dir + "train-labels-idx1-ubyte", input_shape = data_shape, batch_size = args.batch_size, shuffle = True, flat = flat, num_parts = kv.num_workers, part_index = kv.rank) val = mx.io.MNISTIter( image = data_dir + "t10k-images-idx3-ubyte", label = data_dir + "t10k-labels-idx1-ubyte", input_shape = data_shape, batch_size = args.batch_size, flat = flat, num_parts = kv.num_workers, part_index = kv.rank) return (train, val) return get_iterator_impldef parse_args(): parser = argparse.ArgumentParser(description='train an image classifer on mnist') parser.add_argument('--network', type=str, default='mlp', choices = ['mlp', 'lenet', 'lenet-stn'], help = 'the cnn to use') parser.add_argument('--data-dir', type=str, default='mnist/', help='the input data directory') parser.add_argument('--gpus', type=str, help='the gpus will be used, e.g "0,1,2,3"') parser.add_argument('--num-examples', type=int, default=60000, help='the number of training examples') parser.add_argument('--batch-size', type=int, default=128, help='the batch size') parser.add_argument('--lr', type=float, default=.1, help='the initial learning rate') parser.add_argument('--model-prefix', type=str, help='the prefix of the model to load/save') parser.add_argument('--save-model-prefix', type=str, help='the prefix of the model to save') parser.add_argument('--num-epochs', type=int, default=10, help='the number of training epochs') parser.add_argument('--load-epoch', type=int, help="load the model on an epoch using the model-prefix") parser.add_argument('--kv-store', type=str, default='local', help='the kvstore type') parser.add_argument('--lr-factor', type=float, default=1, help='times the lr with a factor for every lr-factor-epoch epoch') parser.add_argument('--lr-factor-epoch', type=float, default=1, help='the number of epoch to factor the lr, could be .5') return parser.parse_args(['--gpus', '0', '--data-dir', 'G:/OpenSource/mxnet/example/image-classification/mnist/'])if __name__ == '__main__': args = parse_args() if args.network == 'mlp': data_shape = (784, ) net = get_mlp() elif args.network == 'lenet-stn': data_shape = (1, 28, 28) net = get_lenet(True) else: data_shape = (1, 28, 28) net = get_lenet() # kvstore kv = mx.kvstore.create(args.kv_store) # logging head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s' logger = logging.getLogger() formatter = logging.Formatter(head) stdout_handler = logging.StreamHandler(sys.stdout) stdout_handler.setFormatter(formatter) logger.addHandler(stdout_handler) logger.setLevel(logging.INFO) logger.info('start with arguments %s', args) # load model model_prefix = args.model_prefix if model_prefix is not None: model_prefix += "-%d" % (kv.rank) model_args = {} if args.load_epoch is not None: assert model_prefix is not None tmp = mx.model.FeedForward.load(model_prefix, args.load_epoch) model_args = {'arg_params' : tmp.arg_params, 'aux_params' : tmp.aux_params, 'begin_epoch' : args.load_epoch} # save model save_model_prefix = args.save_model_prefix if save_model_prefix is None: save_model_prefix = model_prefix checkpoint = None if save_model_prefix is None else mx.callback.do_checkpoint(save_model_prefix) # data (train, val) = get_iterator(data_shape)(args, kv) # train devs = mx.cpu() if args.gpus is None else [ mx.gpu(int(i)) for i in args.gpus.split(',')] epoch_size = args.num_examples / args.batch_size if args.kv_store == 'dist_sync': epoch_size /= kv.num_workers model_args['epoch_size'] = epoch_size if 'lr_factor' in args and args.lr_factor < 1: model_args['lr_scheduler'] = mx.lr_scheduler.FactorScheduler( step = max(int(epoch_size * args.lr_factor_epoch), 1), factor = args.lr_factor) if 'clip_gradient' in args and args.clip_gradient is not None: model_args['clip_gradient'] = args.clip_gradient # disable kvstore for single device if 'local' in kv.type and ( args.gpus is None or len(args.gpus.split(',')) is 1): kv = None model = mx.model.FeedForward( ctx = devs, symbol = net, num_epoch = args.num_epochs, learning_rate = args.lr, momentum = 0.9, wd = 0.00001, initializer = mx.init.Xavier(factor_type="in", magnitude=2.34), **model_args) eval_metrics = ['accuracy'] ## TopKAccuracy only allows top_k > 1 for top_k in [5, 10, 20]: eval_metrics.append(mx.metric.create('top_k_accuracy', top_k = top_k)) model.fit( X = train, eval_data = val, eval_metric = eval_metrics, kvstore = kv, batch_end_callback = [mx.callback.Speedometer(args.batch_size, 50)], epoch_end_callback = checkpoint)

INFO:root:start with arguments Namespace(batch_size=128, data_dir='G:/OpenSource/mxnet/example/image-classification/mnist/', gpus='0', kv_store='local', load_epoch=None, lr=0.1, lr_factor=1, lr_factor_epoch=1, model_prefix=None, network='mlp', num_epochs=10, num_examples=60000, save_model_prefix=None)2016-10-26 19:37:38,994 Node[0] start with arguments Namespace(batch_size=128, data_dir='G:/OpenSource/mxnet/example/image-classification/mnist/', gpus='0', kv_store='local', load_epoch=None, lr=0.1, lr_factor=1, lr_factor_epoch=1, model_prefix=None, network='mlp', num_epochs=10, num_examples=60000, save_model_prefix=None)INFO:root:Start training with [gpu(0)]2016-10-26 19:37:42,038 Node[0] Start training with [gpu(0)]INFO:root:Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-accuracy=0.6873442016-10-26 19:37:45,351 Node[0] Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-accuracy=0.687344INFO:root:Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-top_k_accuracy_5=0.9359372016-10-26 19:37:45,354 Node[0] Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-top_k_accuracy_5=0.935937INFO:root:Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:45,357 Node[0] Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:45,361 Node[0] Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[0] Batch [100] Speed: 22535.22 samples/sec Train-accuracy=0.8971882016-10-26 19:37:45,648 Node[0] Epoch[0] Batch [100] Speed: 22535.22 samples/sec Train-accuracy=0.897188INFO:root:Epoch[0] Batch [100] Speed: 22535.22 samples/sec Train-top_k_accuracy_5=0.9928122016-10-26 19:37:45,650 Node[0] Epoch[0] Batch [100] Speed: 22535.22 samples/sec Train-top_k_accuracy_5=0.992812INFO:root:Epoch[0] Batch [100] Speed: 22535.22 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:45,651 Node[0] Epoch[0] Batch [100] Speed: 22535.22 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[0] Batch [100] Speed: 22535.22 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:45,654 Node[0] Epoch[0] Batch [100] Speed: 22535.22 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[0] Batch [150] Speed: 23443.22 samples/sec Train-accuracy=0.9196872016-10-26 19:37:45,930 Node[0] Epoch[0] Batch [150] Speed: 23443.22 samples/sec Train-accuracy=0.919687INFO:root:Epoch[0] Batch [150] Speed: 23443.22 samples/sec Train-top_k_accuracy_5=0.9954692016-10-26 19:37:45,930 Node[0] Epoch[0] Batch [150] Speed: 23443.22 samples/sec Train-top_k_accuracy_5=0.995469INFO:root:Epoch[0] Batch [150] Speed: 23443.22 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:45,933 Node[0] Epoch[0] Batch [150] Speed: 23443.22 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[0] Batch [150] Speed: 23443.22 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:45,934 Node[0] Epoch[0] Batch [150] Speed: 23443.22 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[0] Batch [200] Speed: 24150.96 samples/sec Train-accuracy=0.9276562016-10-26 19:37:46,200 Node[0] Epoch[0] Batch [200] Speed: 24150.96 samples/sec Train-accuracy=0.927656INFO:root:Epoch[0] Batch [200] Speed: 24150.96 samples/sec Train-top_k_accuracy_5=0.9970312016-10-26 19:37:46,203 Node[0] Epoch[0] Batch [200] Speed: 24150.96 samples/sec Train-top_k_accuracy_5=0.997031INFO:root:Epoch[0] Batch [200] Speed: 24150.96 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:46,206 Node[0] Epoch[0] Batch [200] Speed: 24150.96 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[0] Batch [200] Speed: 24150.96 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:46,210 Node[0] Epoch[0] Batch [200] Speed: 24150.96 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[0] Batch [250] Speed: 22145.33 samples/sec Train-accuracy=0.9420312016-10-26 19:37:46,502 Node[0] Epoch[0] Batch [250] Speed: 22145.33 samples/sec Train-accuracy=0.942031INFO:root:Epoch[0] Batch [250] Speed: 22145.33 samples/sec Train-top_k_accuracy_5=0.9968752016-10-26 19:37:46,503 Node[0] Epoch[0] Batch [250] Speed: 22145.33 samples/sec Train-top_k_accuracy_5=0.996875INFO:root:Epoch[0] Batch [250] Speed: 22145.33 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:46,509 Node[0] Epoch[0] Batch [250] Speed: 22145.33 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[0] Batch [250] Speed: 22145.33 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:46,513 Node[0] Epoch[0] Batch [250] Speed: 22145.33 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[0] Batch [300] Speed: 25600.00 samples/sec Train-accuracy=0.9407812016-10-26 19:37:46,766 Node[0] Epoch[0] Batch [300] Speed: 25600.00 samples/sec Train-accuracy=0.940781INFO:root:Epoch[0] Batch [300] Speed: 25600.00 samples/sec Train-top_k_accuracy_5=0.9976562016-10-26 19:37:46,767 Node[0] Epoch[0] Batch [300] Speed: 25600.00 samples/sec Train-top_k_accuracy_5=0.997656INFO:root:Epoch[0] Batch [300] Speed: 25600.00 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:46,769 Node[0] Epoch[0] Batch [300] Speed: 25600.00 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[0] Batch [300] Speed: 25600.00 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:46,770 Node[0] Epoch[0] Batch [300] Speed: 25600.00 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[0] Batch [350] Speed: 25497.99 samples/sec Train-accuracy=0.9437502016-10-26 19:37:47,025 Node[0] Epoch[0] Batch [350] Speed: 25497.99 samples/sec Train-accuracy=0.943750INFO:root:Epoch[0] Batch [350] Speed: 25497.99 samples/sec Train-top_k_accuracy_5=0.9985942016-10-26 19:37:47,026 Node[0] Epoch[0] Batch [350] Speed: 25497.99 samples/sec Train-top_k_accuracy_5=0.998594INFO:root:Epoch[0] Batch [350] Speed: 25497.99 samples/sec 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19:37:47,634 Node[0] Epoch[0] Resetting Data IteratorINFO:root:Epoch[0] Time cost=3.1002016-10-26 19:37:47,637 Node[0] Epoch[0] Time cost=3.100INFO:root:Epoch[0] Validation-accuracy=0.9600362016-10-26 19:37:47,826 Node[0] Epoch[0] Validation-accuracy=0.960036INFO:root:Epoch[0] Validation-top_k_accuracy_5=0.9986982016-10-26 19:37:47,828 Node[0] Epoch[0] Validation-top_k_accuracy_5=0.998698INFO:root:Epoch[0] Validation-top_k_accuracy_10=1.0000002016-10-26 19:37:47,829 Node[0] Epoch[0] Validation-top_k_accuracy_10=1.000000INFO:root:Epoch[0] Validation-top_k_accuracy_20=1.0000002016-10-26 19:37:47,832 Node[0] Epoch[0] Validation-top_k_accuracy_20=1.000000INFO:root:Epoch[1] Batch [50] Speed: 25806.46 samples/sec Train-accuracy=0.9551562016-10-26 19:37:48,085 Node[0] Epoch[1] Batch [50] Speed: 25806.46 samples/sec Train-accuracy=0.955156INFO:root:Epoch[1] Batch [50] Speed: 25806.46 samples/sec Train-top_k_accuracy_5=0.9985942016-10-26 19:37:48,088 Node[0] Epoch[1] Batch [50] Speed: 25806.46 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samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[1] Batch [450] Speed: 26229.51 samples/sec Train-accuracy=0.9695312016-10-26 19:37:50,099 Node[0] Epoch[1] Batch [450] Speed: 26229.51 samples/sec Train-accuracy=0.969531INFO:root:Epoch[1] Batch [450] Speed: 26229.51 samples/sec Train-top_k_accuracy_5=0.9998442016-10-26 19:37:50,101 Node[0] Epoch[1] Batch [450] Speed: 26229.51 samples/sec Train-top_k_accuracy_5=0.999844INFO:root:Epoch[1] Batch [450] Speed: 26229.51 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:50,105 Node[0] Epoch[1] Batch [450] Speed: 26229.51 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[1] Batch [450] Speed: 26229.51 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:50,109 Node[0] Epoch[1] Batch [450] Speed: 26229.51 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[1] Resetting Data Iterator2016-10-26 19:37:50,196 Node[0] Epoch[1] Resetting Data IteratorINFO:root:Epoch[1] Time cost=2.3642016-10-26 19:37:50,197 Node[0] Epoch[1] Time cost=2.364INFO:root:Epoch[1] Validation-accuracy=0.9683492016-10-26 19:37:50,381 Node[0] Epoch[1] Validation-accuracy=0.968349INFO:root:Epoch[1] Validation-top_k_accuracy_5=0.9990992016-10-26 19:37:50,382 Node[0] Epoch[1] Validation-top_k_accuracy_5=0.999099INFO:root:Epoch[1] Validation-top_k_accuracy_10=1.0000002016-10-26 19:37:50,384 Node[0] Epoch[1] Validation-top_k_accuracy_10=1.000000INFO:root:Epoch[1] Validation-top_k_accuracy_20=1.0000002016-10-26 19:37:50,385 Node[0] Epoch[1] Validation-top_k_accuracy_20=1.000000INFO:root:Epoch[2] Batch [50] Speed: 27004.22 samples/sec Train-accuracy=0.9718752016-10-26 19:37:50,635 Node[0] Epoch[2] Batch [50] Speed: 27004.22 samples/sec Train-accuracy=0.971875INFO:root:Epoch[2] Batch [50] Speed: 27004.22 samples/sec Train-top_k_accuracy_5=0.9992192016-10-26 19:37:50,638 Node[0] Epoch[2] Batch [50] Speed: 27004.22 samples/sec Train-top_k_accuracy_5=0.999219INFO:root:Epoch[2] Batch [50] Speed: 27004.22 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Train-accuracy=0.9771872016-10-26 19:37:52,647 Node[0] Epoch[2] Batch [450] Speed: 25196.83 samples/sec Train-accuracy=0.977187INFO:root:Epoch[2] Batch [450] Speed: 25196.83 samples/sec Train-top_k_accuracy_5=0.9996872016-10-26 19:37:52,648 Node[0] Epoch[2] Batch [450] Speed: 25196.83 samples/sec Train-top_k_accuracy_5=0.999687INFO:root:Epoch[2] Batch [450] Speed: 25196.83 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:52,650 Node[0] Epoch[2] Batch [450] Speed: 25196.83 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[2] Batch [450] Speed: 25196.83 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:52,651 Node[0] Epoch[2] Batch [450] Speed: 25196.83 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[2] Resetting Data Iterator2016-10-26 19:37:52,736 Node[0] Epoch[2] Resetting Data IteratorINFO:root:Epoch[2] Time cost=2.3452016-10-26 19:37:52,737 Node[0] Epoch[2] Time cost=2.345INFO:root:Epoch[2] Validation-accuracy=0.9738582016-10-26 19:37:52,903 Node[0] Epoch[2] Validation-accuracy=0.973858INFO:root:Epoch[2] Validation-top_k_accuracy_5=0.9990992016-10-26 19:37:52,905 Node[0] Epoch[2] Validation-top_k_accuracy_5=0.999099INFO:root:Epoch[2] Validation-top_k_accuracy_10=1.0000002016-10-26 19:37:52,907 Node[0] Epoch[2] Validation-top_k_accuracy_10=1.000000INFO:root:Epoch[2] Validation-top_k_accuracy_20=1.0000002016-10-26 19:37:52,910 Node[0] Epoch[2] Validation-top_k_accuracy_20=1.000000INFO:root:Epoch[3] Batch [50] Speed: 27705.61 samples/sec Train-accuracy=0.9779692016-10-26 19:37:53,147 Node[0] Epoch[3] Batch [50] Speed: 27705.61 samples/sec Train-accuracy=0.977969INFO:root:Epoch[3] Batch [50] Speed: 27705.61 samples/sec Train-top_k_accuracy_5=0.9998442016-10-26 19:37:53,148 Node[0] Epoch[3] Batch [50] Speed: 27705.61 samples/sec Train-top_k_accuracy_5=0.999844INFO:root:Epoch[3] Batch [50] Speed: 27705.61 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:53,151 Node[0] Epoch[3] Batch [50] Speed: 27705.61 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samples/sec Train-accuracy=0.980781INFO:root:Epoch[3] Batch [200] Speed: 26229.51 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:37:53,895 Node[0] Epoch[3] Batch [200] Speed: 26229.51 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[3] Batch [200] Speed: 26229.51 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:53,898 Node[0] Epoch[3] Batch [200] Speed: 26229.51 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[3] Batch [200] Speed: 26229.51 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:53,900 Node[0] Epoch[3] Batch [200] Speed: 26229.51 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[3] Batch [250] Speed: 26890.77 samples/sec Train-accuracy=0.9795312016-10-26 19:37:54,141 Node[0] Epoch[3] Batch [250] Speed: 26890.77 samples/sec Train-accuracy=0.979531INFO:root:Epoch[3] Batch [250] Speed: 26890.77 samples/sec Train-top_k_accuracy_5=0.9996872016-10-26 19:37:54,142 Node[0] Epoch[3] Batch [250] Speed: 26890.77 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samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[3] Batch [400] Speed: 27004.22 samples/sec Train-accuracy=0.9826562016-10-26 19:37:54,862 Node[0] Epoch[3] Batch [400] Speed: 27004.22 samples/sec Train-accuracy=0.982656INFO:root:Epoch[3] Batch [400] Speed: 27004.22 samples/sec Train-top_k_accuracy_5=0.9995312016-10-26 19:37:54,865 Node[0] Epoch[3] Batch [400] Speed: 27004.22 samples/sec Train-top_k_accuracy_5=0.999531INFO:root:Epoch[3] Batch [400] Speed: 27004.22 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:54,867 Node[0] Epoch[3] Batch [400] Speed: 27004.22 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[3] Batch [400] Speed: 27004.22 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:54,868 Node[0] Epoch[3] Batch [400] Speed: 27004.22 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[3] Batch [450] Speed: 27586.19 samples/sec Train-accuracy=0.9810942016-10-26 19:37:55,101 Node[0] Epoch[3] Batch [450] Speed: 27586.19 samples/sec Train-accuracy=0.981094INFO:root:Epoch[3] Batch [450] Speed: 27586.19 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:37:55,104 Node[0] Epoch[3] Batch [450] Speed: 27586.19 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[3] Batch [450] Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:55,105 Node[0] Epoch[3] Batch [450] Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[3] Batch [450] Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:55,107 Node[0] Epoch[3] Batch [450] Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[3] Resetting Data Iterator2016-10-26 19:37:55,191 Node[0] Epoch[3] Resetting Data IteratorINFO:root:Epoch[3] Time cost=2.2832016-10-26 19:37:55,194 Node[0] Epoch[3] Time cost=2.283INFO:root:Epoch[3] Validation-accuracy=0.9743592016-10-26 19:37:55,359 Node[0] Epoch[3] Validation-accuracy=0.974359INFO:root:Epoch[3] Validation-top_k_accuracy_5=0.9991992016-10-26 19:37:55,361 Node[0] Epoch[3] Validation-top_k_accuracy_5=0.999199INFO:root:Epoch[3] Validation-top_k_accuracy_10=1.0000002016-10-26 19:37:55,362 Node[0] Epoch[3] Validation-top_k_accuracy_10=1.000000INFO:root:Epoch[3] Validation-top_k_accuracy_20=1.0000002016-10-26 19:37:55,364 Node[0] Epoch[3] Validation-top_k_accuracy_20=1.000000INFO:root:Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-accuracy=0.9809382016-10-26 19:37:55,601 Node[0] Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-accuracy=0.980938INFO:root:Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-top_k_accuracy_5=0.9998442016-10-26 19:37:55,605 Node[0] Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-top_k_accuracy_5=0.999844INFO:root:Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:55,608 Node[0] Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:55,611 Node[0] Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[4] Batch [100] Speed: 27586.19 samples/sec Train-accuracy=0.9814062016-10-26 19:37:55,844 Node[0] Epoch[4] Batch [100] Speed: 27586.19 samples/sec Train-accuracy=0.981406INFO:root:Epoch[4] Batch [100] Speed: 27586.19 samples/sec Train-top_k_accuracy_5=0.9996872016-10-26 19:37:55,845 Node[0] Epoch[4] Batch [100] Speed: 27586.19 samples/sec Train-top_k_accuracy_5=0.999687INFO:root:Epoch[4] Batch [100] Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:55,846 Node[0] Epoch[4] Batch [100] Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[4] Batch [100] Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:55,848 Node[0] Epoch[4] Batch [100] Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[4] Batch [150] Speed: 27118.64 samples/sec Train-accuracy=0.9818752016-10-26 19:37:56,085 Node[0] Epoch[4] Batch [150] Speed: 27118.64 samples/sec Train-accuracy=0.981875INFO:root:Epoch[4] Batch [150] Speed: 27118.64 samples/sec Train-top_k_accuracy_5=0.9996872016-10-26 19:37:56,088 Node[0] Epoch[4] Batch [150] Speed: 27118.64 samples/sec Train-top_k_accuracy_5=0.999687INFO:root:Epoch[4] Batch [150] Speed: 27118.64 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:56,089 Node[0] Epoch[4] Batch [150] Speed: 27118.64 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[4] Batch [150] Speed: 27118.64 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:56,091 Node[0] Epoch[4] Batch [150] Speed: 27118.64 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[4] Batch [200] Speed: 28571.43 samples/sec Train-accuracy=0.9871872016-10-26 19:37:56,315 Node[0] Epoch[4] Batch [200] Speed: 28571.43 samples/sec Train-accuracy=0.987187INFO:root:Epoch[4] Batch [200] Speed: 28571.43 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:37:56,318 Node[0] Epoch[4] Batch [200] Speed: 28571.43 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[4] Batch [200] Speed: 28571.43 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:56,319 Node[0] Epoch[4] Batch [200] Speed: 28571.43 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[4] Batch [200] Speed: 28571.43 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:56,321 Node[0] Epoch[4] Batch [200] Speed: 28571.43 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[4] Batch [250] Speed: 27350.43 samples/sec Train-accuracy=0.9828122016-10-26 19:37:56,555 Node[0] Epoch[4] Batch [250] Speed: 27350.43 samples/sec Train-accuracy=0.982812INFO:root:Epoch[4] Batch [250] Speed: 27350.43 samples/sec Train-top_k_accuracy_5=0.9995312016-10-26 19:37:56,558 Node[0] Epoch[4] Batch [250] Speed: 27350.43 samples/sec Train-top_k_accuracy_5=0.999531INFO:root:Epoch[4] Batch [250] Speed: 27350.43 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samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:56,786 Node[0] Epoch[4] Batch [300] Speed: 29493.09 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[4] Batch [350] Speed: 26016.25 samples/sec Train-accuracy=0.9815632016-10-26 19:37:57,036 Node[0] Epoch[4] Batch [350] Speed: 26016.25 samples/sec Train-accuracy=0.981563INFO:root:Epoch[4] Batch [350] Speed: 26016.25 samples/sec Train-top_k_accuracy_5=0.9995312016-10-26 19:37:57,039 Node[0] Epoch[4] Batch [350] Speed: 26016.25 samples/sec Train-top_k_accuracy_5=0.999531INFO:root:Epoch[4] Batch [350] Speed: 26016.25 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:57,040 Node[0] Epoch[4] Batch [350] Speed: 26016.25 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[4] Batch [350] Speed: 26016.25 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:57,042 Node[0] Epoch[4] Batch [350] Speed: 26016.25 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[4] Batch [400] Speed: 27118.66 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Validation-top_k_accuracy_5=0.999299INFO:root:Epoch[4] Validation-top_k_accuracy_10=1.0000002016-10-26 19:37:57,779 Node[0] Epoch[4] Validation-top_k_accuracy_10=1.000000INFO:root:Epoch[4] Validation-top_k_accuracy_20=1.0000002016-10-26 19:37:57,779 Node[0] Epoch[4] Validation-top_k_accuracy_20=1.000000INFO:root:Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-accuracy=0.9842192016-10-26 19:37:58,019 Node[0] Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-accuracy=0.984219INFO:root:Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-top_k_accuracy_5=0.9996872016-10-26 19:37:58,022 Node[0] Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-top_k_accuracy_5=0.999687INFO:root:Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:58,023 Node[0] Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:58,025 Node[0] Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[5] Batch [100] Speed: 28193.83 samples/sec Train-accuracy=0.9873442016-10-26 19:37:58,253 Node[0] Epoch[5] Batch [100] Speed: 28193.83 samples/sec Train-accuracy=0.987344INFO:root:Epoch[5] Batch [100] Speed: 28193.83 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:37:58,255 Node[0] Epoch[5] Batch [100] Speed: 28193.83 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[5] Batch [100] Speed: 28193.83 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:58,256 Node[0] Epoch[5] Batch [100] Speed: 28193.83 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[5] Batch [100] Speed: 28193.83 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:58,257 Node[0] Epoch[5] Batch [100] Speed: 28193.83 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[5] Batch [150] Speed: 27947.62 samples/sec Train-accuracy=0.9860942016-10-26 19:37:58,487 Node[0] Epoch[5] Batch [150] Speed: 27947.62 samples/sec Train-accuracy=0.986094INFO:root:Epoch[5] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_5=0.9995312016-10-26 19:37:58,489 Node[0] Epoch[5] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_5=0.999531INFO:root:Epoch[5] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:58,490 Node[0] Epoch[5] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[5] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:58,492 Node[0] Epoch[5] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[5] Batch [200] Speed: 28828.84 samples/sec Train-accuracy=0.9870312016-10-26 19:37:58,714 Node[0] Epoch[5] Batch [200] Speed: 28828.84 samples/sec Train-accuracy=0.987031INFO:root:Epoch[5] Batch [200] Speed: 28828.84 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:37:58,717 Node[0] Epoch[5] Batch [200] Speed: 28828.84 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[5] Batch [200] Speed: 28828.84 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:58,719 Node[0] Epoch[5] Batch [200] Speed: 28828.84 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[5] Batch [200] Speed: 28828.84 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:58,720 Node[0] Epoch[5] Batch [200] Speed: 28828.84 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[5] Batch [250] Speed: 28571.40 samples/sec Train-accuracy=0.9845312016-10-26 19:37:58,946 Node[0] Epoch[5] Batch [250] Speed: 28571.40 samples/sec Train-accuracy=0.984531INFO:root:Epoch[5] Batch [250] Speed: 28571.40 samples/sec Train-top_k_accuracy_5=0.9998442016-10-26 19:37:58,947 Node[0] Epoch[5] Batch [250] Speed: 28571.40 samples/sec Train-top_k_accuracy_5=0.999844INFO:root:Epoch[5] Batch [250] Speed: 28571.40 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:58,950 Node[0] Epoch[5] Batch [250] Speed: 28571.40 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[5] Batch [250] Speed: 28571.40 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:58,953 Node[0] Epoch[5] Batch [250] Speed: 28571.40 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[5] Batch [300] Speed: 27826.08 samples/sec Train-accuracy=0.9854692016-10-26 19:37:59,184 Node[0] Epoch[5] Batch [300] Speed: 27826.08 samples/sec Train-accuracy=0.985469INFO:root:Epoch[5] Batch [300] Speed: 27826.08 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:37:59,187 Node[0] Epoch[5] Batch [300] Speed: 27826.08 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[5] Batch [300] Speed: 27826.08 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:59,187 Node[0] Epoch[5] Batch [300] Speed: 27826.08 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[5] Batch [300] Speed: 27826.08 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:59,188 Node[0] Epoch[5] Batch [300] Speed: 27826.08 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[5] Batch [350] Speed: 28444.46 samples/sec Train-accuracy=0.9831252016-10-26 19:37:59,415 Node[0] Epoch[5] Batch [350] Speed: 28444.46 samples/sec Train-accuracy=0.983125INFO:root:Epoch[5] Batch [350] Speed: 28444.46 samples/sec Train-top_k_accuracy_5=0.9998442016-10-26 19:37:59,417 Node[0] Epoch[5] Batch [350] Speed: 28444.46 samples/sec Train-top_k_accuracy_5=0.999844INFO:root:Epoch[5] Batch [350] Speed: 28444.46 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:59,418 Node[0] Epoch[5] Batch [350] Speed: 28444.46 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[5] Batch [350] Speed: 28444.46 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:59,421 Node[0] Epoch[5] Batch [350] Speed: 28444.46 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[5] Batch [400] Speed: 28318.58 samples/sec Train-accuracy=0.9875002016-10-26 19:37:59,648 Node[0] Epoch[5] Batch [400] Speed: 28318.58 samples/sec Train-accuracy=0.987500INFO:root:Epoch[5] Batch [400] Speed: 28318.58 samples/sec Train-top_k_accuracy_5=0.9996872016-10-26 19:37:59,650 Node[0] Epoch[5] Batch [400] Speed: 28318.58 samples/sec Train-top_k_accuracy_5=0.999687INFO:root:Epoch[5] Batch [400] Speed: 28318.58 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:59,651 Node[0] Epoch[5] Batch [400] Speed: 28318.58 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[5] Batch [400] Speed: 28318.58 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:59,653 Node[0] Epoch[5] Batch [400] Speed: 28318.58 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[5] Batch [450] Speed: 28571.40 samples/sec Train-accuracy=0.9870312016-10-26 19:37:59,880 Node[0] Epoch[5] Batch [450] Speed: 28571.40 samples/sec Train-accuracy=0.987031INFO:root:Epoch[5] Batch [450] Speed: 28571.40 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:37:59,881 Node[0] Epoch[5] Batch [450] Speed: 28571.40 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[5] Batch [450] Speed: 28571.40 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:37:59,882 Node[0] Epoch[5] Batch [450] Speed: 28571.40 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[5] Batch [450] Speed: 28571.40 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:37:59,884 Node[0] Epoch[5] Batch [450] Speed: 28571.40 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[5] Resetting Data Iterator2016-10-26 19:37:59,973 Node[0] Epoch[5] Resetting Data IteratorINFO:root:Epoch[5] Time cost=2.1942016-10-26 19:37:59,974 Node[0] Epoch[5] Time cost=2.194INFO:root:Epoch[5] Validation-accuracy=0.9744592016-10-26 19:38:00,132 Node[0] Epoch[5] Validation-accuracy=0.974459INFO:root:Epoch[5] Validation-top_k_accuracy_5=0.9991992016-10-26 19:38:00,134 Node[0] Epoch[5] Validation-top_k_accuracy_5=0.999199INFO:root:Epoch[5] Validation-top_k_accuracy_10=1.0000002016-10-26 19:38:00,135 Node[0] Epoch[5] Validation-top_k_accuracy_10=1.000000INFO:root:Epoch[5] Validation-top_k_accuracy_20=1.0000002016-10-26 19:38:00,138 Node[0] Epoch[5] Validation-top_k_accuracy_20=1.000000INFO:root:Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-accuracy=0.9901562016-10-26 19:38:00,361 Node[0] Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-accuracy=0.990156INFO:root:Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:38:00,364 Node[0] Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:00,365 Node[0] Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:00,367 Node[0] Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[6] Batch [100] Speed: 26890.77 samples/sec Train-accuracy=0.9892192016-10-26 19:38:00,605 Node[0] Epoch[6] Batch [100] Speed: 26890.77 samples/sec Train-accuracy=0.989219INFO:root:Epoch[6] Batch [100] Speed: 26890.77 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:38:00,607 Node[0] Epoch[6] Batch [100] Speed: 26890.77 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[6] Batch [100] Speed: 26890.77 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:00,608 Node[0] Epoch[6] Batch [100] Speed: 26890.77 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[6] Batch [100] Speed: 26890.77 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:00,609 Node[0] Epoch[6] Batch [100] Speed: 26890.77 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[6] Batch [150] Speed: 27947.62 samples/sec Train-accuracy=0.9892192016-10-26 19:38:00,839 Node[0] Epoch[6] Batch [150] Speed: 27947.62 samples/sec Train-accuracy=0.989219INFO:root:Epoch[6] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:38:00,842 Node[0] Epoch[6] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[6] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:00,842 Node[0] Epoch[6] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[6] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:00,845 Node[0] Epoch[6] Batch [150] Speed: 27947.62 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[6] Batch [200] Speed: 26890.74 samples/sec Train-accuracy=0.9882812016-10-26 19:38:01,084 Node[0] Epoch[6] Batch [200] Speed: 26890.74 samples/sec Train-accuracy=0.988281INFO:root:Epoch[6] Batch [200] Speed: 26890.74 samples/sec Train-top_k_accuracy_5=0.9998442016-10-26 19:38:01,088 Node[0] Epoch[6] Batch [200] Speed: 26890.74 samples/sec Train-top_k_accuracy_5=0.999844INFO:root:Epoch[6] Batch [200] Speed: 26890.74 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:01,088 Node[0] Epoch[6] Batch [200] Speed: 26890.74 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[6] Batch [200] Speed: 26890.74 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:01,089 Node[0] Epoch[6] Batch [200] Speed: 26890.74 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[6] Batch [250] Speed: 27947.59 samples/sec Train-accuracy=0.9879692016-10-26 19:38:01,322 Node[0] Epoch[6] Batch [250] Speed: 27947.59 samples/sec Train-accuracy=0.987969INFO:root:Epoch[6] Batch [250] Speed: 27947.59 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:38:01,323 Node[0] Epoch[6] Batch [250] Speed: 27947.59 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[6] Batch [250] Speed: 27947.59 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:01,325 Node[0] Epoch[6] Batch [250] Speed: 27947.59 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[6] Batch [250] Speed: 27947.59 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:01,326 Node[0] Epoch[6] Batch [250] Speed: 27947.59 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[6] Batch [300] Speed: 28070.18 samples/sec Train-accuracy=0.9871872016-10-26 19:38:01,555 Node[0] Epoch[6] Batch [300] Speed: 28070.18 samples/sec Train-accuracy=0.987187INFO:root:Epoch[6] Batch [300] Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:38:01,558 Node[0] Epoch[6] Batch [300] Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[6] Batch [300] Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:01,559 Node[0] Epoch[6] Batch [300] Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[6] Batch [300] Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:01,561 Node[0] Epoch[6] Batch [300] Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[6] Batch [350] Speed: 27234.03 samples/sec Train-accuracy=0.9817192016-10-26 19:38:01,798 Node[0] Epoch[6] Batch [350] Speed: 27234.03 samples/sec Train-accuracy=0.981719INFO:root:Epoch[6] Batch [350] Speed: 27234.03 samples/sec Train-top_k_accuracy_5=0.9996872016-10-26 19:38:01,799 Node[0] Epoch[6] Batch [350] Speed: 27234.03 samples/sec Train-top_k_accuracy_5=0.999687INFO:root:Epoch[6] Batch [350] Speed: 27234.03 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:01,802 Node[0] Epoch[6] Batch [350] Speed: 27234.03 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[6] Batch [350] Speed: 27234.03 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:01,803 Node[0] Epoch[6] Batch [350] Speed: 27234.03 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[6] Batch [400] Speed: 28193.83 samples/sec Train-accuracy=0.9871872016-10-26 19:38:02,032 Node[0] Epoch[6] Batch [400] Speed: 28193.83 samples/sec Train-accuracy=0.987187INFO:root:Epoch[6] Batch [400] Speed: 28193.83 samples/sec Train-top_k_accuracy_5=0.9996872016-10-26 19:38:02,035 Node[0] Epoch[6] Batch [400] Speed: 28193.83 samples/sec Train-top_k_accuracy_5=0.999687INFO:root:Epoch[6] Batch [400] Speed: 28193.83 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:02,036 Node[0] Epoch[6] Batch [400] Speed: 28193.83 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[6] Batch [400] Speed: 28193.83 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:02,038 Node[0] Epoch[6] Batch [400] Speed: 28193.83 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[6] Batch [450] Speed: 28318.58 samples/sec Train-accuracy=0.9887502016-10-26 19:38:02,265 Node[0] Epoch[6] Batch [450] Speed: 28318.58 samples/sec Train-accuracy=0.988750INFO:root:Epoch[6] Batch [450] Speed: 28318.58 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:38:02,266 Node[0] Epoch[6] Batch [450] Speed: 28318.58 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[6] Batch [450] Speed: 28318.58 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:02,269 Node[0] Epoch[6] Batch [450] Speed: 28318.58 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[6] Batch [450] Speed: 28318.58 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:02,270 Node[0] Epoch[6] Batch [450] Speed: 28318.58 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[6] Resetting Data Iterator2016-10-26 19:38:02,349 Node[0] Epoch[6] Resetting Data IteratorINFO:root:Epoch[6] Time cost=2.2142016-10-26 19:38:02,354 Node[0] Epoch[6] Time cost=2.214INFO:root:Epoch[6] Validation-accuracy=0.9751602016-10-26 19:38:02,510 Node[0] Epoch[6] Validation-accuracy=0.975160INFO:root:Epoch[6] Validation-top_k_accuracy_5=0.9994992016-10-26 19:38:02,512 Node[0] Epoch[6] Validation-top_k_accuracy_5=0.999499INFO:root:Epoch[6] Validation-top_k_accuracy_10=1.0000002016-10-26 19:38:02,513 Node[0] Epoch[6] Validation-top_k_accuracy_10=1.000000INFO:root:Epoch[6] Validation-top_k_accuracy_20=1.0000002016-10-26 19:38:02,515 Node[0] Epoch[6] Validation-top_k_accuracy_20=1.000000INFO:root:Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-accuracy=0.9898442016-10-26 19:38:02,733 Node[0] Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-accuracy=0.989844INFO:root:Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-top_k_accuracy_5=0.9998442016-10-26 19:38:02,736 Node[0] Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-top_k_accuracy_5=0.999844INFO:root:Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:02,736 Node[0] Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:02,739 Node[0] Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[7] Batch [100] Speed: 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Train-top_k_accuracy_10=1.0000002016-10-26 19:38:04,601 Node[0] Epoch[7] Batch [450] Speed: 27826.08 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[7] Batch [450] Speed: 27826.08 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:04,604 Node[0] Epoch[7] Batch [450] Speed: 27826.08 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[7] Resetting Data Iterator2016-10-26 19:38:04,687 Node[0] Epoch[7] Resetting Data IteratorINFO:root:Epoch[7] Time cost=2.1722016-10-26 19:38:04,690 Node[0] Epoch[7] Time cost=2.172INFO:root:Epoch[7] Validation-accuracy=0.9775642016-10-26 19:38:04,842 Node[0] Epoch[7] Validation-accuracy=0.977564INFO:root:Epoch[7] Validation-top_k_accuracy_5=0.9995992016-10-26 19:38:04,845 Node[0] Epoch[7] Validation-top_k_accuracy_5=0.999599INFO:root:Epoch[7] Validation-top_k_accuracy_10=1.0000002016-10-26 19:38:04,845 Node[0] Epoch[7] Validation-top_k_accuracy_10=1.000000INFO:root:Epoch[7] Validation-top_k_accuracy_20=1.0000002016-10-26 19:38:04,848 Node[0] Epoch[7] Validation-top_k_accuracy_20=1.000000INFO:root:Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-accuracy=0.9904692016-10-26 19:38:05,069 Node[0] Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-accuracy=0.990469INFO:root:Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:38:05,072 Node[0] Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:05,072 Node[0] Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:05,075 Node[0] Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[8] Batch [100] Speed: 28699.55 samples/sec Train-accuracy=0.9885942016-10-26 19:38:05,299 Node[0] Epoch[8] Batch [100] Speed: 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samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[8] Batch [450] Speed: 27350.43 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:06,937 Node[0] Epoch[8] Batch [450] Speed: 27350.43 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[8] Resetting Data Iterator2016-10-26 19:38:07,016 Node[0] Epoch[8] Resetting Data IteratorINFO:root:Epoch[8] Time cost=2.1692016-10-26 19:38:07,017 Node[0] Epoch[8] Time cost=2.169INFO:root:Epoch[8] Validation-accuracy=0.9768632016-10-26 19:38:07,174 Node[0] Epoch[8] Validation-accuracy=0.976863INFO:root:Epoch[8] Validation-top_k_accuracy_5=0.9997002016-10-26 19:38:07,174 Node[0] Epoch[8] Validation-top_k_accuracy_5=0.999700INFO:root:Epoch[8] Validation-top_k_accuracy_10=1.0000002016-10-26 19:38:07,177 Node[0] Epoch[8] Validation-top_k_accuracy_10=1.000000INFO:root:Epoch[8] Validation-top_k_accuracy_20=1.0000002016-10-26 19:38:07,177 Node[0] Epoch[8] Validation-top_k_accuracy_20=1.000000INFO:root:Epoch[9] Batch [50] Speed: 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Train-top_k_accuracy_10=1.0000002016-10-26 19:38:07,875 Node[0] Epoch[9] Batch [150] Speed: 28444.46 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[9] Batch [150] Speed: 28444.46 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:07,877 Node[0] Epoch[9] Batch [150] Speed: 28444.46 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[9] Batch [200] Speed: 28070.18 samples/sec Train-accuracy=0.9929692016-10-26 19:38:08,105 Node[0] Epoch[9] Batch [200] Speed: 28070.18 samples/sec Train-accuracy=0.992969INFO:root:Epoch[9] Batch [200] Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:38:08,108 Node[0] Epoch[9] Batch [200] Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[9] Batch [200] Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:08,111 Node[0] Epoch[9] Batch [200] Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[9] Batch [200] Speed: 28070.18 samples/sec 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Train-accuracy=0.9890622016-10-26 19:38:08,581 Node[0] Epoch[9] Batch [300] Speed: 28070.16 samples/sec Train-accuracy=0.989062INFO:root:Epoch[9] Batch [300] Speed: 28070.16 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:38:08,582 Node[0] Epoch[9] Batch [300] Speed: 28070.16 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[9] Batch [300] Speed: 28070.16 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:08,584 Node[0] Epoch[9] Batch [300] Speed: 28070.16 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[9] Batch [300] Speed: 28070.16 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:08,585 Node[0] Epoch[9] Batch [300] Speed: 28070.16 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[9] Batch [350] Speed: 28699.55 samples/sec Train-accuracy=0.9895312016-10-26 19:38:08,809 Node[0] Epoch[9] Batch [350] Speed: 28699.55 samples/sec Train-accuracy=0.989531INFO:root:Epoch[9] Batch [350] Speed: 28699.55 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:38:08,812 Node[0] Epoch[9] Batch [350] Speed: 28699.55 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[9] Batch [350] Speed: 28699.55 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:08,813 Node[0] Epoch[9] Batch [350] Speed: 28699.55 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[9] Batch [350] Speed: 28699.55 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:08,815 Node[0] Epoch[9] Batch [350] Speed: 28699.55 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[9] Batch [400] Speed: 28571.43 samples/sec Train-accuracy=0.9893752016-10-26 19:38:09,040 Node[0] Epoch[9] Batch [400] Speed: 28571.43 samples/sec Train-accuracy=0.989375INFO:root:Epoch[9] Batch [400] Speed: 28571.43 samples/sec Train-top_k_accuracy_5=0.9996872016-10-26 19:38:09,042 Node[0] Epoch[9] Batch [400] Speed: 28571.43 samples/sec Train-top_k_accuracy_5=0.999687INFO:root:Epoch[9] Batch [400] Speed: 28571.43 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:09,043 Node[0] Epoch[9] Batch [400] Speed: 28571.43 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[9] Batch [400] Speed: 28571.43 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:09,046 Node[0] Epoch[9] Batch [400] Speed: 28571.43 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[9] Batch [450] Speed: 28828.81 samples/sec Train-accuracy=0.9914062016-10-26 19:38:09,269 Node[0] Epoch[9] Batch [450] Speed: 28828.81 samples/sec Train-accuracy=0.991406INFO:root:Epoch[9] Batch [450] Speed: 28828.81 samples/sec Train-top_k_accuracy_5=1.0000002016-10-26 19:38:09,270 Node[0] Epoch[9] Batch [450] Speed: 28828.81 samples/sec Train-top_k_accuracy_5=1.000000INFO:root:Epoch[9] Batch [450] Speed: 28828.81 samples/sec Train-top_k_accuracy_10=1.0000002016-10-26 19:38:09,272 Node[0] Epoch[9] Batch [450] Speed: 28828.81 samples/sec Train-top_k_accuracy_10=1.000000INFO:root:Epoch[9] Batch [450] Speed: 28828.81 samples/sec Train-top_k_accuracy_20=1.0000002016-10-26 19:38:09,273 Node[0] Epoch[9] Batch [450] Speed: 28828.81 samples/sec Train-top_k_accuracy_20=1.000000INFO:root:Epoch[9] Resetting Data Iterator2016-10-26 19:38:09,355 Node[0] Epoch[9] Resetting Data IteratorINFO:root:Epoch[9] Time cost=2.1792016-10-26 19:38:09,358 Node[0] Epoch[9] Time cost=2.179INFO:root:Epoch[9] Validation-accuracy=0.9739582016-10-26 19:38:09,522 Node[0] Epoch[9] Validation-accuracy=0.973958INFO:root:Epoch[9] Validation-top_k_accuracy_5=0.9992992016-10-26 19:38:09,523 Node[0] Epoch[9] Validation-top_k_accuracy_5=0.999299INFO:root:Epoch[9] Validation-top_k_accuracy_10=1.0000002016-10-26 19:38:09,525 Node[0] Epoch[9] Validation-top_k_accuracy_10=1.000000INFO:root:Epoch[9] Validation-top_k_accuracy_20=1.0000002016-10-26 19:38:09,526 Node[0] Epoch[9] Validation-top_k_accuracy_20=1.000000

可以看到，验证集上的准确率接近于1，这说明我们的安装过程是成功的。总结一下期间遇到的错误： 1.CMake Error: The following variables are used in this project, but they are set to NOTFOUND. Please set them or make sure they are set and tested correctly in the CMake files: CUDA_cublas_LIBRARY (ADVANCED) linked by target "mxnet" in directory G:/OpenSource/mxnet linked by target "mxnet" in directory G:/OpenSource/mxnet CUDA_cublas_device_LIBRARY (ADVANCED) linked by target "mxnet" in directory G:/OpenSource/mxnet linked by target "mxnet" in directory G:/OpenSource/mxnet CUDA_curand_LIBRARY (ADVANCED) linked by target "mxnet" in directory G:/OpenSource/mxnet linked by target "mxnet" in directory G:/OpenSource/mxnet 解决方法：参考github上的issue，换成64位编译器。 2.无法打开包括文件:opencv2.hpp 解决方法：在项目属性页的VC++标签页中的包含目录选项中加入opencv的头文件路径G:\opencv\build\include即可。 3.错误 5373 error LNK2001: 无法解析的外部符号 "int __cdecl cv::_interlockedExchangeAdd(int *,int)"(?_interlockedExchangeAdd@cv@@YAHPEAHH@Z) 解决方法：在项目属性页的标签页中的链接器下的附加依赖项属性中加入opencv的库文件G:\opencv\build\x64\vc12\lib\opencv_core2413.lib