Hi,

According to the message below, you are still using conv3d2d instead of cuDNN 
for convolutions. Is that on purpose?

On Wed, Oct 12, 2016, luca.wagner.0...@gmail.com wrote:
> Hi Fred,
> now it works on a small test convnet withoyut delay; tomorrow I'll try  the 
> big one on Tesla K40.
> Many thanks
> 
> I Installed Theano==0.9.0.dev3 from  gvtulder/Theano forked from 
> Theano/Theano: https://github.com/Theano/Theano/pull/4862
> 
> .theanorc is:
> [global]
> floatX = float16
> device=cuda
> [cuda] 
> root = /usr/local/cuda-7.5
> 
> 
> 
> [nvcc]
> fastmath=True
> 
> optimizer = fast_compile
> 
> [dnn.conv]
> algo_fwd =  time_once
> algo_bwd_filter = time_once
> algo_bwd_data = time_once 
> 
> This the output:
> Python 2.7.12 |Anaconda custom (64-bit)| (default, Jul  2 2016, 17:42:40) 
> [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
> >>> 
> runfile('/home/luca/data/DeepLearningTutorials/Theano-3D-ConvNet-master/convnet3d/core/run_multi_conv_t.py',
>  
> wdir='/home/luca/data/DeepLearningTutorials/Theano-3D-ConvNet-master/convnet3d/core')
> Mapped name None to device cuda: GeForce 840M
> Using cuDNN version 5103 on context None
> Disabling C code for Elemwise{mul,no_inplace} due to unsupported float16
> Disabling C code for Elemwise{Cast{float32}} due to unsupported float16
> Disabling C code for DiagonalSubtensor{inplace} due to unsupported float16
> Disabling C code for IncDiagonalSubtensor due to unsupported float16
> Disabling C code for DiagonalSubtensor{inplace} due to unsupported float16
> Disabling C code for MaxAndArgmax due to unsupported float16
> 
> 
> start time:
> 12/10/2016
> 14:20:32
> 
> 
> training @ iter =  0
> training cost 0.69320
> epoch 1, training batch 316/316, validation error 39.286 %
> training @ iter =  400
> training cost 0.69127
> epoch 2, training batch 316/316, validation error 36.607 %
> training @ iter =  800
> training cost 0.68616
> epoch 3, training batch 316/316, validation error 33.929 %
> training @ iter =  1200
> training cost 0.68399
> epoch 4, training batch 316/316, validation error 33.036 %
> training cost 0.67953
> epoch 5, training batch 316/316, validation error 29.643 %
> 
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> 
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-- 
Pascal

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