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 >> >>
