Where communities thrive


  • Join over 1.5M+ people
  • Join over 100K+ communities
  • Free without limits
  • Create your own community
People
Repo info
Activity
    Zheng Xu
    @XericZephyr
    You will probably need to shuffle your training and testing data before that.
    The function of shuffle in ImageRecordIter is currently weak.
    So you can do shuffle the lines in the image list several times.
    Sheng Wang
    @shengwa
    Data shuffling may be one problem. You can also test different learning rates.
    Kornél
    @korneil
    data is shuffled. what is the range of learning rate you suggest to try out?
    sunsocool
    @sunsocool
    Hello Guys. I am founding a Deep Learning International WeChat group. I would like to invite you to join this WeChat group.
    The mission of this group is not calling for papers but calling for awesome products.
    I wrote the group member introduction in this post and the link is:
    https://www.linkedin.com/pulse/deep-learning-international-wechat-group-welcome-join-lin-sun
    My WeChat ID is sunsocool, Add my WeChat with message "MxNet" and I'll send you the group invitation.
    Thank you guys. Welcome to join the group!
    Qingsong Liu
    @pineking
    @sunsocool
    Tong Guo
    @guotong1988
    @sunsocool
    Deep Learning International WeChat Group,I add
    sunsocool
    @sunsocool
    @guotong1988 @pineking Welcome you guys join Deep Learning International WeChat group~ :clap:
    Parag K Mital
    @pkmital
    Hi I'm interested in using the pretrained 21k ImageNet model, though not sure how to get started. I have installed mxnet, and found the model here: https://github.com/dmlc/mxnet-model-gallery/tree/master/imagenet-21k-inception - though not sure how to load the parameters into a network for prediction?
    Parag K Mital
    @pkmital
    ok, nevermind, figured it out... first create a FeedForward net, then call load with the string 'Inception' and the epoch number...
    sunsocool
    @sunsocool
    @tw991 Welcome you join Deep Learning International WeChat group~ :clap:
    Tian Wang
    @tw991
    @sunsocool Deep Learning International WeChat Group. Thanks for inviting!
    Kornél
    @korneil
    i wrote a new operator for test purposes. how can I include in the build?
    oh I see Makefile reads it
    Chris DuBois
    @doobwa
    What happened to mx.sym.Embedding? It's still used by https://github.com/dmlc/mxnet/blob/master/example/rnn/lstm.py, but I don't see it in v0.5.0.
    Zheng Xu
    @XericZephyr
    I think you need to git pull the newest mxnet source code and rebuild the entire project. Embedding seems to be a new operator in C++ implementation.
    sunsocool
    @sunsocool
    @XericZephyr Welcome you join Deep Learning International WeChat group~ :clap:
    baoqp
    @baoqingping
    @sunsocool Deep Learning International WeChat Group. Thanks for inviting!
    sunsocool
    @sunsocool
    @baoqingping Welcome you join Deep Learning International WeChat group~ :clap:
    Chris DuBois
    @doobwa
    @XericZephyr That worked. Thanks!
    Another question: I'm trying to use my compiled MXNet from Julia. I set MXNET_HOME, but I get:
    INFO: MXNET_HOME environment detected: /home/chris/mxnet INFO: Trying to load existing libmxnet... INFO: Failed to load existing libmxnet, trying to build from source...
    Any ideas why a given libmxnet.so might fail to load?
    baoqp
    @baoqingping
    Does anyone run the example adversary. I met an error
    baoqp
    @baoqingping

    for data, label in train_iter:
    ValueError: too many values to unpack
    then I modified the code as follows:

    for data, label, pad ,index in train_iter:

    another error occured: TypeError: type <type 'list'> not supported at arg_map["data"][:] = data
    again I modified the code and it worked

    for data, label, pad ,index in train_iter:
            data = train_iter.getdata()
            label = train_iter.getlabel()

    I wonder is it necessory to use getdata() and getlabel()

    Tianqi Chen
    @tqchen
    @doobwa This means you did not build mxnet in the path
    you can open an issue on MXNet.jl to ask chiyuan
    Chris DuBois
    @doobwa
    @tqchen I'm pretty certain I built mxnet, and exported MXNET_HOME. Started an issue here: dmlc/MXNet.jl#41. Thanks!
    zhubuntu
    @zhubuntu
    This message was deleted
    This message was deleted
    This message was deleted
    This message was deleted
    zhubuntu
    @zhubuntu

    i can't enter the main function with this code in my program

    mshadow::TShape f;
        f = mshadow::Shape2(3, 5);
        mxnet::Context d = mxnet::Context::Create(mxnet::Context::kCPU, 1);
        mxnet::NDArray a(f, d, false);

    but it worked with

    mxnet::NDArray c;

    who can help me

    sunsocool
    @sunsocool
    @zhubuntu How did you delete your chat history. Why I can't delete my Chat history?
    zhubuntu
    @zhubuntu
    @sunsocool put the mouse on the right top of the message you send.
    sunsocool
    @sunsocool
    @zhubuntu My Edit and Delete button both grey , I cannot click the button~
    sunsocool
    @sunsocool
    This message was deleted
    sunsocool
    @sunsocool
    @zhubuntu I seems that I can only delete the lastest message I just sent. I cannot delete it when it is not the lastest message in the room.
    Hang Su
    @suhangpro

    I found two examples about making predictions using pre-trained models:
    1) mxnet/example/notebooks/predict-with-pretrained-model.ipynb
    2) mxnet/tests/python/predict/mxnet_predict_example.py

    In 2) there is an explicit conversion from rgb to bgr:

    sample = sample[:, :, [2,1,0]]
    however there's no such conversion in 1). Which one should be fixed?

    freepjf
    @freepjf
    i want to know can there any w
    can there any c/c++ exmples for training with mxnet
    i can see only phython and java exmples in the doc but no c,
    Brian C
    @NOTtheMessiah
    don't have enough RAM to run deepstyle above max-edge-width 350, what can other consumer video cards run deepstyle at?
    maybe a better question might be how does RAM usage scale with width? I assume O(X^2) for width X, which should be easy to test
    Brian C
    @NOTtheMessiah
    Why does out.infer_shape(data=(1, 3, 192, 192)) work within Python, but mx.infer_shape(out,data=(1,3, 192,192)) doesn't seem to work in Julia?

    this is what I'm getting:
    ./dmlc-core/include/dmlc/logging.h:208: [14:35:29] src/operator/./convolution-inl.h:323: Check failed: ksize_x <= dshape[3] && ksize_y <= dshape[2] kernel size exceed input
    LoadError: MXNet.mx.MXError("[14:35:29] src/operator/./convolution-inl.h:323: Check failed: ksize_x <= dshape[3] && ksize_y <= dshape[2] kernel size exceed input")
    while loading In[7], in expression starting on line 1

    [inlined code] from ~/.julia/v0.4/MXNet/src/base.jl:57
    in infer_shape#3 at ~/.julia/v0.4/MXNet/src/symbolic-node.jl:253

    Brian C
    @NOTtheMessiah
    I figured it out, it seems to be backwards, because mx.infer_shape(out,data=(192,192,1,3)) gets no errors
    Adam
    @AdamHibble
    Hey guys,
    What is the status on the scala team? I am writing a deep learning with Scala series, would love to include mxnet.
    Yuan (Terry) Tang
    @terrytangyuan
    Hi Adam, thanks for your interest! You can watch our progress in my forked copy of mxnet.