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 > >
