Hi Frederic,
I am pre-processing the image data again to regenerate the training
and validation .hkl image files. I found in my code (alexnet\train.py) that
the program crashes before it completes the first iteration, i.e., as it
looks at the first of 5003 minibatches. In order to make room on my
external solid state hard drive, I deleted the training and validation file
folders named train_(or val_)hkl_b256_b_128, which I don't think are used
but take up 237GB of space... and kept the folders named train_(or
val_)hkl_b256_b_256. Perhaps in another day or two when the 1.2 M images
are reshaped into 5003 files each containing 256 images that are size (256
x 256)... then I can try to run the train.py again and see if the errors
correct themselves.
This may have been my mistake for wanting to save space for my neural
net model output (weights and biases).
Best,
Arnold
On Wednesday, August 16, 2017 at 10:00:43 PM UTC-4, nouiz wrote:
>
> I think the problem are the values in the index vector. Double check that.
>
> Frédéric
>
> On Wed, Aug 16, 2017 at 5:49 PM ephi5757 via theano-users <
> [email protected] <javascript:>> wrote:
>
>> I'm retraining my implementation of the neural network model AlexNet in
>> Theano and not long after it initializes the program crashes with the error
>> "ValueError: dimension mismatch in x,y_idx arguments." see traceback below.
>> Any comments or suggestions that you may offer would be helpful. Note
>> that the only discernible difference in this training in comparison to the
>> previous one is that I am using 5003 .hkl training image data files instead
>> of 5004. Nevertheless, I don't think this value needs to be fixed.
>> Looking forward to your reply.
>> Arnold
>> _______________________________________________________________.
>>
>>
>> C:\SciSoft\Git\theano_alexnet>python train.py THEANO_FLAGS=mode=FAST_RUN,
>> floatX=float32
>> Using gpu device 0: Quadro K4000M (CNMeM is disabled, CuDNN 3007)
>> Using gpu device 0: Quadro K4000M (CNMeM is disabled, CuDNN 3007)
>> ... building the model
>> conv (cudnn) layer with shape_in: (3, 227, 227, 1)
>> conv (cudnn) layer with shape_in: (96, 27, 27, 1)
>> conv (cudnn) layer with shape_in: (256, 13, 13, 1)
>> conv (cudnn) layer with shape_in: (384, 13, 13, 1)
>> conv (cudnn) layer with shape_in: (384, 13, 13, 1)
>> fc layer with num_in: 9216 num_out: 4096
>> dropout layer with P_drop: 0.5
>> fc layer with num_in: 4096 num_out: 4096
>> dropout layer with P_drop: 0.5
>> softmax layer with num_in: 4096 num_out: 1000
>> ... training
>>
>>
>> ______________________________________________________________________________.
>> Traceback (most recent call last):
>> File
>> "C:\SciSoft\WinPython-64bit-2.7.9.4\python-2.7.9.amd64\lib\multiprocessing\process.py",
>>
>> line 266, in _bootstrap
>> self.run()
>> File
>> "C:\SciSoft\WinPython-64bit-2.7.9.4\python-2.7.9.amd64\lib\multiprocessing\process.py",
>>
>> line 120, in run
>> self._target(*self._args, **self._kwargs)
>> File "C:\SciSoft\Git\theano_alexnet\train.py", line 128, in train_net
>> recv_queue=load_recv_queue)
>> File "C:\SciSoft\Git\theano_alexnet\train_funcs.py", line 171, in
>> train_model_wrap
>> cost_ij = train_model()
>> File "c:\scisoft\git\theano\theano\compile\function_module.py", line
>> 871, in __call__
>> storage_map=getattr(self.fn, 'storage_map', None))
>> File "c:\scisoft\git\theano\theano\gof\link.py", line 314, in
>> raise_with_op
>> reraise(exc_type, exc_value, exc_trace)
>> File "c:\scisoft\git\theano\theano\compile\function_module.py", line
>> 859, in __call__
>> outputs = self.fn()
>>
>> ValueError: dimension mismatch in x,y_idx arguments
>> Apply node that caused the error:
>> GpuCrossentropySoftmaxArgmax1HotWithBias(GpuDot22.0,
>> <CudaNdarrayType(float32, vector)>, GpuFromHost.0)
>> Toposort index: 298
>> Inputs types: [CudaNdarrayType(float32, matrix), CudaNdarrayType(float32,
>> vector), CudaNdarrayType(float32, vector)]
>> Inputs shapes: [(256, 1000), (1000,), (1,)]
>> Inputs strides: [(1000, 1), (1,), (0,)]
>> Inputs values: ['not shown', 'not shown', CudaNdarray([ 275.])]
>> Outputs clients:
>> [[GpuCAReduce{add}{1}(GpuCrossentropySoftmaxArgmax1HotWithBias.0)],
>> [GpuCrossentropySoftmax1HotWithBiasDx(GpuElemwise{Inv}[(0, 0)].0,
>> GpuCrossentropySoftmaxArgmax1HotWithBias.1, GpuFromHost.0)], []]
>> .
>> _____________________________________________________________________.
>>
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