I tried to reduce overfitting using two hidden layers with dropout=0.5 On Monday, August 1, 2016 at 11:54:16 AM UTC+2, [email protected] wrote: > > Hi all, > I'm trying to train a 3D convnet using only half of the images because of > lack of the graphic card Tesla K40 Nvidia memory. > The convnet has two classify two different type of images: I use 574 > images for training and 102 images for validation. > > The training cost starts with a value of 0.70964 and after 500 epochs (3,3 > days) is converging almost to zero wit a value of 0.06942, while the > validation error starts with a value of 45.098 % and after 500 epochs is > asymptotically reduced to a value of 25.225 %. > If I test the convnet when it has to classify simple and small 3D > objects, training cost and validation error are both converging to zero > after a while. > > Training cost is defined as: > -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y]) > Validation error is defined as: T.mean(T.neq(self.y_pred, y)) > > I thank you very much for your help. > > Python 2.7.11 |Anaconda 4.0.0 (64-bit)| (default, Dec 6 2015, 18:08:32) > [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2 > Type "help", "copyright", "credits" or "license" for more information. > Anaconda is brought to you by Continuum Analytics. > Please check out: http://continuum.io/thanks and https://anaconda.org > >>> import run_multi_conv > Using gpu device 0: Tesla K40c > >>> run_multi_conv.run_experiments() > > > start time: > 28/07/2016 > 14:18:42 > > > images for training: 574 > images for validation: 102 > epochs: 500 > > > ... training neural network 27 > > > training @ iter = 0 > training @ iter = 200 > training @ iter = 400 > > > training cost 0.70964 > epoch 1, training batch 574/574,validation error 45.098 % > training @ iter = 600 > training @ iter = 800 > training @ iter = 1000 > > > training cost 0.70255 > epoch 2, training batch 574/574,validation error 45.098 % > training @ iter = 1200 > training @ iter = 1400 > training @ iter = 1600 > > > ----------------- > > ... training neural network 27 > > > training cost 0.06980 > epoch 496, training batch 574/574,validation error 25.237 % > training @ iter = 284800 > training @ iter = 285000 > training @ iter = 285200 > > > training cost 0.06968 > epoch 497, training batch 574/574,validation error 25.234 % > training @ iter = 285400 > training @ iter = 285600 > training @ iter = 285800 > > > training cost 0.06955 > epoch 498, training batch 574/574,validation error 25.232 % > training @ iter = 286000 > training @ iter = 286200 > training @ iter = 286400 > > > training cost 0.06942 > epoch 499, training batch 574/574,validation error 25.231 % > training @ iter = 286600 > training @ iter = 286800 > > > ... training neural network 27 > > > training cost 0.06930 > epoch 500, training batch 574/574,validation error 25.225 % > > > Best validation error of 25.23 % obtained at iteration 287000, > > > The neural network for file mpr_convnet_class.so ran for 4859.41m > ---------- > > > > > > > graphic card used: TeslaK40 > > +------------------------------------------------------+ > > > | NVIDIA-SMI 352.93 Driver Version: 352.93 > | > > |-------------------------------+----------------------+----------------------+ > | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. > ECC | > | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute > M. | > > |===============================+======================+======================| > | 0 Tesla K40c Off | 0000:04:00.0 Off > | 0 | > | 26% 53C P0 65W / 235W | 7326MiB / 11519MiB | 0% > Default | > > +-------------------------------+----------------------+----------------------+ > > > > > +-----------------------------------------------------------------------------+ > | Processes: GPU > Memory | > | GPU PID Type Process name > Usage | > > |=============================================================================| > | 0 4526 C python > 7301MiB | > > +-----------------------------------------------------------------------------+ > > > > >
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