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 12.0VC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:mxnetuildRelease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:OpenSourcemxnetpython，运行

        <code>python setup.py install</code>
    

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:Anaconda2Libsite-packagesmxnet-0.7.0-py2.7.eggmxnet下。将这些文件放入目录后，我们测试一下能不能导入

            <code>
                <span>import</span> ctypes
ctypes._dlopen(<span>r"C:Anaconda2Libsite-packagesmxnet-0.7.0-py2.7.eggmxnetcudart64_75.dll"</span>)
ctypes._dlopen(<span>r"C:Anaconda2Libsite-packagesmxnet-0.7.0-py2.7.eggmxnetcublas64_75.dll"</span>)
ctypes._dlopen(<span>r"cudnn64_5.dll"</span>)
ctypes._dlopen(<span>r"C:Anaconda2Libsite-packagesmxnet-0.7.0-py2.7.eggmxnetlibopenblas.dll"</span>)
ctypes._dlopen(<span>r"C:Anaconda2Libsite-packagesmxnet-0.7.0-py2.7.eggmxnetopencv_world300.dll"</span>)
ctypes._dlopen(<span>r"C:Anaconda2Libsite-packagesmxnet-0.7.0-py2.7.eggmxnetopencv_core2413.dll"</span>)
ctypes._dlopen(<span>r"vcomp120.dll"</span>)
ctypes._dlopen(<span>r"kernel32.dll"</span>)
<span>import</span> mxnet <span>as</span> mx
<span>print</span><span>"mxnet version is:</span><span>%s</span><span>"</span><span>%</span>mx.__version__</code>
        

        <code>mxnet version is:0.7.0</code>
    

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

            <code>
                <span>import</span> ctypes
ctypes._dlopen(<span>r"C:Anaconda2Libsite-packagesmxnet-0.7.0-py2.7.eggmxnetcudart64_75.dll"</span>)
ctypes._dlopen(<span>r"C:Anaconda2Libsite-packagesmxnet-0.7.0-py2.7.eggmxnetcublas64_75.dll"</span>)
ctypes._dlopen(<span>r"cudnn64_5.dll"</span>)
ctypes._dlopen(<span>r"C:Anaconda2Libsite-packagesmxnet-0.7.0-py2.7.eggmxnetlibopenblas.dll"</span>)
ctypes._dlopen(<span>r"C:Anaconda2Libsite-packagesmxnet-0.7.0-py2.7.eggmxnetopencv_world300.dll"</span>)
ctypes._dlopen(<span>r"C:Anaconda2Libsite-packagesmxnet-0.7.0-py2.7.eggmxnetopencv_core2413.dll"</span>)
ctypes._dlopen(<span>r"vcomp120.dll"</span>)
ctypes._dlopen(<span>r"kernel32.dll"</span>)
<span>import</span> mxnet <span>as</span> mx
<span>import</span> argparse
<span>import</span> os, sys
<span>import</span> logging

<span>def</span> _download(data_dir):
    <span>if</span><span>not</span> os.path.isdir(data_dir):
        os.system(<span>"mkdir "</span><span>+</span> data_dir)
    os.chdir(data_dir)
    <span>if</span> (<span>not</span> os.path.exists(<span>‘train-images-idx3-ubyte‘</span>)) <span>or</span><span></span>
       (<span>not</span> os.path.exists(<span>‘train-labels-idx1-ubyte‘</span>)) <span>or</span><span></span>
       (<span>not</span> os.path.exists(<span>‘t10k-images-idx3-ubyte‘</span>)) <span>or</span><span></span>
       (<span>not</span> os.path.exists(<span>‘t10k-labels-idx1-ubyte‘</span>)):
        os.system(<span>"wget http://data.dmlc.ml/mxnet/data/mnist.zip"</span>)
        os.system(<span>"unzip -u mnist.zip; rm mnist.zip"</span>)
    os.chdir(<span>".."</span>)

<span>def</span> get_loc(data, attr<span>=</span>{<span>‘lr_mult‘</span>:<span>‘0.01‘</span>}):
    <span>"""</span><span>    the localisation network in lenet-stn, it will increase acc about more than 1%,</span><span>    when num-epoch >=15</span><span>    """</span>
    loc <span>=</span> mx.symbol.Convolution(data<span>=</span>data, num_filter<span>=</span><span>30</span>, kernel<span>=</span>(<span>5</span>, <span>5</span>), stride<span>=</span>(<span>2</span>,<span>2</span>))
    loc <span>=</span> mx.symbol.Activation(data <span>=</span> loc, act_type<span>=</span><span>‘relu‘</span>)
    loc <span>=</span> mx.symbol.Pooling(data<span>=</span>loc, kernel<span>=</span>(<span>2</span>, <span>2</span>), stride<span>=</span>(<span>2</span>, <span>2</span>), pool_type<span>=</span><span>‘max‘</span>)
    loc <span>=</span> mx.symbol.Convolution(data<span>=</span>loc, num_filter<span>=</span><span>60</span>, kernel<span>=</span>(<span>3</span>, <span>3</span>), stride<span>=</span>(<span>1</span>,<span>1</span>), pad<span>=</span>(<span>1</span>, <span>1</span>))
    loc <span>=</span> mx.symbol.Activation(data <span>=</span> loc, act_type<span>=</span><span>‘relu‘</span>)
    loc <span>=</span> mx.symbol.Pooling(data<span>=</span>loc, global_pool<span>=</span><span>True</span>, kernel<span>=</span>(<span>2</span>, <span>2</span>), pool_type<span>=</span><span>‘avg‘</span>)
    loc <span>=</span> mx.symbol.Flatten(data<span>=</span>loc)
    loc <span>=</span> mx.symbol.FullyConnected(data<span>=</span>loc, num_hidden<span>=</span><span>6</span>, name<span>=</span><span>"stn_loc"</span>, attr<span>=</span>attr)
    <span>return</span> loc

<span>def</span> get_mlp():
    <span>"""</span><span>    multi-layer perceptron</span><span>    """</span>
    data <span>=</span> mx.symbol.Variable(<span>‘data‘</span>)
    fc1  <span>=</span> mx.symbol.FullyConnected(data <span>=</span> data, name<span>=</span><span>‘fc1‘</span>, num_hidden<span>=</span><span>128</span>)
    act1 <span>=</span> mx.symbol.Activation(data <span>=</span> fc1, name<span>=</span><span>‘relu1‘</span>, act_type<span>=</span><span>"relu"</span>)
    fc2  <span>=</span> mx.symbol.FullyConnected(data <span>=</span> act1, name <span>=</span><span>‘fc2‘</span>, num_hidden <span>=</span><span>64</span>)
    act2 <span>=</span> mx.symbol.Activation(data <span>=</span> fc2, name<span>=</span><span>‘relu2‘</span>, act_type<span>=</span><span>"relu"</span>)
    fc3  <span>=</span> mx.symbol.FullyConnected(data <span>=</span> act2, name<span>=</span><span>‘fc3‘</span>, num_hidden<span>=</span><span>10</span>)
    mlp  <span>=</span> mx.symbol.SoftmaxOutput(data <span>=</span> fc3, name <span>=</span><span>‘softmax‘</span>)
    <span>return</span> mlp

<span>def</span> get_lenet(add_stn<span>=</span><span>False</span>):
    <span>"""</span><span>    LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick</span><span>    Haffner. "Gradient-based learning applied to document recognition."</span><span>    Proceedings of the IEEE (1998)</span><span>    """</span>
    data <span>=</span> mx.symbol.Variable(<span>‘data‘</span>)
    <span>if</span>(add_stn):
        data <span>=</span> mx.sym.SpatialTransformer(data<span>=</span>data, loc<span>=</span>get_loc(data), target_shape <span>=</span> (<span>28</span>,<span>28</span>),
                                         transform_type<span>=</span><span>"affine"</span>, sampler_type<span>=</span><span>"bilinear"</span>)
    <span># first conv</span>
    conv1 <span>=</span> mx.symbol.Convolution(data<span>=</span>data, kernel<span>=</span>(<span>5</span>,<span>5</span>), num_filter<span>=</span><span>20</span>)
    tanh1 <span>=</span> mx.symbol.Activation(data<span>=</span>conv1, act_type<span>=</span><span>"tanh"</span>)
    pool1 <span>=</span> mx.symbol.Pooling(data<span>=</span>tanh1, pool_type<span>=</span><span>"max"</span>,
                              kernel<span>=</span>(<span>2</span>,<span>2</span>), stride<span>=</span>(<span>2</span>,<span>2</span>))
    <span># second conv</span>
    conv2 <span>=</span> mx.symbol.Convolution(data<span>=</span>pool1, kernel<span>=</span>(<span>5</span>,<span>5</span>), num_filter<span>=</span><span>50</span>)
    tanh2 <span>=</span> mx.symbol.Activation(data<span>=</span>conv2, act_type<span>=</span><span>"tanh"</span>)
    pool2 <span>=</span> mx.symbol.Pooling(data<span>=</span>tanh2, pool_type<span>=</span><span>"max"</span>,
                              kernel<span>=</span>(<span>2</span>,<span>2</span>), stride<span>=</span>(<span>2</span>,<span>2</span>))
    <span># first fullc</span>
    flatten <span>=</span> mx.symbol.Flatten(data<span>=</span>pool2)
    fc1 <span>=</span> mx.symbol.FullyConnected(data<span>=</span>flatten, num_hidden<span>=</span><span>500</span>)
    tanh3 <span>=</span> mx.symbol.Activation(data<span>=</span>fc1, act_type<span>=</span><span>"tanh"</span>)
    <span># second fullc</span>
    fc2 <span>=</span> mx.symbol.FullyConnected(data<span>=</span>tanh3, num_hidden<span>=</span><span>10</span>)
    <span># loss</span>
    lenet <span>=</span> mx.symbol.SoftmaxOutput(data<span>=</span>fc2, name<span>=</span><span>‘softmax‘</span>)
    <span>return</span> lenet

<span>def</span> get_iterator(data_shape):
    <span>def</span> get_iterator_impl(args, kv):
        data_dir <span>=</span> args.data_dir
        <span>"""</span><span>        if ‘://‘ not in args.data_dir:</span><span>            _download(args.data_dir)</span><span>        """</span>
        flat <span>=</span><span>False</span><span>if</span><span>len</span>(data_shape) <span>==</span><span>3</span><span>else</span><span>True</span>

        train           <span>=</span> mx.io.MNISTIter(
            image       <span>=</span> data_dir <span>+</span><span>"train-images-idx3-ubyte"</span>,
            label       <span>=</span> data_dir <span>+</span><span>"train-labels-idx1-ubyte"</span>,
            input_shape <span>=</span> data_shape,
            batch_size  <span>=</span> args.batch_size,
            shuffle     <span>=</span><span>True</span>,
            flat        <span>=</span> flat,
            num_parts   <span>=</span> kv.num_workers,
            part_index  <span>=</span> kv.rank)

        val <span>=</span> mx.io.MNISTIter(
            image       <span>=</span> data_dir <span>+</span><span>"t10k-images-idx3-ubyte"</span>,
            label       <span>=</span> data_dir <span>+</span><span>"t10k-labels-idx1-ubyte"</span>,
            input_shape <span>=</span> data_shape,
            batch_size  <span>=</span> args.batch_size,
            flat        <span>=</span> flat,
            num_parts   <span>=</span> kv.num_workers,
            part_index  <span>=</span> kv.rank)

        <span>return</span> (train, val)
    <span>return</span> get_iterator_impl

<span>def</span> parse_args():
    parser <span>=</span> argparse.ArgumentParser(description<span>=</span><span>‘train an image classifer on mnist‘</span>)
    parser.add_argument(<span>‘--network‘</span>, <span>type</span><span>=</span><span>str</span>, default<span>=</span><span>‘mlp‘</span>,
                        choices <span>=</span> [<span>‘mlp‘</span>, <span>‘lenet‘</span>, <span>‘lenet-stn‘</span>],
                        <span>help</span><span>=</span><span>‘the cnn to use‘</span>)
    parser.add_argument(<span>‘--data-dir‘</span>, <span>type</span><span>=</span><span>str</span>, default<span>=</span><span>‘mnist/‘</span>,
                        <span>help</span><span>=</span><span>‘the input data directory‘</span>)
    parser.add_argument(<span>‘--gpus‘</span>, <span>type</span><span>=</span><span>str</span>,
                        <span>help</span><span>=</span><span>‘the gpus will be used, e.g "0,1,2,3"‘</span>)
    parser.add_argument(<span>‘--num-examples‘</span>, <span>type</span><span>=</span><span>int</span>, default<span>=</span><span>60000</span>,
                        <span>help</span><span>=</span><span>‘the number of training examples‘</span>)
    parser.add_argument(<span>‘--batch-size‘</span>, <span>type</span><span>=</span><span>int</span>, default<span>=</span><span>128</span>,
                        <span>help</span><span>=</span><span>‘the batch size‘</span>)
    parser.add_argument(<span>‘--lr‘</span>, <span>type</span><span>=</span><span>float</span>, default<span>=</span>.<span>1</span>,
                        <span>help</span><span>=</span><span>‘the initial learning rate‘</span>)
    parser.add_argument(<span>‘--model-prefix‘</span>, <span>type</span><span>=</span><span>str</span>,
                        <span>help</span><span>=</span><span>‘the prefix of the model to load/save‘</span>)
    parser.add_argument(<span>‘--save-model-prefix‘</span>, <span>type</span><span>=</span><span>str</span>,
                        <span>help</span><span>=</span><span>‘the prefix of the model to save‘</span>)
    parser.add_argument(<span>‘--num-epochs‘</span>, <span>type</span><span>=</span><span>int</span>, default<span>=</span><span>10</span>,
                        <span>help</span><span>=</span><span>‘the number of training epochs‘</span>)
    parser.add_argument(<span>‘--load-epoch‘</span>, <span>type</span><span>=</span><span>int</span>,
                        <span>help</span><span>=</span><span>"load the model on an epoch using the model-prefix"</span>)
    parser.add_argument(<span>‘--kv-store‘</span>, <span>type</span><span>=</span><span>str</span>, default<span>=</span><span>‘local‘</span>,
                        <span>help</span><span>=</span><span>‘the kvstore type‘</span>)
    parser.add_argument(<span>‘--lr-factor‘</span>, <span>type</span><span>=</span><span>float</span>, default<span>=</span><span>1</span>,
                        <span>help</span><span>=</span><span>‘times the lr with a factor for every lr-factor-epoch epoch‘</span>)
    parser.add_argument(<span>‘--lr-factor-epoch‘</span>, <span>type</span><span>=</span><span>float</span>, default<span>=</span><span>1</span>,
                        <span>help</span><span>=</span><span>‘the number of epoch to factor the lr, could be .5‘</span>)
    <span>return</span> parser.parse_args([<span>‘--gpus‘</span>, <span>‘0‘</span>, <span>‘--data-dir‘</span>, <span>‘G:/OpenSource/mxnet/example/image-classification/mnist/‘</span>])

<span>if</span><span>__name__</span><span>==</span><span>‘__main__‘</span>:
    args <span>=</span> parse_args()

    <span>if</span> args.network <span>==</span><span>‘mlp‘</span>:
        data_shape <span>=</span> (<span>784</span>, )
        net <span>=</span> get_mlp()
    <span>elif</span> args.network <span>==</span><span>‘lenet-stn‘</span>:
        data_shape <span>=</span> (<span>1</span>, <span>28</span>, <span>28</span>)
        net <span>=</span> get_lenet(<span>True</span>)
    <span>else</span>:
        data_shape <span>=</span> (<span>1</span>, <span>28</span>, <span>28</span>)
        net <span>=</span> get_lenet()

    <span># kvstore</span>
    kv <span>=</span> mx.kvstore.create(args.kv_store)

    <span># logging</span>
    head <span>=</span><span>‘</span><span>%(asctime)-15s</span><span> Node[‘</span><span>+</span><span>str</span>(kv.rank) <span>+</span><span>‘] </span><span>%(message)s</span><span>‘</span>
    
    logger <span>=</span> logging.getLogger()
    formatter <span>=</span> logging.Formatter(head)
    stdout_handler <span>=</span> logging.StreamHandler(sys.stdout)
    stdout_handler.setFormatter(formatter)

    logger.addHandler(stdout_handler)

    logger.setLevel(logging.INFO)
    logger.info(<span>‘start with arguments </span><span>%s</span><span>‘</span>, args)
    
    <span># load model</span>
    model_prefix <span>=</span> args.model_prefix
    <span>if</span> model_prefix <span>is</span><span>not</span><span>None</span>:
        model_prefix <span>+=</span><span>"-</span><span>%d</span><span>"</span><span>%</span> (kv.rank)
    model_args <span>=</span> {}
    <span>if</span> args.load_epoch <span>is</span><span>not</span><span>None</span>:
        <span>assert</span> model_prefix <span>is</span><span>not</span><span>None</span>
        tmp <span>=</span> mx.model.FeedForward.load(model_prefix, args.load_epoch)
        model_args <span>=</span> {<span>‘arg_params‘</span> : tmp.arg_params,
                      <span>‘aux_params‘</span> : tmp.aux_params,
                      <span>‘begin_epoch‘</span> : args.load_epoch}
    <span># save model</span>
    save_model_prefix <span>=</span> args.save_model_prefix
    <span>if</span> save_model_prefix <span>is</span><span>None</span>:
        save_model_prefix <span>=</span> model_prefix
    checkpoint <span>=</span><span>None</span><span>if</span> save_model_prefix <span>is</span><span>None</span><span>else</span> mx.callback.do_checkpoint(save_model_prefix)

    <span># data</span>
    (train, val) <span>=</span> get_iterator(data_shape)(args, kv)

    <span># train</span>
    devs <span>=</span> mx.cpu() <span>if</span> args.gpus <span>is</span><span>None</span><span>else</span> [
        mx.gpu(<span>int</span>(i)) <span>for</span> i <span>in</span> args.gpus.split(<span>‘,‘</span>)]

    epoch_size <span>=</span> args.num_examples <span>/</span> args.batch_size

    <span>if</span> args.kv_store <span>==</span><span>‘dist_sync‘</span>:
        epoch_size <span>/=</span> kv.num_workers
        model_args[<span>‘epoch_size‘</span>] <span>=</span> epoch_size

    <span>if</span><span>‘lr_factor‘</span><span>in</span> args <span>and</span> args.lr_factor <span><</span><span>1</span>:
        model_args[<span>‘lr_scheduler‘</span>] <span>=</span> mx.lr_scheduler.FactorScheduler(
            step <span>=</span><span>max</span>(<span>int</span>(epoch_size <span>*</span> args.lr_factor_epoch), <span>1</span>),
            factor <span>=</span> args.lr_factor)

    <span>if</span><span>‘clip_gradient‘</span><span>in</span> args <span>and</span> args.clip_gradient <span>is</span><span>not</span><span>None</span>:
        model_args[<span>‘clip_gradient‘</span>] <span>=</span> args.clip_gradient

    <span># disable kvstore for single device</span><span>if</span><span>‘local‘</span><span>in</span> kv.<span>type</span><span>and</span> (
            args.gpus <span>is</span><span>None</span><span>or</span><span>len</span>(args.gpus.split(<span>‘,‘</span>)) <span>is</span><span>1</span>):
        kv <span>=</span><span>None</span>

    model <span>=</span> mx.model.FeedForward(
        ctx                <span>=</span> devs,
        symbol             <span>=</span> net,
        num_epoch          <span>=</span> args.num_epochs,
        learning_rate      <span>=</span> args.lr,
        momentum           <span>=</span><span>0.9</span>,
        wd                 <span>=</span><span>0.00001</span>,
        initializer        <span>=</span> mx.init.Xavier(factor_type<span>=</span><span>"in"</span>, magnitude<span>=</span><span>2.34</span>),
        <span>**</span>model_args)

    eval_metrics <span>=</span> [<span>‘accuracy‘</span>]
    <span>## TopKAccuracy only allows top_k > 1</span><span>for</span> top_k <span>in</span> [<span>5</span>, <span>10</span>, <span>20</span>]:
        eval_metrics.append(mx.metric.create(<span>‘top_k_accuracy‘</span>, top_k <span>=</span> top_k))

    model.fit(
        X                  <span>=</span> train,
        eval_data          <span>=</span> val,
        eval_metric        <span>=</span> eval_metrics,
        kvstore            <span>=</span> kv,
        batch_end_callback <span>=</span> [mx.callback.Speedometer(args.batch_size, <span>50</span>)],
        epoch_end_callback <span>=</span> checkpoint)
</code>
        

        <code>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=0.687344


2016-10-26 19:37:45,351 Node[0] Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-accuracy=0.687344


INFO:root:Epoch[0] Batch [50]   Speed: 23104.70 samples/sec Train-top_k_accuracy_5=0.935937


2016-10-26 19:37:45,354 Node[0] Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-top_k_accuracy_5=0.935937


INFO:root:Epoch[0] Batch [50]   Speed: 23104.70 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:45,357 Node[0] Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[0] Batch [50]   Speed: 23104.70 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:45,361 Node[0] Epoch[0] Batch [50] Speed: 23104.70 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[0] Batch [100]  Speed: 22535.22 samples/sec Train-accuracy=0.897188


2016-10-26 19:37:45,648 Node[0] Epoch[0] Batch [100]    Speed: 22535.22 samples/sec Train-accuracy=0.897188


INFO:root:Epoch[0] Batch [100]  Speed: 22535.22 samples/sec Train-top_k_accuracy_5=0.992812


2016-10-26 19:37:45,650 Node[0] Epoch[0] Batch [100]    Speed: 22535.22 samples/sec Train-top_k_accuracy_5=0.992812


INFO:root:Epoch[0] Batch [100]  Speed: 22535.22 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:45,651 Node[0] Epoch[0] Batch [100]    Speed: 22535.22 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[0] Batch [100]  Speed: 22535.22 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:45,654 Node[0] Epoch[0] Batch [100]    Speed: 22535.22 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[0] Batch [150]  Speed: 23443.22 samples/sec Train-accuracy=0.919687


2016-10-26 19:37:45,930 Node[0] Epoch[0] Batch [150]    Speed: 23443.22 samples/sec Train-accuracy=0.919687


INFO:root:Epoch[0] Batch [150]  Speed: 23443.22 samples/sec Train-top_k_accuracy_5=0.995469


2016-10-26 19:37:45,930 Node[0] Epoch[0] Batch [150]    Speed: 23443.22 samples/sec Train-top_k_accuracy_5=0.995469


INFO:root:Epoch[0] Batch [150]  Speed: 23443.22 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:45,933 Node[0] Epoch[0] Batch [150]    Speed: 23443.22 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[0] Batch [150]  Speed: 23443.22 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:45,934 Node[0] Epoch[0] Batch [150]    Speed: 23443.22 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[0] Batch [200]  Speed: 24150.96 samples/sec Train-accuracy=0.927656


2016-10-26 19:37:46,200 Node[0] Epoch[0] Batch [200]    Speed: 24150.96 samples/sec Train-accuracy=0.927656


INFO:root:Epoch[0] Batch [200]  Speed: 24150.96 samples/sec Train-top_k_accuracy_5=0.997031


2016-10-26 19:37:46,203 Node[0] Epoch[0] Batch [200]    Speed: 24150.96 samples/sec Train-top_k_accuracy_5=0.997031


INFO:root:Epoch[0] Batch [200]  Speed: 24150.96 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:46,206 Node[0] Epoch[0] Batch [200]    Speed: 24150.96 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[0] Batch [200]  Speed: 24150.96 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:46,210 Node[0] Epoch[0] Batch [200]    Speed: 24150.96 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[0] Batch [250]  Speed: 22145.33 samples/sec Train-accuracy=0.942031


2016-10-26 19:37:46,502 Node[0] Epoch[0] Batch [250]    Speed: 22145.33 samples/sec Train-accuracy=0.942031


INFO:root:Epoch[0] Batch [250]  Speed: 22145.33 samples/sec Train-top_k_accuracy_5=0.996875


2016-10-26 19:37:46,503 Node[0] Epoch[0] Batch [250]    Speed: 22145.33 samples/sec Train-top_k_accuracy_5=0.996875


INFO:root:Epoch[0] Batch [250]  Speed: 22145.33 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:46,509 Node[0] Epoch[0] Batch [250]    Speed: 22145.33 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[0] Batch [250]  Speed: 22145.33 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:46,513 Node[0] Epoch[0] Batch [250]    Speed: 22145.33 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[0] Batch [300]  Speed: 25600.00 samples/sec Train-accuracy=0.940781


2016-10-26 19:37:46,766 Node[0] Epoch[0] Batch [300]    Speed: 25600.00 samples/sec Train-accuracy=0.940781


INFO:root:Epoch[0] Batch [300]  Speed: 25600.00 samples/sec Train-top_k_accuracy_5=0.997656


2016-10-26 19:37:46,767 Node[0] Epoch[0] Batch [300]    Speed: 25600.00 samples/sec Train-top_k_accuracy_5=0.997656


INFO:root:Epoch[0] Batch [300]  Speed: 25600.00 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:46,769 Node[0] Epoch[0] Batch [300]    Speed: 25600.00 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[0] Batch [300]  Speed: 25600.00 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:46,770 Node[0] Epoch[0] Batch [300]    Speed: 25600.00 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[0] Batch [350]  Speed: 25497.99 samples/sec Train-accuracy=0.943750


2016-10-26 19:37:47,025 Node[0] Epoch[0] Batch [350]    Speed: 25497.99 samples/sec Train-accuracy=0.943750


INFO:root:Epoch[0] Batch [350]  Speed: 25497.99 samples/sec Train-top_k_accuracy_5=0.998594


2016-10-26 19:37:47,026 Node[0] Epoch[0] Batch [350]    Speed: 25497.99 samples/sec Train-top_k_accuracy_5=0.998594


INFO:root:Epoch[0] Batch [350]  Speed: 25497.99 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:47,028 Node[0] Epoch[0] Batch [350]    Speed: 25497.99 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[0] Batch [350]  Speed: 25497.99 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:47,029 Node[0] Epoch[0] Batch [350]    Speed: 25497.99 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[0] Batch [400]  Speed: 23970.04 samples/sec Train-accuracy=0.952344


2016-10-26 19:37:47,296 Node[0] Epoch[0] Batch [400]    Speed: 23970.04 samples/sec Train-accuracy=0.952344


INFO:root:Epoch[0] Batch [400]  Speed: 23970.04 samples/sec Train-top_k_accuracy_5=0.998594


2016-10-26 19:37:47,298 Node[0] Epoch[0] Batch [400]    Speed: 23970.04 samples/sec Train-top_k_accuracy_5=0.998594


INFO:root:Epoch[0] Batch [400]  Speed: 23970.04 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:47,299 Node[0] Epoch[0] Batch [400]    Speed: 23970.04 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[0] Batch [400]  Speed: 23970.04 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:47,302 Node[0] Epoch[0] Batch [400]    Speed: 23970.04 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[0] Batch [450]  Speed: 27350.40 samples/sec Train-accuracy=0.952969


2016-10-26 19:37:47,539 Node[0] Epoch[0] Batch [450]    Speed: 27350.40 samples/sec Train-accuracy=0.952969


INFO:root:Epoch[0] Batch [450]  Speed: 27350.40 samples/sec Train-top_k_accuracy_5=0.998906


2016-10-26 19:37:47,542 Node[0] Epoch[0] Batch [450]    Speed: 27350.40 samples/sec Train-top_k_accuracy_5=0.998906


INFO:root:Epoch[0] Batch [450]  Speed: 27350.40 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:47,546 Node[0] Epoch[0] Batch [450]    Speed: 27350.40 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[0] Batch [450]  Speed: 27350.40 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:47,548 Node[0] Epoch[0] Batch [450]    Speed: 27350.40 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[0] Resetting Data Iterator


2016-10-26 19:37:47,634 Node[0] Epoch[0] Resetting Data Iterator


INFO:root:Epoch[0] Time cost=3.100


2016-10-26 19:37:47,637 Node[0] Epoch[0] Time cost=3.100


INFO:root:Epoch[0] Validation-accuracy=0.960036


2016-10-26 19:37:47,826 Node[0] Epoch[0] Validation-accuracy=0.960036


INFO:root:Epoch[0] Validation-top_k_accuracy_5=0.998698


2016-10-26 19:37:47,828 Node[0] Epoch[0] Validation-top_k_accuracy_5=0.998698


INFO:root:Epoch[0] Validation-top_k_accuracy_10=1.000000


2016-10-26 19:37:47,829 Node[0] Epoch[0] Validation-top_k_accuracy_10=1.000000


INFO:root:Epoch[0] Validation-top_k_accuracy_20=1.000000


2016-10-26 19:37:47,832 Node[0] Epoch[0] Validation-top_k_accuracy_20=1.000000


INFO:root:Epoch[1] Batch [50]   Speed: 25806.46 samples/sec Train-accuracy=0.955156


2016-10-26 19:37:48,085 Node[0] Epoch[1] Batch [50] Speed: 25806.46 samples/sec Train-accuracy=0.955156


INFO:root:Epoch[1] Batch [50]   Speed: 25806.46 samples/sec Train-top_k_accuracy_5=0.998594


2016-10-26 19:37:48,088 Node[0] Epoch[1] Batch [50] Speed: 25806.46 samples/sec Train-top_k_accuracy_5=0.998594


INFO:root:Epoch[1] Batch [50]   Speed: 25806.46 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:48,091 Node[0] Epoch[1] Batch [50] Speed: 25806.46 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[1] Batch [50]   Speed: 25806.46 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:48,095 Node[0] Epoch[1] Batch [50] Speed: 25806.46 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[1] Batch [100]  Speed: 27004.19 samples/sec Train-accuracy=0.957969


2016-10-26 19:37:48,334 Node[0] Epoch[1] Batch [100]    Speed: 27004.19 samples/sec Train-accuracy=0.957969


INFO:root:Epoch[1] Batch [100]  Speed: 27004.19 samples/sec Train-top_k_accuracy_5=0.998281


2016-10-26 19:37:48,335 Node[0] Epoch[1] Batch [100]    Speed: 27004.19 samples/sec Train-top_k_accuracy_5=0.998281


INFO:root:Epoch[1] Batch [100]  Speed: 27004.19 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:48,336 Node[0] Epoch[1] Batch [100]    Speed: 27004.19 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[1] Batch [100]  Speed: 27004.19 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:48,338 Node[0] Epoch[1] Batch [100]    Speed: 27004.19 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[1] Batch [150]  Speed: 23443.22 samples/sec Train-accuracy=0.962969


2016-10-26 19:37:48,612 Node[0] Epoch[1] Batch [150]    Speed: 23443.22 samples/sec Train-accuracy=0.962969


INFO:root:Epoch[1] Batch [150]  Speed: 23443.22 samples/sec Train-top_k_accuracy_5=0.999062


2016-10-26 19:37:48,615 Node[0] Epoch[1] Batch [150]    Speed: 23443.22 samples/sec Train-top_k_accuracy_5=0.999062


INFO:root:Epoch[1] Batch [150]  Speed: 23443.22 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:48,618 Node[0] Epoch[1] Batch [150]    Speed: 23443.22 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[1] Batch [150]  Speed: 23443.22 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:48,619 Node[0] Epoch[1] Batch [150]    Speed: 23443.22 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[1] Batch [200]  Speed: 26446.27 samples/sec Train-accuracy=0.964688


2016-10-26 19:37:48,864 Node[0] Epoch[1] Batch [200]    Speed: 26446.27 samples/sec Train-accuracy=0.964688


INFO:root:Epoch[1] Batch [200]  Speed: 26446.27 samples/sec Train-top_k_accuracy_5=0.999531


2016-10-26 19:37:48,865 Node[0] Epoch[1] Batch [200]    Speed: 26446.27 samples/sec Train-top_k_accuracy_5=0.999531


INFO:root:Epoch[1] Batch [200]  Speed: 26446.27 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:48,867 Node[0] Epoch[1] Batch [200]    Speed: 26446.27 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[1] Batch [200]  Speed: 26446.27 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:48,868 Node[0] Epoch[1] Batch [200]    Speed: 26446.27 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[1] Batch [250]  Speed: 28193.83 samples/sec Train-accuracy=0.967656


2016-10-26 19:37:49,096 Node[0] Epoch[1] Batch [250]    Speed: 28193.83 samples/sec Train-accuracy=0.967656


INFO:root:Epoch[1] Batch [250]  Speed: 28193.83 samples/sec Train-top_k_accuracy_5=0.998906


2016-10-26 19:37:49,098 Node[0] Epoch[1] Batch [250]    Speed: 28193.83 samples/sec Train-top_k_accuracy_5=0.998906


INFO:root:Epoch[1] Batch [250]  Speed: 28193.83 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:49,101 Node[0] Epoch[1] Batch [250]    Speed: 28193.83 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[1] Batch [250]  Speed: 28193.83 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:49,102 Node[0] Epoch[1] Batch [250]    Speed: 28193.83 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[1] Batch [300]  Speed: 24806.19 samples/sec Train-accuracy=0.962656


2016-10-26 19:37:49,364 Node[0] Epoch[1] Batch [300]    Speed: 24806.19 samples/sec Train-accuracy=0.962656


INFO:root:Epoch[1] Batch [300]  Speed: 24806.19 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:37:49,365 Node[0] Epoch[1] Batch [300]    Speed: 24806.19 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[1] Batch [300]  Speed: 24806.19 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:49,368 Node[0] Epoch[1] Batch [300]    Speed: 24806.19 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[1] Batch [300]  Speed: 24806.19 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:49,369 Node[0] Epoch[1] Batch [300]    Speed: 24806.19 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[1] Batch [350]  Speed: 27004.22 samples/sec Train-accuracy=0.966719


2016-10-26 19:37:49,608 Node[0] Epoch[1] Batch [350]    Speed: 27004.22 samples/sec Train-accuracy=0.966719


INFO:root:Epoch[1] Batch [350]  Speed: 27004.22 samples/sec Train-top_k_accuracy_5=0.999062


2016-10-26 19:37:49,609 Node[0] Epoch[1] Batch [350]    Speed: 27004.22 samples/sec Train-top_k_accuracy_5=0.999062


INFO:root:Epoch[1] Batch [350]  Speed: 27004.22 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:49,611 Node[0] Epoch[1] Batch [350]    Speed: 27004.22 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[1] Batch [350]  Speed: 27004.22 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:49,612 Node[0] Epoch[1] Batch [350]    Speed: 27004.22 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[1] Batch [400]  Speed: 27586.22 samples/sec Train-accuracy=0.970313


2016-10-26 19:37:49,846 Node[0] Epoch[1] Batch [400]    Speed: 27586.22 samples/sec Train-accuracy=0.970313


INFO:root:Epoch[1] Batch [400]  Speed: 27586.22 samples/sec Train-top_k_accuracy_5=0.999219


2016-10-26 19:37:49,848 Node[0] Epoch[1] Batch [400]    Speed: 27586.22 samples/sec Train-top_k_accuracy_5=0.999219


INFO:root:Epoch[1] Batch [400]  Speed: 27586.22 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:49,851 Node[0] Epoch[1] Batch [400]    Speed: 27586.22 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[1] Batch [400]  Speed: 27586.22 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:49,852 Node[0] Epoch[1] Batch [400]    Speed: 27586.22 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[1] Batch [450]  Speed: 26229.51 samples/sec Train-accuracy=0.969531


2016-10-26 19:37:50,099 Node[0] Epoch[1] Batch [450]    Speed: 26229.51 samples/sec Train-accuracy=0.969531


INFO:root:Epoch[1] Batch [450]  Speed: 26229.51 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:37:50,101 Node[0] Epoch[1] Batch [450]    Speed: 26229.51 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[1] Batch [450]  Speed: 26229.51 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:50,105 Node[0] Epoch[1] Batch [450]    Speed: 26229.51 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[1] Batch [450]  Speed: 26229.51 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:50,109 Node[0] Epoch[1] Batch [450]    Speed: 26229.51 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[1] Resetting Data Iterator


2016-10-26 19:37:50,196 Node[0] Epoch[1] Resetting Data Iterator


INFO:root:Epoch[1] Time cost=2.364


2016-10-26 19:37:50,197 Node[0] Epoch[1] Time cost=2.364


INFO:root:Epoch[1] Validation-accuracy=0.968349


2016-10-26 19:37:50,381 Node[0] Epoch[1] Validation-accuracy=0.968349


INFO:root:Epoch[1] Validation-top_k_accuracy_5=0.999099


2016-10-26 19:37:50,382 Node[0] Epoch[1] Validation-top_k_accuracy_5=0.999099


INFO:root:Epoch[1] Validation-top_k_accuracy_10=1.000000


2016-10-26 19:37:50,384 Node[0] Epoch[1] Validation-top_k_accuracy_10=1.000000


INFO:root:Epoch[1] Validation-top_k_accuracy_20=1.000000


2016-10-26 19:37:50,385 Node[0] Epoch[1] Validation-top_k_accuracy_20=1.000000


INFO:root:Epoch[2] Batch [50]   Speed: 27004.22 samples/sec Train-accuracy=0.971875


2016-10-26 19:37:50,635 Node[0] Epoch[2] Batch [50] Speed: 27004.22 samples/sec Train-accuracy=0.971875


INFO:root:Epoch[2] Batch [50]   Speed: 27004.22 samples/sec Train-top_k_accuracy_5=0.999219


2016-10-26 19:37:50,638 Node[0] Epoch[2] Batch [50] Speed: 27004.22 samples/sec Train-top_k_accuracy_5=0.999219


INFO:root:Epoch[2] Batch [50]   Speed: 27004.22 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:50,644 Node[0] Epoch[2] Batch [50] Speed: 27004.22 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[2] Batch [50]   Speed: 27004.22 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:50,644 Node[0] Epoch[2] Batch [50] Speed: 27004.22 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[2] Batch [100]  Speed: 27234.05 samples/sec Train-accuracy=0.971250


2016-10-26 19:37:50,881 Node[0] Epoch[2] Batch [100]    Speed: 27234.05 samples/sec Train-accuracy=0.971250


INFO:root:Epoch[2] Batch [100]  Speed: 27234.05 samples/sec Train-top_k_accuracy_5=0.999531


2016-10-26 19:37:50,882 Node[0] Epoch[2] Batch [100]    Speed: 27234.05 samples/sec Train-top_k_accuracy_5=0.999531


INFO:root:Epoch[2] Batch [100]  Speed: 27234.05 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:50,884 Node[0] Epoch[2] Batch [100]    Speed: 27234.05 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[2] Batch [100]  Speed: 27234.05 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:50,887 Node[0] Epoch[2] Batch [100]    Speed: 27234.05 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[2] Batch [150]  Speed: 26778.23 samples/sec Train-accuracy=0.970625


2016-10-26 19:37:51,127 Node[0] Epoch[2] Batch [150]    Speed: 26778.23 samples/sec Train-accuracy=0.970625


INFO:root:Epoch[2] Batch [150]  Speed: 26778.23 samples/sec Train-top_k_accuracy_5=0.999531


2016-10-26 19:37:51,128 Node[0] Epoch[2] Batch [150]    Speed: 26778.23 samples/sec Train-top_k_accuracy_5=0.999531


INFO:root:Epoch[2] Batch [150]  Speed: 26778.23 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:51,130 Node[0] Epoch[2] Batch [150]    Speed: 26778.23 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[2] Batch [150]  Speed: 26778.23 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:51,131 Node[0] Epoch[2] Batch [150]    Speed: 26778.23 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[2] Batch [200]  Speed: 27705.61 samples/sec Train-accuracy=0.974844


2016-10-26 19:37:51,364 Node[0] Epoch[2] Batch [200]    Speed: 27705.61 samples/sec Train-accuracy=0.974844


INFO:root:Epoch[2] Batch [200]  Speed: 27705.61 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:37:51,367 Node[0] Epoch[2] Batch [200]    Speed: 27705.61 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[2] Batch [200]  Speed: 27705.61 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:51,368 Node[0] Epoch[2] Batch [200]    Speed: 27705.61 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[2] Batch [200]  Speed: 27705.61 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:51,371 Node[0] Epoch[2] Batch [200]    Speed: 27705.61 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[2] Batch [250]  Speed: 24902.73 samples/sec Train-accuracy=0.975781


2016-10-26 19:37:51,631 Node[0] Epoch[2] Batch [250]    Speed: 24902.73 samples/sec Train-accuracy=0.975781


INFO:root:Epoch[2] Batch [250]  Speed: 24902.73 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:37:51,631 Node[0] Epoch[2] Batch [250]    Speed: 24902.73 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[2] Batch [250]  Speed: 24902.73 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:51,634 Node[0] Epoch[2] Batch [250]    Speed: 24902.73 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[2] Batch [250]  Speed: 24902.73 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:51,635 Node[0] Epoch[2] Batch [250]    Speed: 24902.73 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[2] Batch [300]  Speed: 25600.00 samples/sec Train-accuracy=0.973125


2016-10-26 19:37:51,887 Node[0] Epoch[2] Batch [300]    Speed: 25600.00 samples/sec Train-accuracy=0.973125


INFO:root:Epoch[2] Batch [300]  Speed: 25600.00 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:37:51,890 Node[0] Epoch[2] Batch [300]    Speed: 25600.00 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[2] Batch [300]  Speed: 25600.00 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:51,894 Node[0] Epoch[2] Batch [300]    Speed: 25600.00 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[2] Batch [300]  Speed: 25600.00 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:51,895 Node[0] Epoch[2] Batch [300]    Speed: 25600.00 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[2] Batch [350]  Speed: 27118.64 samples/sec Train-accuracy=0.975781


2016-10-26 19:37:52,134 Node[0] Epoch[2] Batch [350]    Speed: 27118.64 samples/sec Train-accuracy=0.975781


INFO:root:Epoch[2] Batch [350]  Speed: 27118.64 samples/sec Train-top_k_accuracy_5=0.999531


2016-10-26 19:37:52,137 Node[0] Epoch[2] Batch [350]    Speed: 27118.64 samples/sec Train-top_k_accuracy_5=0.999531


INFO:root:Epoch[2] Batch [350]  Speed: 27118.64 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:52,140 Node[0] Epoch[2] Batch [350]    Speed: 27118.64 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[2] Batch [350]  Speed: 27118.64 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:52,141 Node[0] Epoch[2] Batch [350]    Speed: 27118.64 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[2] Batch [400]  Speed: 26337.45 samples/sec Train-accuracy=0.977969


2016-10-26 19:37:52,385 Node[0] Epoch[2] Batch [400]    Speed: 26337.45 samples/sec Train-accuracy=0.977969


INFO:root:Epoch[2] Batch [400]  Speed: 26337.45 samples/sec Train-top_k_accuracy_5=0.999219


2016-10-26 19:37:52,387 Node[0] Epoch[2] Batch [400]    Speed: 26337.45 samples/sec Train-top_k_accuracy_5=0.999219


INFO:root:Epoch[2] Batch [400]  Speed: 26337.45 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:52,390 Node[0] Epoch[2] Batch [400]    Speed: 26337.45 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[2] Batch [400]  Speed: 26337.45 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:52,391 Node[0] Epoch[2] Batch [400]    Speed: 26337.45 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[2] Batch [450]  Speed: 25196.83 samples/sec Train-accuracy=0.977187


2016-10-26 19:37:52,647 Node[0] Epoch[2] Batch [450]    Speed: 25196.83 samples/sec Train-accuracy=0.977187


INFO:root:Epoch[2] Batch [450]  Speed: 25196.83 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:37:52,648 Node[0] Epoch[2] Batch [450]    Speed: 25196.83 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[2] Batch [450]  Speed: 25196.83 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:52,650 Node[0] Epoch[2] Batch [450]    Speed: 25196.83 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[2] Batch [450]  Speed: 25196.83 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:52,651 Node[0] Epoch[2] Batch [450]    Speed: 25196.83 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[2] Resetting Data Iterator


2016-10-26 19:37:52,736 Node[0] Epoch[2] Resetting Data Iterator


INFO:root:Epoch[2] Time cost=2.345


2016-10-26 19:37:52,737 Node[0] Epoch[2] Time cost=2.345


INFO:root:Epoch[2] Validation-accuracy=0.973858


2016-10-26 19:37:52,903 Node[0] Epoch[2] Validation-accuracy=0.973858


INFO:root:Epoch[2] Validation-top_k_accuracy_5=0.999099


2016-10-26 19:37:52,905 Node[0] Epoch[2] Validation-top_k_accuracy_5=0.999099


INFO:root:Epoch[2] Validation-top_k_accuracy_10=1.000000


2016-10-26 19:37:52,907 Node[0] Epoch[2] Validation-top_k_accuracy_10=1.000000


INFO:root:Epoch[2] Validation-top_k_accuracy_20=1.000000


2016-10-26 19:37:52,910 Node[0] Epoch[2] Validation-top_k_accuracy_20=1.000000


INFO:root:Epoch[3] Batch [50]   Speed: 27705.61 samples/sec Train-accuracy=0.977969


2016-10-26 19:37:53,147 Node[0] Epoch[3] Batch [50] Speed: 27705.61 samples/sec Train-accuracy=0.977969


INFO:root:Epoch[3] Batch [50]   Speed: 27705.61 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:37:53,148 Node[0] Epoch[3] Batch [50] Speed: 27705.61 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[3] Batch [50]   Speed: 27705.61 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:53,151 Node[0] Epoch[3] Batch [50] Speed: 27705.61 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[3] Batch [50]   Speed: 27705.61 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:53,151 Node[0] Epoch[3] Batch [50] Speed: 27705.61 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[3] Batch [100]  Speed: 26446.30 samples/sec Train-accuracy=0.978281


2016-10-26 19:37:53,395 Node[0] Epoch[3] Batch [100]    Speed: 26446.30 samples/sec Train-accuracy=0.978281


INFO:root:Epoch[3] Batch [100]  Speed: 26446.30 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:37:53,398 Node[0] Epoch[3] Batch [100]    Speed: 26446.30 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[3] Batch [100]  Speed: 26446.30 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:53,400 Node[0] Epoch[3] Batch [100]    Speed: 26446.30 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[3] Batch [100]  Speed: 26446.30 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:53,401 Node[0] Epoch[3] Batch [100]    Speed: 26446.30 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[3] Batch [150]  Speed: 26666.69 samples/sec Train-accuracy=0.977969


2016-10-26 19:37:53,642 Node[0] Epoch[3] Batch [150]    Speed: 26666.69 samples/sec Train-accuracy=0.977969


INFO:root:Epoch[3] Batch [150]  Speed: 26666.69 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:37:53,644 Node[0] Epoch[3] Batch [150]    Speed: 26666.69 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[3] Batch [150]  Speed: 26666.69 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:53,647 Node[0] Epoch[3] Batch [150]    Speed: 26666.69 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[3] Batch [150]  Speed: 26666.69 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:53,648 Node[0] Epoch[3] Batch [150]    Speed: 26666.69 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[3] Batch [200]  Speed: 26229.51 samples/sec Train-accuracy=0.980781


2016-10-26 19:37:53,894 Node[0] Epoch[3] Batch [200]    Speed: 26229.51 samples/sec Train-accuracy=0.980781


INFO:root:Epoch[3] Batch [200]  Speed: 26229.51 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:37:53,895 Node[0] Epoch[3] Batch [200]    Speed: 26229.51 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[3] Batch [200]  Speed: 26229.51 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:53,898 Node[0] Epoch[3] Batch [200]    Speed: 26229.51 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[3] Batch [200]  Speed: 26229.51 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:53,900 Node[0] Epoch[3] Batch [200]    Speed: 26229.51 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[3] Batch [250]  Speed: 26890.77 samples/sec Train-accuracy=0.979531


2016-10-26 19:37:54,141 Node[0] Epoch[3] Batch [250]    Speed: 26890.77 samples/sec Train-accuracy=0.979531


INFO:root:Epoch[3] Batch [250]  Speed: 26890.77 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:37:54,142 Node[0] Epoch[3] Batch [250]    Speed: 26890.77 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[3] Batch [250]  Speed: 26890.77 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:54,144 Node[0] Epoch[3] Batch [250]    Speed: 26890.77 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[3] Batch [250]  Speed: 26890.77 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:54,145 Node[0] Epoch[3] Batch [250]    Speed: 26890.77 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[3] Batch [300]  Speed: 27586.22 samples/sec Train-accuracy=0.979844


2016-10-26 19:37:54,378 Node[0] Epoch[3] Batch [300]    Speed: 27586.22 samples/sec Train-accuracy=0.979844


INFO:root:Epoch[3] Batch [300]  Speed: 27586.22 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:37:54,380 Node[0] Epoch[3] Batch [300]    Speed: 27586.22 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[3] Batch [300]  Speed: 27586.22 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:54,381 Node[0] Epoch[3] Batch [300]    Speed: 27586.22 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[3] Batch [300]  Speed: 27586.22 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:54,384 Node[0] Epoch[3] Batch [300]    Speed: 27586.22 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[3] Batch [350]  Speed: 27467.81 samples/sec Train-accuracy=0.979375


2016-10-26 19:37:54,618 Node[0] Epoch[3] Batch [350]    Speed: 27467.81 samples/sec Train-accuracy=0.979375


INFO:root:Epoch[3] Batch [350]  Speed: 27467.81 samples/sec Train-top_k_accuracy_5=0.999531


2016-10-26 19:37:54,621 Node[0] Epoch[3] Batch [350]    Speed: 27467.81 samples/sec Train-top_k_accuracy_5=0.999531


INFO:root:Epoch[3] Batch [350]  Speed: 27467.81 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:54,621 Node[0] Epoch[3] Batch [350]    Speed: 27467.81 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[3] Batch [350]  Speed: 27467.81 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:54,622 Node[0] Epoch[3] Batch [350]    Speed: 27467.81 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[3] Batch [400]  Speed: 27004.22 samples/sec Train-accuracy=0.982656


2016-10-26 19:37:54,862 Node[0] Epoch[3] Batch [400]    Speed: 27004.22 samples/sec Train-accuracy=0.982656


INFO:root:Epoch[3] Batch [400]  Speed: 27004.22 samples/sec Train-top_k_accuracy_5=0.999531


2016-10-26 19:37:54,865 Node[0] Epoch[3] Batch [400]    Speed: 27004.22 samples/sec Train-top_k_accuracy_5=0.999531


INFO:root:Epoch[3] Batch [400]  Speed: 27004.22 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:54,867 Node[0] Epoch[3] Batch [400]    Speed: 27004.22 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[3] Batch [400]  Speed: 27004.22 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:54,868 Node[0] Epoch[3] Batch [400]    Speed: 27004.22 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[3] Batch [450]  Speed: 27586.19 samples/sec Train-accuracy=0.981094


2016-10-26 19:37:55,101 Node[0] Epoch[3] Batch [450]    Speed: 27586.19 samples/sec Train-accuracy=0.981094


INFO:root:Epoch[3] Batch [450]  Speed: 27586.19 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:37:55,104 Node[0] Epoch[3] Batch [450]    Speed: 27586.19 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[3] Batch [450]  Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:55,105 Node[0] Epoch[3] Batch [450]    Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[3] Batch [450]  Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:55,107 Node[0] Epoch[3] Batch [450]    Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[3] Resetting Data Iterator


2016-10-26 19:37:55,191 Node[0] Epoch[3] Resetting Data Iterator


INFO:root:Epoch[3] Time cost=2.283


2016-10-26 19:37:55,194 Node[0] Epoch[3] Time cost=2.283


INFO:root:Epoch[3] Validation-accuracy=0.974359


2016-10-26 19:37:55,359 Node[0] Epoch[3] Validation-accuracy=0.974359


INFO:root:Epoch[3] Validation-top_k_accuracy_5=0.999199


2016-10-26 19:37:55,361 Node[0] Epoch[3] Validation-top_k_accuracy_5=0.999199


INFO:root:Epoch[3] Validation-top_k_accuracy_10=1.000000


2016-10-26 19:37:55,362 Node[0] Epoch[3] Validation-top_k_accuracy_10=1.000000


INFO:root:Epoch[3] Validation-top_k_accuracy_20=1.000000


2016-10-26 19:37:55,364 Node[0] Epoch[3] Validation-top_k_accuracy_20=1.000000


INFO:root:Epoch[4] Batch [50]   Speed: 27586.19 samples/sec Train-accuracy=0.980938


2016-10-26 19:37:55,601 Node[0] Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-accuracy=0.980938


INFO:root:Epoch[4] Batch [50]   Speed: 27586.19 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:37:55,605 Node[0] Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[4] Batch [50]   Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:55,608 Node[0] Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[4] Batch [50]   Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:55,611 Node[0] Epoch[4] Batch [50] Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[4] Batch [100]  Speed: 27586.19 samples/sec Train-accuracy=0.981406


2016-10-26 19:37:55,844 Node[0] Epoch[4] Batch [100]    Speed: 27586.19 samples/sec Train-accuracy=0.981406


INFO:root:Epoch[4] Batch [100]  Speed: 27586.19 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:37:55,845 Node[0] Epoch[4] Batch [100]    Speed: 27586.19 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[4] Batch [100]  Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:55,846 Node[0] Epoch[4] Batch [100]    Speed: 27586.19 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[4] Batch [100]  Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:55,848 Node[0] Epoch[4] Batch [100]    Speed: 27586.19 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[4] Batch [150]  Speed: 27118.64 samples/sec Train-accuracy=0.981875


2016-10-26 19:37:56,085 Node[0] Epoch[4] Batch [150]    Speed: 27118.64 samples/sec Train-accuracy=0.981875


INFO:root:Epoch[4] Batch [150]  Speed: 27118.64 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:37:56,088 Node[0] Epoch[4] Batch [150]    Speed: 27118.64 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[4] Batch [150]  Speed: 27118.64 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:56,089 Node[0] Epoch[4] Batch [150]    Speed: 27118.64 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[4] Batch [150]  Speed: 27118.64 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:56,091 Node[0] Epoch[4] Batch [150]    Speed: 27118.64 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[4] Batch [200]  Speed: 28571.43 samples/sec Train-accuracy=0.987187


2016-10-26 19:37:56,315 Node[0] Epoch[4] Batch [200]    Speed: 28571.43 samples/sec Train-accuracy=0.987187


INFO:root:Epoch[4] Batch [200]  Speed: 28571.43 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:37:56,318 Node[0] Epoch[4] Batch [200]    Speed: 28571.43 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[4] Batch [200]  Speed: 28571.43 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:56,319 Node[0] Epoch[4] Batch [200]    Speed: 28571.43 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[4] Batch [200]  Speed: 28571.43 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:56,321 Node[0] Epoch[4] Batch [200]    Speed: 28571.43 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[4] Batch [250]  Speed: 27350.43 samples/sec Train-accuracy=0.982812


2016-10-26 19:37:56,555 Node[0] Epoch[4] Batch [250]    Speed: 27350.43 samples/sec Train-accuracy=0.982812


INFO:root:Epoch[4] Batch [250]  Speed: 27350.43 samples/sec Train-top_k_accuracy_5=0.999531


2016-10-26 19:37:56,558 Node[0] Epoch[4] Batch [250]    Speed: 27350.43 samples/sec Train-top_k_accuracy_5=0.999531


INFO:root:Epoch[4] Batch [250]  Speed: 27350.43 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:56,559 Node[0] Epoch[4] Batch [250]    Speed: 27350.43 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[4] Batch [250]  Speed: 27350.43 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:56,559 Node[0] Epoch[4] Batch [250]    Speed: 27350.43 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[4] Batch [300]  Speed: 29493.09 samples/sec Train-accuracy=0.982969


2016-10-26 19:37:56,779 Node[0] Epoch[4] Batch [300]    Speed: 29493.09 samples/sec Train-accuracy=0.982969


INFO:root:Epoch[4] Batch [300]  Speed: 29493.09 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:37:56,780 Node[0] Epoch[4] Batch [300]    Speed: 29493.09 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[4] Batch [300]  Speed: 29493.09 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:56,782 Node[0] Epoch[4] Batch [300]    Speed: 29493.09 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[4] Batch [300]  Speed: 29493.09 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:56,786 Node[0] Epoch[4] Batch [300]    Speed: 29493.09 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[4] Batch [350]  Speed: 26016.25 samples/sec Train-accuracy=0.981563


2016-10-26 19:37:57,036 Node[0] Epoch[4] Batch [350]    Speed: 26016.25 samples/sec Train-accuracy=0.981563


INFO:root:Epoch[4] Batch [350]  Speed: 26016.25 samples/sec Train-top_k_accuracy_5=0.999531


2016-10-26 19:37:57,039 Node[0] Epoch[4] Batch [350]    Speed: 26016.25 samples/sec Train-top_k_accuracy_5=0.999531


INFO:root:Epoch[4] Batch [350]  Speed: 26016.25 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:57,040 Node[0] Epoch[4] Batch [350]    Speed: 26016.25 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[4] Batch [350]  Speed: 26016.25 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:57,042 Node[0] Epoch[4] Batch [350]    Speed: 26016.25 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[4] Batch [400]  Speed: 27118.66 samples/sec Train-accuracy=0.985000


2016-10-26 19:37:57,280 Node[0] Epoch[4] Batch [400]    Speed: 27118.66 samples/sec Train-accuracy=0.985000


INFO:root:Epoch[4] Batch [400]  Speed: 27118.66 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:37:57,282 Node[0] Epoch[4] Batch [400]    Speed: 27118.66 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[4] Batch [400]  Speed: 27118.66 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:57,285 Node[0] Epoch[4] Batch [400]    Speed: 27118.66 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[4] Batch [400]  Speed: 27118.66 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:57,286 Node[0] Epoch[4] Batch [400]    Speed: 27118.66 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[4] Batch [450]  Speed: 26446.30 samples/sec Train-accuracy=0.983281


2016-10-26 19:37:57,530 Node[0] Epoch[4] Batch [450]    Speed: 26446.30 samples/sec Train-accuracy=0.983281


INFO:root:Epoch[4] Batch [450]  Speed: 26446.30 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:37:57,532 Node[0] Epoch[4] Batch [450]    Speed: 26446.30 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[4] Batch [450]  Speed: 26446.30 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:57,533 Node[0] Epoch[4] Batch [450]    Speed: 26446.30 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[4] Batch [450]  Speed: 26446.30 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:57,535 Node[0] Epoch[4] Batch [450]    Speed: 26446.30 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[4] Resetting Data Iterator


2016-10-26 19:37:57,615 Node[0] Epoch[4] Resetting Data Iterator


INFO:root:Epoch[4] Time cost=2.251


2016-10-26 19:37:57,617 Node[0] Epoch[4] Time cost=2.251


INFO:root:Epoch[4] Validation-accuracy=0.972456


2016-10-26 19:37:57,776 Node[0] Epoch[4] Validation-accuracy=0.972456


INFO:root:Epoch[4] Validation-top_k_accuracy_5=0.999299


2016-10-26 19:37:57,776 Node[0] Epoch[4] Validation-top_k_accuracy_5=0.999299


INFO:root:Epoch[4] Validation-top_k_accuracy_10=1.000000


2016-10-26 19:37:57,779 Node[0] Epoch[4] Validation-top_k_accuracy_10=1.000000


INFO:root:Epoch[4] Validation-top_k_accuracy_20=1.000000


2016-10-26 19:37:57,779 Node[0] Epoch[4] Validation-top_k_accuracy_20=1.000000


INFO:root:Epoch[5] Batch [50]   Speed: 27705.63 samples/sec Train-accuracy=0.984219


2016-10-26 19:37:58,019 Node[0] Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-accuracy=0.984219


INFO:root:Epoch[5] Batch [50]   Speed: 27705.63 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:37:58,022 Node[0] Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[5] Batch [50]   Speed: 27705.63 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:58,023 Node[0] Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[5] Batch [50]   Speed: 27705.63 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:58,025 Node[0] Epoch[5] Batch [50] Speed: 27705.63 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[5] Batch [100]  Speed: 28193.83 samples/sec Train-accuracy=0.987344


2016-10-26 19:37:58,253 Node[0] Epoch[5] Batch [100]    Speed: 28193.83 samples/sec Train-accuracy=0.987344


INFO:root:Epoch[5] Batch [100]  Speed: 28193.83 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:37:58,255 Node[0] Epoch[5] Batch [100]    Speed: 28193.83 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[5] Batch [100]  Speed: 28193.83 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:58,256 Node[0] Epoch[5] Batch [100]    Speed: 28193.83 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[5] Batch [100]  Speed: 28193.83 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:58,257 Node[0] Epoch[5] Batch [100]    Speed: 28193.83 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[5] Batch [150]  Speed: 27947.62 samples/sec Train-accuracy=0.986094


2016-10-26 19:37:58,487 Node[0] Epoch[5] Batch [150]    Speed: 27947.62 samples/sec Train-accuracy=0.986094


INFO:root:Epoch[5] Batch [150]  Speed: 27947.62 samples/sec Train-top_k_accuracy_5=0.999531


2016-10-26 19:37:58,489 Node[0] Epoch[5] Batch [150]    Speed: 27947.62 samples/sec Train-top_k_accuracy_5=0.999531


INFO:root:Epoch[5] Batch [150]  Speed: 27947.62 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:58,490 Node[0] Epoch[5] Batch [150]    Speed: 27947.62 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[5] Batch [150]  Speed: 27947.62 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:58,492 Node[0] Epoch[5] Batch [150]    Speed: 27947.62 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[5] Batch [200]  Speed: 28828.84 samples/sec Train-accuracy=0.987031


2016-10-26 19:37:58,714 Node[0] Epoch[5] Batch [200]    Speed: 28828.84 samples/sec Train-accuracy=0.987031


INFO:root:Epoch[5] Batch [200]  Speed: 28828.84 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:37:58,717 Node[0] Epoch[5] Batch [200]    Speed: 28828.84 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[5] Batch [200]  Speed: 28828.84 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:58,719 Node[0] Epoch[5] Batch [200]    Speed: 28828.84 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[5] Batch [200]  Speed: 28828.84 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:58,720 Node[0] Epoch[5] Batch [200]    Speed: 28828.84 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[5] Batch [250]  Speed: 28571.40 samples/sec Train-accuracy=0.984531


2016-10-26 19:37:58,946 Node[0] Epoch[5] Batch [250]    Speed: 28571.40 samples/sec Train-accuracy=0.984531


INFO:root:Epoch[5] Batch [250]  Speed: 28571.40 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:37:58,947 Node[0] Epoch[5] Batch [250]    Speed: 28571.40 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[5] Batch [250]  Speed: 28571.40 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:58,950 Node[0] Epoch[5] Batch [250]    Speed: 28571.40 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[5] Batch [250]  Speed: 28571.40 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:58,953 Node[0] Epoch[5] Batch [250]    Speed: 28571.40 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[5] Batch [300]  Speed: 27826.08 samples/sec Train-accuracy=0.985469


2016-10-26 19:37:59,184 Node[0] Epoch[5] Batch [300]    Speed: 27826.08 samples/sec Train-accuracy=0.985469


INFO:root:Epoch[5] Batch [300]  Speed: 27826.08 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:37:59,187 Node[0] Epoch[5] Batch [300]    Speed: 27826.08 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[5] Batch [300]  Speed: 27826.08 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:59,187 Node[0] Epoch[5] Batch [300]    Speed: 27826.08 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[5] Batch [300]  Speed: 27826.08 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:59,188 Node[0] Epoch[5] Batch [300]    Speed: 27826.08 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[5] Batch [350]  Speed: 28444.46 samples/sec Train-accuracy=0.983125


2016-10-26 19:37:59,415 Node[0] Epoch[5] Batch [350]    Speed: 28444.46 samples/sec Train-accuracy=0.983125


INFO:root:Epoch[5] Batch [350]  Speed: 28444.46 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:37:59,417 Node[0] Epoch[5] Batch [350]    Speed: 28444.46 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[5] Batch [350]  Speed: 28444.46 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:59,418 Node[0] Epoch[5] Batch [350]    Speed: 28444.46 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[5] Batch [350]  Speed: 28444.46 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:59,421 Node[0] Epoch[5] Batch [350]    Speed: 28444.46 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[5] Batch [400]  Speed: 28318.58 samples/sec Train-accuracy=0.987500


2016-10-26 19:37:59,648 Node[0] Epoch[5] Batch [400]    Speed: 28318.58 samples/sec Train-accuracy=0.987500


INFO:root:Epoch[5] Batch [400]  Speed: 28318.58 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:37:59,650 Node[0] Epoch[5] Batch [400]    Speed: 28318.58 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[5] Batch [400]  Speed: 28318.58 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:59,651 Node[0] Epoch[5] Batch [400]    Speed: 28318.58 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[5] Batch [400]  Speed: 28318.58 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:59,653 Node[0] Epoch[5] Batch [400]    Speed: 28318.58 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[5] Batch [450]  Speed: 28571.40 samples/sec Train-accuracy=0.987031


2016-10-26 19:37:59,880 Node[0] Epoch[5] Batch [450]    Speed: 28571.40 samples/sec Train-accuracy=0.987031


INFO:root:Epoch[5] Batch [450]  Speed: 28571.40 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:37:59,881 Node[0] Epoch[5] Batch [450]    Speed: 28571.40 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[5] Batch [450]  Speed: 28571.40 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:37:59,882 Node[0] Epoch[5] Batch [450]    Speed: 28571.40 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[5] Batch [450]  Speed: 28571.40 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:37:59,884 Node[0] Epoch[5] Batch [450]    Speed: 28571.40 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[5] Resetting Data Iterator


2016-10-26 19:37:59,973 Node[0] Epoch[5] Resetting Data Iterator


INFO:root:Epoch[5] Time cost=2.194


2016-10-26 19:37:59,974 Node[0] Epoch[5] Time cost=2.194


INFO:root:Epoch[5] Validation-accuracy=0.974459


2016-10-26 19:38:00,132 Node[0] Epoch[5] Validation-accuracy=0.974459


INFO:root:Epoch[5] Validation-top_k_accuracy_5=0.999199


2016-10-26 19:38:00,134 Node[0] Epoch[5] Validation-top_k_accuracy_5=0.999199


INFO:root:Epoch[5] Validation-top_k_accuracy_10=1.000000


2016-10-26 19:38:00,135 Node[0] Epoch[5] Validation-top_k_accuracy_10=1.000000


INFO:root:Epoch[5] Validation-top_k_accuracy_20=1.000000


2016-10-26 19:38:00,138 Node[0] Epoch[5] Validation-top_k_accuracy_20=1.000000


INFO:root:Epoch[6] Batch [50]   Speed: 29357.81 samples/sec Train-accuracy=0.990156


2016-10-26 19:38:00,361 Node[0] Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-accuracy=0.990156


INFO:root:Epoch[6] Batch [50]   Speed: 29357.81 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:00,364 Node[0] Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[6] Batch [50]   Speed: 29357.81 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:00,365 Node[0] Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[6] Batch [50]   Speed: 29357.81 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:00,367 Node[0] Epoch[6] Batch [50] Speed: 29357.81 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[6] Batch [100]  Speed: 26890.77 samples/sec Train-accuracy=0.989219


2016-10-26 19:38:00,605 Node[0] Epoch[6] Batch [100]    Speed: 26890.77 samples/sec Train-accuracy=0.989219


INFO:root:Epoch[6] Batch [100]  Speed: 26890.77 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:00,607 Node[0] Epoch[6] Batch [100]    Speed: 26890.77 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[6] Batch [100]  Speed: 26890.77 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:00,608 Node[0] Epoch[6] Batch [100]    Speed: 26890.77 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[6] Batch [100]  Speed: 26890.77 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:00,609 Node[0] Epoch[6] Batch [100]    Speed: 26890.77 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[6] Batch [150]  Speed: 27947.62 samples/sec Train-accuracy=0.989219


2016-10-26 19:38:00,839 Node[0] Epoch[6] Batch [150]    Speed: 27947.62 samples/sec Train-accuracy=0.989219


INFO:root:Epoch[6] Batch [150]  Speed: 27947.62 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:00,842 Node[0] Epoch[6] Batch [150]    Speed: 27947.62 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[6] Batch [150]  Speed: 27947.62 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:00,842 Node[0] Epoch[6] Batch [150]    Speed: 27947.62 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[6] Batch [150]  Speed: 27947.62 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:00,845 Node[0] Epoch[6] Batch [150]    Speed: 27947.62 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[6] Batch [200]  Speed: 26890.74 samples/sec Train-accuracy=0.988281


2016-10-26 19:38:01,084 Node[0] Epoch[6] Batch [200]    Speed: 26890.74 samples/sec Train-accuracy=0.988281


INFO:root:Epoch[6] Batch [200]  Speed: 26890.74 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:38:01,088 Node[0] Epoch[6] Batch [200]    Speed: 26890.74 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[6] Batch [200]  Speed: 26890.74 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:01,088 Node[0] Epoch[6] Batch [200]    Speed: 26890.74 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[6] Batch [200]  Speed: 26890.74 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:01,089 Node[0] Epoch[6] Batch [200]    Speed: 26890.74 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[6] Batch [250]  Speed: 27947.59 samples/sec Train-accuracy=0.987969


2016-10-26 19:38:01,322 Node[0] Epoch[6] Batch [250]    Speed: 27947.59 samples/sec Train-accuracy=0.987969


INFO:root:Epoch[6] Batch [250]  Speed: 27947.59 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:01,323 Node[0] Epoch[6] Batch [250]    Speed: 27947.59 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[6] Batch [250]  Speed: 27947.59 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:01,325 Node[0] Epoch[6] Batch [250]    Speed: 27947.59 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[6] Batch [250]  Speed: 27947.59 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:01,326 Node[0] Epoch[6] Batch [250]    Speed: 27947.59 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[6] Batch [300]  Speed: 28070.18 samples/sec Train-accuracy=0.987187


2016-10-26 19:38:01,555 Node[0] Epoch[6] Batch [300]    Speed: 28070.18 samples/sec Train-accuracy=0.987187


INFO:root:Epoch[6] Batch [300]  Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:01,558 Node[0] Epoch[6] Batch [300]    Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[6] Batch [300]  Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:01,559 Node[0] Epoch[6] Batch [300]    Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[6] Batch [300]  Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:01,561 Node[0] Epoch[6] Batch [300]    Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[6] Batch [350]  Speed: 27234.03 samples/sec Train-accuracy=0.981719


2016-10-26 19:38:01,798 Node[0] Epoch[6] Batch [350]    Speed: 27234.03 samples/sec Train-accuracy=0.981719


INFO:root:Epoch[6] Batch [350]  Speed: 27234.03 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:38:01,799 Node[0] Epoch[6] Batch [350]    Speed: 27234.03 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[6] Batch [350]  Speed: 27234.03 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:01,802 Node[0] Epoch[6] Batch [350]    Speed: 27234.03 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[6] Batch [350]  Speed: 27234.03 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:01,803 Node[0] Epoch[6] Batch [350]    Speed: 27234.03 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[6] Batch [400]  Speed: 28193.83 samples/sec Train-accuracy=0.987187


2016-10-26 19:38:02,032 Node[0] Epoch[6] Batch [400]    Speed: 28193.83 samples/sec Train-accuracy=0.987187


INFO:root:Epoch[6] Batch [400]  Speed: 28193.83 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:38:02,035 Node[0] Epoch[6] Batch [400]    Speed: 28193.83 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[6] Batch [400]  Speed: 28193.83 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:02,036 Node[0] Epoch[6] Batch [400]    Speed: 28193.83 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[6] Batch [400]  Speed: 28193.83 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:02,038 Node[0] Epoch[6] Batch [400]    Speed: 28193.83 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[6] Batch [450]  Speed: 28318.58 samples/sec Train-accuracy=0.988750


2016-10-26 19:38:02,265 Node[0] Epoch[6] Batch [450]    Speed: 28318.58 samples/sec Train-accuracy=0.988750


INFO:root:Epoch[6] Batch [450]  Speed: 28318.58 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:02,266 Node[0] Epoch[6] Batch [450]    Speed: 28318.58 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[6] Batch [450]  Speed: 28318.58 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:02,269 Node[0] Epoch[6] Batch [450]    Speed: 28318.58 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[6] Batch [450]  Speed: 28318.58 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:02,270 Node[0] Epoch[6] Batch [450]    Speed: 28318.58 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[6] Resetting Data Iterator


2016-10-26 19:38:02,349 Node[0] Epoch[6] Resetting Data Iterator


INFO:root:Epoch[6] Time cost=2.214


2016-10-26 19:38:02,354 Node[0] Epoch[6] Time cost=2.214


INFO:root:Epoch[6] Validation-accuracy=0.975160


2016-10-26 19:38:02,510 Node[0] Epoch[6] Validation-accuracy=0.975160


INFO:root:Epoch[6] Validation-top_k_accuracy_5=0.999499


2016-10-26 19:38:02,512 Node[0] Epoch[6] Validation-top_k_accuracy_5=0.999499


INFO:root:Epoch[6] Validation-top_k_accuracy_10=1.000000


2016-10-26 19:38:02,513 Node[0] Epoch[6] Validation-top_k_accuracy_10=1.000000


INFO:root:Epoch[6] Validation-top_k_accuracy_20=1.000000


2016-10-26 19:38:02,515 Node[0] Epoch[6] Validation-top_k_accuracy_20=1.000000


INFO:root:Epoch[7] Batch [50]   Speed: 30188.69 samples/sec Train-accuracy=0.989844


2016-10-26 19:38:02,733 Node[0] Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-accuracy=0.989844


INFO:root:Epoch[7] Batch [50]   Speed: 30188.69 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:38:02,736 Node[0] Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[7] Batch [50]   Speed: 30188.69 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:02,736 Node[0] Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[7] Batch [50]   Speed: 30188.69 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:02,739 Node[0] Epoch[7] Batch [50] Speed: 30188.69 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[7] Batch [100]  Speed: 27350.43 samples/sec Train-accuracy=0.990781


2016-10-26 19:38:02,973 Node[0] Epoch[7] Batch [100]    Speed: 27350.43 samples/sec Train-accuracy=0.990781


INFO:root:Epoch[7] Batch [100]  Speed: 27350.43 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:02,974 Node[0] Epoch[7] Batch [100]    Speed: 27350.43 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[7] Batch [100]  Speed: 27350.43 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:02,976 Node[0] Epoch[7] Batch [100]    Speed: 27350.43 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[7] Batch [100]  Speed: 27350.43 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:02,977 Node[0] Epoch[7] Batch [100]    Speed: 27350.43 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[7] Batch [150]  Speed: 27947.62 samples/sec Train-accuracy=0.988125


2016-10-26 19:38:03,207 Node[0] Epoch[7] Batch [150]    Speed: 27947.62 samples/sec Train-accuracy=0.988125


INFO:root:Epoch[7] Batch [150]  Speed: 27947.62 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:38:03,210 Node[0] Epoch[7] Batch [150]    Speed: 27947.62 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[7] Batch [150]  Speed: 27947.62 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:03,210 Node[0] Epoch[7] Batch [150]    Speed: 27947.62 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[7] Batch [150]  Speed: 27947.62 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:03,211 Node[0] Epoch[7] Batch [150]    Speed: 27947.62 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[7] Batch [200]  Speed: 28070.18 samples/sec Train-accuracy=0.985625


2016-10-26 19:38:03,441 Node[0] Epoch[7] Batch [200]    Speed: 28070.18 samples/sec Train-accuracy=0.985625


INFO:root:Epoch[7] Batch [200]  Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:03,444 Node[0] Epoch[7] Batch [200]    Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[7] Batch [200]  Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:03,444 Node[0] Epoch[7] Batch [200]    Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[7] Batch [200]  Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:03,446 Node[0] Epoch[7] Batch [200]    Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[7] Batch [250]  Speed: 27705.63 samples/sec Train-accuracy=0.989219


2016-10-26 19:38:03,677 Node[0] Epoch[7] Batch [250]    Speed: 27705.63 samples/sec Train-accuracy=0.989219


INFO:root:Epoch[7] Batch [250]  Speed: 27705.63 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:03,680 Node[0] Epoch[7] Batch [250]    Speed: 27705.63 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[7] Batch [250]  Speed: 27705.63 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:03,680 Node[0] Epoch[7] Batch [250]    Speed: 27705.63 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[7] Batch [250]  Speed: 27705.63 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:03,683 Node[0] Epoch[7] Batch [250]    Speed: 27705.63 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[7] Batch [300]  Speed: 28070.18 samples/sec Train-accuracy=0.988125


2016-10-26 19:38:03,911 Node[0] Epoch[7] Batch [300]    Speed: 28070.18 samples/sec Train-accuracy=0.988125


INFO:root:Epoch[7] Batch [300]  Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:03,914 Node[0] Epoch[7] Batch [300]    Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[7] Batch [300]  Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:03,915 Node[0] Epoch[7] Batch [300]    Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[7] Batch [300]  Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:03,917 Node[0] Epoch[7] Batch [300]    Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[7] Batch [350]  Speed: 28318.58 samples/sec Train-accuracy=0.989531


2016-10-26 19:38:04,144 Node[0] Epoch[7] Batch [350]    Speed: 28318.58 samples/sec Train-accuracy=0.989531


INFO:root:Epoch[7] Batch [350]  Speed: 28318.58 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:04,144 Node[0] Epoch[7] Batch [350]    Speed: 28318.58 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[7] Batch [350]  Speed: 28318.58 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:04,147 Node[0] Epoch[7] Batch [350]    Speed: 28318.58 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[7] Batch [350]  Speed: 28318.58 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:04,148 Node[0] Epoch[7] Batch [350]    Speed: 28318.58 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[7] Batch [400]  Speed: 30331.76 samples/sec Train-accuracy=0.987812


2016-10-26 19:38:04,361 Node[0] Epoch[7] Batch [400]    Speed: 30331.76 samples/sec Train-accuracy=0.987812


INFO:root:Epoch[7] Batch [400]  Speed: 30331.76 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:38:04,362 Node[0] Epoch[7] Batch [400]    Speed: 30331.76 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[7] Batch [400]  Speed: 30331.76 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:04,365 Node[0] Epoch[7] Batch [400]    Speed: 30331.76 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[7] Batch [400]  Speed: 30331.76 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:04,367 Node[0] Epoch[7] Batch [400]    Speed: 30331.76 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[7] Batch [450]  Speed: 27826.08 samples/sec Train-accuracy=0.990000


2016-10-26 19:38:04,598 Node[0] Epoch[7] Batch [450]    Speed: 27826.08 samples/sec Train-accuracy=0.990000


INFO:root:Epoch[7] Batch [450]  Speed: 27826.08 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:04,599 Node[0] Epoch[7] Batch [450]    Speed: 27826.08 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[7] Batch [450]  Speed: 27826.08 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:04,601 Node[0] Epoch[7] Batch [450]    Speed: 27826.08 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[7] Batch [450]  Speed: 27826.08 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:04,604 Node[0] Epoch[7] Batch [450]    Speed: 27826.08 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[7] Resetting Data Iterator


2016-10-26 19:38:04,687 Node[0] Epoch[7] Resetting Data Iterator


INFO:root:Epoch[7] Time cost=2.172


2016-10-26 19:38:04,690 Node[0] Epoch[7] Time cost=2.172


INFO:root:Epoch[7] Validation-accuracy=0.977564


2016-10-26 19:38:04,842 Node[0] Epoch[7] Validation-accuracy=0.977564


INFO:root:Epoch[7] Validation-top_k_accuracy_5=0.999599


2016-10-26 19:38:04,845 Node[0] Epoch[7] Validation-top_k_accuracy_5=0.999599


INFO:root:Epoch[7] Validation-top_k_accuracy_10=1.000000


2016-10-26 19:38:04,845 Node[0] Epoch[7] Validation-top_k_accuracy_10=1.000000


INFO:root:Epoch[7] Validation-top_k_accuracy_20=1.000000


2016-10-26 19:38:04,848 Node[0] Epoch[7] Validation-top_k_accuracy_20=1.000000


INFO:root:Epoch[8] Batch [50]   Speed: 29629.65 samples/sec Train-accuracy=0.990469


2016-10-26 19:38:05,069 Node[0] Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-accuracy=0.990469


INFO:root:Epoch[8] Batch [50]   Speed: 29629.65 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:05,072 Node[0] Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[8] Batch [50]   Speed: 29629.65 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:05,072 Node[0] Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[8] Batch [50]   Speed: 29629.65 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:05,075 Node[0] Epoch[8] Batch [50] Speed: 29629.65 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[8] Batch [100]  Speed: 28699.55 samples/sec Train-accuracy=0.988594


2016-10-26 19:38:05,299 Node[0] Epoch[8] Batch [100]    Speed: 28699.55 samples/sec Train-accuracy=0.988594


INFO:root:Epoch[8] Batch [100]  Speed: 28699.55 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:05,299 Node[0] Epoch[8] Batch [100]    Speed: 28699.55 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[8] Batch [100]  Speed: 28699.55 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:05,302 Node[0] Epoch[8] Batch [100]    Speed: 28699.55 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[8] Batch [100]  Speed: 28699.55 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:05,303 Node[0] Epoch[8] Batch [100]    Speed: 28699.55 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[8] Batch [150]  Speed: 28070.18 samples/sec Train-accuracy=0.990313


2016-10-26 19:38:05,533 Node[0] Epoch[8] Batch [150]    Speed: 28070.18 samples/sec Train-accuracy=0.990313


INFO:root:Epoch[8] Batch [150]  Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:05,536 Node[0] Epoch[8] Batch [150]    Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[8] Batch [150]  Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:05,538 Node[0] Epoch[8] Batch [150]    Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[8] Batch [150]  Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:05,539 Node[0] Epoch[8] Batch [150]    Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[8] Batch [200]  Speed: 29223.73 samples/sec Train-accuracy=0.991250


2016-10-26 19:38:05,759 Node[0] Epoch[8] Batch [200]    Speed: 29223.73 samples/sec Train-accuracy=0.991250


INFO:root:Epoch[8] Batch [200]  Speed: 29223.73 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:05,762 Node[0] Epoch[8] Batch [200]    Speed: 29223.73 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[8] Batch [200]  Speed: 29223.73 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:05,763 Node[0] Epoch[8] Batch [200]    Speed: 29223.73 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[8] Batch [200]  Speed: 29223.73 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:05,766 Node[0] Epoch[8] Batch [200]    Speed: 29223.73 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[8] Batch [250]  Speed: 26016.25 samples/sec Train-accuracy=0.987969


2016-10-26 19:38:06,016 Node[0] Epoch[8] Batch [250]    Speed: 26016.25 samples/sec Train-accuracy=0.987969


INFO:root:Epoch[8] Batch [250]  Speed: 26016.25 samples/sec Train-top_k_accuracy_5=0.999844


2016-10-26 19:38:06,017 Node[0] Epoch[8] Batch [250]    Speed: 26016.25 samples/sec Train-top_k_accuracy_5=0.999844


INFO:root:Epoch[8] Batch [250]  Speed: 26016.25 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:06,019 Node[0] Epoch[8] Batch [250]    Speed: 26016.25 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[8] Batch [250]  Speed: 26016.25 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:06,022 Node[0] Epoch[8] Batch [250]    Speed: 26016.25 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[8] Batch [300]  Speed: 29223.73 samples/sec Train-accuracy=0.990313


2016-10-26 19:38:06,242 Node[0] Epoch[8] Batch [300]    Speed: 29223.73 samples/sec Train-accuracy=0.990313


INFO:root:Epoch[8] Batch [300]  Speed: 29223.73 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:06,243 Node[0] Epoch[8] Batch [300]    Speed: 29223.73 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[8] Batch [300]  Speed: 29223.73 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:06,244 Node[0] Epoch[8] Batch [300]    Speed: 29223.73 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[8] Batch [300]  Speed: 29223.73 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:06,246 Node[0] Epoch[8] Batch [300]    Speed: 29223.73 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[8] Batch [350]  Speed: 29223.73 samples/sec Train-accuracy=0.988750


2016-10-26 19:38:06,467 Node[0] Epoch[8] Batch [350]    Speed: 29223.73 samples/sec Train-accuracy=0.988750


INFO:root:Epoch[8] Batch [350]  Speed: 29223.73 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:06,469 Node[0] Epoch[8] Batch [350]    Speed: 29223.73 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[8] Batch [350]  Speed: 29223.73 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:06,470 Node[0] Epoch[8] Batch [350]    Speed: 29223.73 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[8] Batch [350]  Speed: 29223.73 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:06,471 Node[0] Epoch[8] Batch [350]    Speed: 29223.73 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[8] Batch [400]  Speed: 29223.73 samples/sec Train-accuracy=0.991250


2016-10-26 19:38:06,693 Node[0] Epoch[8] Batch [400]    Speed: 29223.73 samples/sec Train-accuracy=0.991250


INFO:root:Epoch[8] Batch [400]  Speed: 29223.73 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:06,694 Node[0] Epoch[8] Batch [400]    Speed: 29223.73 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[8] Batch [400]  Speed: 29223.73 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:06,696 Node[0] Epoch[8] Batch [400]    Speed: 29223.73 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[8] Batch [400]  Speed: 29223.73 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:06,697 Node[0] Epoch[8] Batch [400]    Speed: 29223.73 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[8] Batch [450]  Speed: 27350.43 samples/sec Train-accuracy=0.989844


2016-10-26 19:38:06,931 Node[0] Epoch[8] Batch [450]    Speed: 27350.43 samples/sec Train-accuracy=0.989844


INFO:root:Epoch[8] Batch [450]  Speed: 27350.43 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:06,934 Node[0] Epoch[8] Batch [450]    Speed: 27350.43 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[8] Batch [450]  Speed: 27350.43 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:06,934 Node[0] Epoch[8] Batch [450]    Speed: 27350.43 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[8] Batch [450]  Speed: 27350.43 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:06,937 Node[0] Epoch[8] Batch [450]    Speed: 27350.43 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[8] Resetting Data Iterator


2016-10-26 19:38:07,016 Node[0] Epoch[8] Resetting Data Iterator


INFO:root:Epoch[8] Time cost=2.169


2016-10-26 19:38:07,017 Node[0] Epoch[8] Time cost=2.169


INFO:root:Epoch[8] Validation-accuracy=0.976863


2016-10-26 19:38:07,174 Node[0] Epoch[8] Validation-accuracy=0.976863


INFO:root:Epoch[8] Validation-top_k_accuracy_5=0.999700


2016-10-26 19:38:07,174 Node[0] Epoch[8] Validation-top_k_accuracy_5=0.999700


INFO:root:Epoch[8] Validation-top_k_accuracy_10=1.000000


2016-10-26 19:38:07,177 Node[0] Epoch[8] Validation-top_k_accuracy_10=1.000000


INFO:root:Epoch[8] Validation-top_k_accuracy_20=1.000000


2016-10-26 19:38:07,177 Node[0] Epoch[8] Validation-top_k_accuracy_20=1.000000


INFO:root:Epoch[9] Batch [50]   Speed: 29906.54 samples/sec Train-accuracy=0.990625


2016-10-26 19:38:07,398 Node[0] Epoch[9] Batch [50] Speed: 29906.54 samples/sec Train-accuracy=0.990625


INFO:root:Epoch[9] Batch [50]   Speed: 29906.54 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:07,400 Node[0] Epoch[9] Batch [50] Speed: 29906.54 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[9] Batch [50]   Speed: 29906.54 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:07,403 Node[0] Epoch[9] Batch [50] Speed: 29906.54 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[9] Batch [50]   Speed: 29906.54 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:07,404 Node[0] Epoch[9] Batch [50] Speed: 29906.54 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[9] Batch [100]  Speed: 27350.43 samples/sec Train-accuracy=0.987969


2016-10-26 19:38:07,641 Node[0] Epoch[9] Batch [100]    Speed: 27350.43 samples/sec Train-accuracy=0.987969


INFO:root:Epoch[9] Batch [100]  Speed: 27350.43 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:07,641 Node[0] Epoch[9] Batch [100]    Speed: 27350.43 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[9] Batch [100]  Speed: 27350.43 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:07,644 Node[0] Epoch[9] Batch [100]    Speed: 27350.43 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[9] Batch [100]  Speed: 27350.43 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:07,644 Node[0] Epoch[9] Batch [100]    Speed: 27350.43 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[9] Batch [150]  Speed: 28444.46 samples/sec Train-accuracy=0.990469


2016-10-26 19:38:07,871 Node[0] Epoch[9] Batch [150]    Speed: 28444.46 samples/sec Train-accuracy=0.990469


INFO:root:Epoch[9] Batch [150]  Speed: 28444.46 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:07,874 Node[0] Epoch[9] Batch [150]    Speed: 28444.46 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[9] Batch [150]  Speed: 28444.46 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:07,875 Node[0] Epoch[9] Batch [150]    Speed: 28444.46 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[9] Batch [150]  Speed: 28444.46 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:07,877 Node[0] Epoch[9] Batch [150]    Speed: 28444.46 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[9] Batch [200]  Speed: 28070.18 samples/sec Train-accuracy=0.992969


2016-10-26 19:38:08,105 Node[0] Epoch[9] Batch [200]    Speed: 28070.18 samples/sec Train-accuracy=0.992969


INFO:root:Epoch[9] Batch [200]  Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:08,108 Node[0] Epoch[9] Batch [200]    Speed: 28070.18 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[9] Batch [200]  Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:08,111 Node[0] Epoch[9] Batch [200]    Speed: 28070.18 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[9] Batch [200]  Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:08,111 Node[0] Epoch[9] Batch [200]    Speed: 28070.18 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[9] Batch [250]  Speed: 27586.22 samples/sec Train-accuracy=0.992344


2016-10-26 19:38:08,345 Node[0] Epoch[9] Batch [250]    Speed: 27586.22 samples/sec Train-accuracy=0.992344


INFO:root:Epoch[9] Batch [250]  Speed: 27586.22 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:08,348 Node[0] Epoch[9] Batch [250]    Speed: 27586.22 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[9] Batch [250]  Speed: 27586.22 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:08,349 Node[0] Epoch[9] Batch [250]    Speed: 27586.22 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[9] Batch [250]  Speed: 27586.22 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:08,351 Node[0] Epoch[9] Batch [250]    Speed: 27586.22 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[9] Batch [300]  Speed: 28070.16 samples/sec Train-accuracy=0.989062


2016-10-26 19:38:08,581 Node[0] Epoch[9] Batch [300]    Speed: 28070.16 samples/sec Train-accuracy=0.989062


INFO:root:Epoch[9] Batch [300]  Speed: 28070.16 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:08,582 Node[0] Epoch[9] Batch [300]    Speed: 28070.16 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[9] Batch [300]  Speed: 28070.16 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:08,584 Node[0] Epoch[9] Batch [300]    Speed: 28070.16 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[9] Batch [300]  Speed: 28070.16 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:08,585 Node[0] Epoch[9] Batch [300]    Speed: 28070.16 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[9] Batch [350]  Speed: 28699.55 samples/sec Train-accuracy=0.989531


2016-10-26 19:38:08,809 Node[0] Epoch[9] Batch [350]    Speed: 28699.55 samples/sec Train-accuracy=0.989531


INFO:root:Epoch[9] Batch [350]  Speed: 28699.55 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:08,812 Node[0] Epoch[9] Batch [350]    Speed: 28699.55 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[9] Batch [350]  Speed: 28699.55 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:08,813 Node[0] Epoch[9] Batch [350]    Speed: 28699.55 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[9] Batch [350]  Speed: 28699.55 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:08,815 Node[0] Epoch[9] Batch [350]    Speed: 28699.55 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[9] Batch [400]  Speed: 28571.43 samples/sec Train-accuracy=0.989375


2016-10-26 19:38:09,040 Node[0] Epoch[9] Batch [400]    Speed: 28571.43 samples/sec Train-accuracy=0.989375


INFO:root:Epoch[9] Batch [400]  Speed: 28571.43 samples/sec Train-top_k_accuracy_5=0.999687


2016-10-26 19:38:09,042 Node[0] Epoch[9] Batch [400]    Speed: 28571.43 samples/sec Train-top_k_accuracy_5=0.999687


INFO:root:Epoch[9] Batch [400]  Speed: 28571.43 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:09,043 Node[0] Epoch[9] Batch [400]    Speed: 28571.43 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[9] Batch [400]  Speed: 28571.43 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:09,046 Node[0] Epoch[9] Batch [400]    Speed: 28571.43 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[9] Batch [450]  Speed: 28828.81 samples/sec Train-accuracy=0.991406


2016-10-26 19:38:09,269 Node[0] Epoch[9] Batch [450]    Speed: 28828.81 samples/sec Train-accuracy=0.991406


INFO:root:Epoch[9] Batch [450]  Speed: 28828.81 samples/sec Train-top_k_accuracy_5=1.000000


2016-10-26 19:38:09,270 Node[0] Epoch[9] Batch [450]    Speed: 28828.81 samples/sec Train-top_k_accuracy_5=1.000000


INFO:root:Epoch[9] Batch [450]  Speed: 28828.81 samples/sec Train-top_k_accuracy_10=1.000000


2016-10-26 19:38:09,272 Node[0] Epoch[9] Batch [450]    Speed: 28828.81 samples/sec Train-top_k_accuracy_10=1.000000


INFO:root:Epoch[9] Batch [450]  Speed: 28828.81 samples/sec Train-top_k_accuracy_20=1.000000


2016-10-26 19:38:09,273 Node[0] Epoch[9] Batch [450]    Speed: 28828.81 samples/sec Train-top_k_accuracy_20=1.000000


INFO:root:Epoch[9] Resetting Data Iterator


2016-10-26 19:38:09,355 Node[0] Epoch[9] Resetting Data Iterator


INFO:root:Epoch[9] Time cost=2.179


2016-10-26 19:38:09,358 Node[0] Epoch[9] Time cost=2.179


INFO:root:Epoch[9] Validation-accuracy=0.973958


2016-10-26 19:38:09,522 Node[0] Epoch[9] Validation-accuracy=0.973958


INFO:root:Epoch[9] Validation-top_k_accuracy_5=0.999299


2016-10-26 19:38:09,523 Node[0] Epoch[9] Validation-top_k_accuracy_5=0.999299


INFO:root:Epoch[9] Validation-top_k_accuracy_10=1.000000


2016-10-26 19:38:09,525 Node[0] Epoch[9] Validation-top_k_accuracy_10=1.000000


INFO:root:Epoch[9] Validation-top_k_accuracy_20=1.000000


2016-10-26 19:38:09,526 Node[0] Epoch[9] Validation-top_k_accuracy_20=1.000000</code>
    

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:opencvuildinclude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:opencvuildx64vc12libopencv_core2413.lib

参考

1.mxnet配置安装 2.https://github.com/dmlc/mxnet/issues/655

原文：http://www.cnblogs.com/wacc/p/6096785.html