最近在摸mxnet和tensorflow。两个我都搭起来了。tensorflow跑了不少代码，总的来说用得比较顺畅，文档很丰富，api熟悉熟悉写代码没什么问题。

今天把两个平台做了一下对比。同是跑mnist，tensorflow 要比mxnet 慢一二十倍。mxnet只需要半分钟，tensorflow跑了13分钟。

在mxnet中如何开跑？

cd /mxnet/example/image-classification
python train_mnist.py

我用的是最新的mxnet版本。运行脚本它会自动下载数据集。然后刷刷刷的刷屏了。我们来看看这个脚本如何写的，从而建立mxnet编程思路：import find_mxnetimport mxnet as mximport argparseimport os, sysimport train_model

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

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

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

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

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

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

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

        return (train, val)    return get_iterator_impl

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

if __name__ == '__main__':    args = parse_args()

    if args.network == 'mlp':        data_shape = (784, )        net = get_mlp()    elif args.network == 'lenet-stn':        data_shape = (1, 28, 28)        net = get_lenet(True)    else:        data_shape = (1, 28, 28)        net = get_lenet()

    # train    train_model.fit(args, net, get_iterator(data_shape))

先看Main函数，就是读配置参数，读网络结构，包括设置数据的大小，然后就是调用已有的包train_model。然后传入这之前设置的三个参数。就开始训练了。编程架构也蛮清晰的。模块化也搞的好。接着看看参数设置问题。参数导入了很多配置文件，基本上caffe中的Proto都在这个里面设置了。包括数据集地址，批大小，学习率，损失函数，等等。然后看看读网络结构，读网络结构就是在一层一层的搭积木，根据之前读入的配置文件或者自己定义一些参数。搭好积木就开始训练了。caffe的一个缺点是不够灵活，毕竟不是自己写代码，只是写配置文件，总感觉受制于人。mxnet和tensorflow就比较方便，提供api，你可以按你的方式来调用和定义网络结构。总的说来，其实是后两个框架模块化做的好，提供底层的api支持你写自己的网络。caffe要自己写网络层的话还是很费劲的