Hi Vincenzo, many thanks for your quick response! Best, Christos On Tuesday, 12 July 2016 11:34:00 UTC+3, Vincenzo Lomonaco wrote: > > Hi Christos, > > You can download the original version from here > <http://www.cs.nyu.edu/%7Eylclab/data/norb-v1.0/> and follow the > instruction to convert it to the pickle format here > <http://deeplearning.net/tutorial/gettingstarted.html>. > > Best regards, > Vincenzo > > Il 12/07/2016 09:38, Geppetto Null ha scritto: > > Hi Vincenzo, could you please help me find the NORB dataset in > Theano/Lasagne format? > > Thank you very much, > Best, > Christos > > On Thursday, 11 June 2015 00:38:04 UTC+3, Vincenzo Lomonaco wrote: >> >> Hello everyone, >> >> I am trying to reproduce with Theano the results obtained on the small >> NORB dataset and reported in the paper "Learning Methods for Generic Object >> Recognition with Invariance to Pose and Lighting" [ Huang, LeCun - >> http://yann.lecun.com/exdb/publis/pdf/lecun-04.pdf ] using CNNs. >> >> Starting from the LeNet tutorial [ >> <http://deeplearning.net/tutorial/lenet.html>http://deeplearning.net/tutorial/lenet.html >> >> ] I changed the model to fit what described in the paper but I can't get >> the error rate below *8,7%* while in the paper is reported as *6,8%*. >> >> Has anyone tried this before? >> >> Here the model details: >> >> nkerns=[8, 24] >> >> layer0_input = x.reshape((batch_size, 2, 96, 96)) >> >> layer0 = LeNetConvPoolLayer( >> rng, >> input=layer0_input, >> image_shape=(batch_size, in_dim, img_dim, img_dim), >> filter_shape=(nkerns[0], in_dim, 5, 5), >> poolsize=(4,4), >> pool_type='max' >> ) >> >> layer1 = LeNetConvPoolLayer( >> rng, >> input=layer0.output, >> image_shape=(batch_size, nkerns[0], 23, 23), >> filter_shape=(nkerns[1], nkerns[0], 6, 6), >> poolsize=(3,3), >> pool_type='max' >> ) >> >> layer2_input = layer1.output.flatten(2) >> >> layer2 = HiddenLayer( >> rng, >> input=layer2_input, >> n_in=nkerns[1] * 6 * 6, >> n_out=batch_size, >> activation=T.tanh >> ) >> >> layer3 = LogisticRegression(input=layer2.output, n_in=batch_size, >> n_out=5) >> >> >> >> I've also tried sum and average pooling other than max, and implemented >> dropout for the hidden layer but without great improvements. >> >> Do you think that the problem is the full-connected convolution >> operation? >> Does anyone has an example code to select input feature maps in a >> convolutional layer? >> Any suggestion? >> > -- > > --- > You received this message because you are subscribed to a topic in the > Google Groups "theano-users" group. > To unsubscribe from this topic, visit > https://groups.google.com/d/topic/theano-users/vBfxxWQODNw/unsubscribe. > To unsubscribe from this group and all its topics, send an email to > [email protected] <javascript:>. > For more options, visit https://groups.google.com/d/optout. > > > -- > > Vincenzo Lomonaco, M.Sc. > PhD student @ University of Bologna > <http://www.vincenzolomonaco.com/>http://www.vincenzolomonaco.com/ > Linkedin <http://it.linkedin.com/in/vincenzolomonaco/> Twitter > <https://twitter.com/v_lomonaco> Facebook > <https://www.facebook.com/vincenzo.lomonaco.91> Google+ > <https://plus.google.com/u/0/+VincenzoLomonaco> >
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