Getting `ERROR: LoadError: UndefVarError: @mxcall not defined` which is odd..
On Friday, 8 April 2016 10:18:03 UTC+2, kleinsplash wrote: > > Hi, > > I reduced the workspace to 1024 and batch_size to 1 - I get the memory > error same as before. I went back to using the whole batch and its running > again. > > Thx for the other stuff will look into that now.. > > > > On Friday, 8 April 2016 09:53:18 UTC+2, Valentin Churavy wrote: >> >> Also take a look at https://github.com/dmlc/MXNet.jl/pull/73 where I >> implemented debug_str in Julia so that you can test your network on its >> space requirements. >> >> On Friday, 8 April 2016 15:54:06 UTC+9, Valentin Churavy wrote: >>> >>> What happens if you set the batch_size to 1? Also take a look at >>> https://github.com/dmlc/mxnet/tree/master/example/memcost >>> >>> Also workspace is per convolution and you should keep it small. >>> >>> On Thursday, 7 April 2016 19:13:36 UTC+9, kleinsplash wrote: >>>> >>>> Hi, >>>> >>>> I have a memory error using Quadro K5000M which has 4GB global memory. >>>> I was wondering if there is some guide as to how to set my workspace and >>>> Convolutional layers. >>>> >>>> My current settings: >>>> >>>> training_data = 128x128x1x800 >>>> batch_size = 128x128x1x8 >>>> workspace = 2048 (I think this can go up to 4096 because of the >>>> .deviceQuery) >>>> >>>> This is my net (still to be designed so just basic ): >>>> >>>> # first conv >>>> conv1 = @mx.chain mx.Convolution(data=data, kernel=(5,5), num_filter=20 >>>> , workspace=workspace) => >>>> mx.Activation(act_type=:relu) => >>>> mx.Pooling(pool_type=:max, kernel=(2,2), stride=(2,2 >>>> )) >>>> # second conv >>>> conv2 = @mx.chain mx.Convolution(data=conv1, kernel=(5,5), num_filter= >>>> 50, workspace=workspace) => >>>> mx.Activation(act_type=:relu) => >>>> mx.Pooling(pool_type=:max, kernel=(2,2), stride=(2,2 >>>> )) >>>> # first fully-connected >>>> fc1 = @mx.chain mx.Flatten(data=conv2) => >>>> mx.FullyConnected(num_hidden=500) => >>>> mx.Activation(act_type=:relu) >>>> # second fully-connected >>>> fc2 = mx.FullyConnected(data=fc1, num_hidden=10) >>>> # third fully-connected >>>> fc3 = mx.FullyConnected(data=fc2, num_hidden=2) >>>> # softmax loss >>>> net = mx.SoftmaxOutput(data=fc3, name=:softmax) >>>> >>>> So far if I reduce my image to 28x28 it all works - but I need to up >>>> the resolution to pick out features. Anyone have any ideas on thumb >>>> sucking >>>> initial values for at least getting past memory issues to the design of >>>> the >>>> net? >>>> >>>> >>>> Thx >>>> >>>>
