[theano-users] Re: VGG16_Places_365 and VGG16_Hybrid_Places_1365 CNN models with theano backend

[ephi5757 via theano-users]

In the code to test the hybrid ImageNet+Places CNN, change the line: model 
= VGG16_Hubrid_1365(weights='places', include_top=False) to model = 
VGG16_Hubrid_1365(weights='places', include_top=True). Then 
preds.shape=(1365L) and the code is debugged.
Note however, that there is a known issue with the pre-trained weights so 
that the top-5 class results for different images are practically identical 
and also the top-5 confidence levels are very low, i.e., 2-3%. Refer 
to the GitHub page  https://github.com/GKalliatakis/Keras-VGG16-places365 

 under 
"issues" for updates.
.

On Tuesday, April 24, 2018 at 4:57:10 PM UTC-4, ephi...@yahoo.com wrote:

> .  I am testing version of a python script (see below) that I found at 
> https://github.com/GKalliatakis/Keras-VGG16-places365 
> 
>  
> to predict the VGG places 365 CNN classes and probabilities for several 
> test images.
>
> > 1) I found that the image variable "x" has dimension 4  and 
> shape (1L, 224L, 224L, 3L).
>
> > 2) I found that the "preds" variable has dimension 1 and 
> shape (365L,) so that  np.sort(preds)[::-1][0:5] looked something like [ 
> 0.03043453  0.02591384  0.02299733  0.01979745  0.01885794].
>
> > *** Are these the predicted probabilities? if so, they are 
> quite small. please advise.
>
> > 3) I also found that "top_preds" variable has dimension 1 
> and shape (5L,) so that np.argsort(preds)[::-1][0:5] looked something like 
> [236  99  21 317  77]. 
>
> > *** These appear to be the top-5 predicted classes, wherein 
> the SCENE CATEGORIES are: museum/indoor; coffee_shop; art_studio; 
> staircase; and campus.
>
> > HOWEVER, my image was image_path='tank_desert' and when I 
> change the test image to 'tank_forest' I get similar classes, i.e., [99  
> 236  21  77  362].
>
> > *** I suspect this test script for the  VGG16 Places 365 
> model is hardwired for the jpg image  'restaurant.jpg' as shown in the 
> example on the github web page for the Places 365 CNN .
>
> > *** Note that when I test the attached 'restaurant.jpg' the 
> top-5 classes are [99, 236, 21, 77, 193] corresponding to the categories 
> coffee_shop, museum/indoor, art_studio, campus, inn/outdoor.
>
> > .
>
> > Is there a way to change a configuration file and/or a part 
> of a .py code to allow for new and different test images?
>
> > Any comments or suggestions that you may offer would be 
> helpful.
>
> > 
>
> > Best Regards,
>
> > Arnold
>
> > 
>
> > PS: I downloaded the categories list locally to my notebook computer 
> because I was having trouble accessing the file online as the code ran.
>
> > .
>
> > PSS: to follow up... I modified the script for  VGG places 365 CNN to 
> test the VGG16_hybrid_places_1365 CNN (see further below) and found that 
> now the variable "x" has dimension 4 and shape 4 (1L, 224L, 224L, 3L).  I 
> also found that "preds" have dimension 3 and shape  (7L, 7L, 512L) and 
> that "top_preds" have shape  (5, 7L, 512L) so that I get a very large 
> array of top-5 class predictions.
>
> > 
>
> > ***Why are the prediction arrays of a different shape in this case? I 
> expected "preds" to have shape (1365L) and top_preds again to have shape 
> (5L).
>
> > Again, Any comments or suggestions that you may offer would be helpful.
>
> > Best,
>
> > Arnold
>
> > 
>
> > #script for  VGG places 365 CNN
>
> > import keras
>
> > import numpy as np
>
> > import os
>
> > from VGG16_places_365 import VGG16_Places365 from keras.preprocessing 
>
> > import image from places_utils import preprocess_input model =
>
> > VGG16_Places365(weights='places')
>
> > 
>
> > img_path = r'C:\Users\atunick\VGG16_hybrid_places_1365\tank_desert.jpg'
>
> > #img_path = 'tank_desert.jpg'
>
> > img = image.load_img(img_path, target_size=(224, 224)) x =
>
> > image.img_to_array(img) x = np.expand_dims(x, axis=0) x =
>
> > preprocess_input(x) print x.ndim, x.shape, 'x'
>
> > predictions_to_return = 5
>
> > preds = model.predict(x)[0]
>
> > print 'preds', preds.ndim, preds.shape, np.sort(preds)[::-1][0:5] 
>
> > top_preds = np.argsort(preds)[::-1][0:predictions_to_return]
>
> > print 'top_preds', top_preds.ndim, top_preds.shape, top_preds # load 
>
> > the class label file_name = 
>
> > r'C:\Users\atunick\VGG16_hybrid_places_1365\categories_places365.txt'
>
> > #if not os.access(file_name, os.W_OK):
>
> > #synset_url = 'Caution-
> https://raw.githubusercontent.com/CSAILVision/places365/master/categories_places365.txt
> '
>
> > # 

[theano-users] VGG16_Places_365 and VGG16_Hybrid_Places_1365 CNN models with theano backend

[ephi5757 via theano-users]

 

.  I am testing version of a python script (see below) that I found at 
https://github.com/GKalliatakis/Keras-VGG16-places365 to predict the VGG 
places 365 CNN classes and probabilities for several test images.

> 1) I found that the image variable "x" has dimension 4  and 
shape (1L, 224L, 224L, 3L).

> 2) I found that the "preds" variable has dimension 1 and 
shape (365L,) so that  np.sort(preds)[::-1][0:5] looked something like [ 
0.03043453  0.02591384  0.02299733  0.01979745  0.01885794].

> *** Are these the predicted probabilities? if so, they are 
quite small. please advise.

> 3) I also found that "top_preds" variable has dimension 1 and 
shape (5L,) so that np.argsort(preds)[::-1][0:5] looked something like [236  
99  21 317  77]. 

> *** These appear to be the top-5 predicted classes, wherein 
the SCENE CATEGORIES are: museum/indoor; coffee_shop; art_studio; 
staircase; and campus.

> HOWEVER, my image was image_path='tank_desert' and when I 
change the test image to 'tank_forest' I get similar classes, i.e., [99  236  
21  77  362].

> *** I suspect this test script for the  VGG16 Places 365 
model is hardwired for the jpg image  'restaurant.jpg' as shown in the 
example on the github web page for the Places 365 CNN .

> *** Note that when I test the attached 'restaurant.jpg' the 
top-5 classes are [99, 236, 21, 77, 193] corresponding to the categories 
coffee_shop, museum/indoor, art_studio, campus, inn/outdoor.

> .

> Is there a way to change a configuration file and/or a part 
of a .py code to allow for new and different test images?

> Any comments or suggestions that you may offer would be 
helpful.

> 

> Best Regards,

> Arnold

> 

> PS: I downloaded the categories list locally to my notebook computer 
because I was having trouble accessing the file online as the code ran.

> .

> PSS: to follow up... I modified the script for  VGG places 365 CNN to 
test the VGG16_hybrid_places_1365 CNN (see further below) and found that 
now the variable "x" has dimension 4 and shape 4 (1L, 224L, 224L, 3L).  I 
also found that "preds" have dimension 3 and shape  (7L, 7L, 512L) and that 
"top_preds" have shape  (5, 7L, 512L) so that I get a very large array of 
top-5 class predictions.

> 

> ***Why are the prediction arrays of a different shape in this case? I 
expected "preds" to have shape (1365L) and top_preds again to have shape 
(5L).

> Again, Any comments or suggestions that you may offer would be helpful.

> Best,

> Arnold

> 

> #script for  VGG places 365 CNN

> import keras

> import numpy as np

> import os

> from VGG16_places_365 import VGG16_Places365 from keras.preprocessing 

> import image from places_utils import preprocess_input model =

> VGG16_Places365(weights='places')

> 

> img_path = r'C:\Users\atunick\VGG16_hybrid_places_1365\tank_desert.jpg'

> #img_path = 'tank_desert.jpg'

> img = image.load_img(img_path, target_size=(224, 224)) x =

> image.img_to_array(img) x = np.expand_dims(x, axis=0) x =

> preprocess_input(x) print x.ndim, x.shape, 'x'

> predictions_to_return = 5

> preds = model.predict(x)[0]

> print 'preds', preds.ndim, preds.shape, np.sort(preds)[::-1][0:5] 

> top_preds = np.argsort(preds)[::-1][0:predictions_to_return]

> print 'top_preds', top_preds.ndim, top_preds.shape, top_preds # load 

> the class label file_name = 

> r'C:\Users\atunick\VGG16_hybrid_places_1365\categories_places365.txt'

> #if not os.access(file_name, os.W_OK):

> #synset_url = 
'Caution-https://raw.githubusercontent.com/CSAILVision/places365/master/categories_places365.txt'

> #os.system('wget ' + synset_url)

> classes = list()

> with open(file_name) as class_file:

>for line in class_file:

>classes.append(line.strip().split(' ')[0][3:]) classes =

> tuple(classes) #print classes print('--SCENE CATEGORIES:') # output 

> the prediction for i in range(0, 5):

>#print top_preds[i]

>print(classes[top_preds[i]])

> print 'completed'

> 

> 

> =

> #script to test the VGG16_hybrid_places_1365 CNN

> import keras

> import numpy as np

> import os

> from VGG16_hybrid_places_1365 import VGG16_Hubrid_1365

> from keras.preprocessing import image

> from places_utils import preprocess_input

> model = VGG16_Hubrid_1365(weights='places', include_top=False)

> 

> img_path = r'C:\Users\atunick\VGG16_hybrid_places_1365\tank_desert.jpg'

> #img_path = 'tank_desert.jpg'

> img = image.load_img(img_path, target_size=(224, 224))

> x = image.img_to_array(img)

> x = np.expand_dims(x, axis=0)

> x = preprocess_input(x)

> print x.ndim, x.shape

> predictions_to_return = 5

> preds = model.predict(x)[0]

> print 'preds', preds.ndim, preds.shape, np.sort(preds)[::-1][0:5]

> top_preds = np.argsort(preds)[::-1][0:predictions

[theano-users] Re: cuDNN not available

[ephi5757 via theano-users]

Problem solved: "Using gpu device 0: Quadro P4000 (CNMeM is enabled with 
initial size: 80.0% of memory, cuDNN 6021)"

see https://github.com/Theano/Theano/issues/3898.


I copied the corresponding files to their respective folders in my CUDA 
folder. For example:

   - cudnn64_6.dll to C:\Program Files\NVIDIA GPU Computing 
   Toolkit\CUDA\v8.0\bin
   - cudnn.h to C:\Program Files\NVIDIA GPU Computing 
   Toolkit\CUDA\v8.0\include
   - cudnn.lib to C:\Program Files\NVIDIA GPU Computing 
   Toolkit\CUDA\v8.0\lib\x64

After copying these files, I add these lines to my .theanorc.txt file:

[dnn]
enabled = True

==
Also see https://github.com/Theano/Theano/issues/5972 .

I tried setting env vars as follows :

LIBRARY_PATHC=:\ProgramData\Miniconda2\pkgs\cudnn-6.0-0\Library\lib\x64
CPATHC=:\ProgramData\Miniconda2\pkgs\cudnn-6.0-0\Library\include

*===.*
*Thanks*

On Tuesday, October 31, 2017 at 11:10:35 AM UTC-4, ephi...@yahoo.com wrote:

> When I try to run my python CNN related programs that depend on theano I 
> get the following statement:
> "Using gpu device 0: Quadro P4000 (CNMeM is enabled with initial size: 
> 80.0% of memory, cuDNN not available)".
> Then the program crashes with the following error:
> ("We can't determine the cudnn version as it is not available".)
> NOTE: I installed cudnn v6.0 and it is located in 
> C:\ProgramData\Miniconda2\pkgs\cudnn-6.0-0.
>
> Previously adding 'cuda.disable_gcc_cudnn_check=True' into the 
> .theanorc.txt worked perfectly.
> Now it does not appear to be effective.
>
> How can I get cudnn to be recognized?
> Are there any solutions to this problem? 
> I am so close to be back up and running my CNN programs on a new computer. 
> Thank you for your help.
> Arnold
>
> FYI, here is my .theanorc.txt file:
> ===.
> [global]
> device = gpu
> REM device = cpu
> floatX=float32
> [cuda]
> root=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\
> cuda.disable_gcc_cudnn_check=True
> [nvcc]
> flags = -LC:\ProgramData\Miniconda2\libs
> compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin
> cxx=C:\ProgramData\Miniconda2\Library\mingw-w64\bin
> optimizer_including=dnn
> [lib]
> cnmem=0.8
> 
>

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[theano-users] cuDNN not available

[ephi5757 via theano-users]

When I try to run my python CNN related programs that depend on theano I 
get the following statement:
"Using gpu device 0: Quadro P4000 (CNMeM is enabled with initial size: 
80.0% of memory, cuDNN not available)".
Then the program crashes with the following error:
("We can't determine the cudnn version as it is not available".)
NOTE: I installed cudnn v6.0 and it is located in 
C:\ProgramData\Miniconda2\pkgs\cudnn-6.0-0.

Previously adding 'cuda.disable_gcc_cudnn_check=True' into the 
.theanorc.txt worked perfectly.
Now it does not appear to be effective.

How can I get cudnn to be recognized?
Are there any solutions to this problem? 
I am so close to be back up and running my CNN programs on a new computer. 
Thank you for your help.
Arnold

FYI, here is my .theanorc.txt file:
===.
[global]
device = gpu
REM device = cpu
floatX=float32
[cuda]
root=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\
cuda.disable_gcc_cudnn_check=True
[nvcc]
flags = -LC:\ProgramData\Miniconda2\libs
compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin
cxx=C:\ProgramData\Miniconda2\Library\mingw-w64\bin
optimizer_including=dnn
[lib]
cnmem=0.8


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[theano-users] Re: Theanorc configuration

[ephi5757 via theano-users]

Got it working. I found that my CNN codes favored python 2.7 so I installed 
miniconda2 and reinstalled theano 0.9.0 and all of the software 
dependencies. I found that putting my .theanorc.txt file into the home 
directory worked, i.e., c:\users\atun...Now the .theanorc.txt file effects 
all of my .py programs that depend on theano.  However, I am not completely 
successful. I get the error statement "Using gpu device 0: Quadro P4000 
(CNMeM is enabled with initial size: 80.0% of memory, cuDNN not available)" 
and then the error ("We can't determine the cudnn version as it is not 
available".
Previously adding into the .theanorc.txt the following worked perfectly:
[cuda]
cuda.disable_gcc_cudnn_check=True
Now it does not appear to be effective.
Are there any solutions to this problem? I think I am very close to be up 
and running my programs on my new computer. Thank you for your help.
Arnold

here is my .theanorc.txt file:
===.
[global]
device = gpu
REM device = cpu
floatX=float32
[cuda]
root=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\
cuda.disable_gcc_cudnn_check=True
[nvcc]
flags = -LC:\ProgramData\Miniconda2\libs
compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin
cxx=C:\ProgramData\Miniconda2\Library\mingw-w64\bin
optimizer_including=dnn
[lib]
cnmem=0.8
=.

On Sunday, October 29, 2017 at 4:41:27 PM UTC-4, Arnold Tunick wrote:

> Hi  Peter,
> Hi Fred,
>  How have you been. Sorry to hear that Theano is coming to an end. As 
> I recently wrote to Pascal, I truly appreciate all of your expert help.
>
> I am in the middle of a project using an implementation of a CNN in 
> Theano so I have to ask the following question:
>
>  I have just installed the latest version of Theano v0.9.0 on a new 
> windows notebook using Miniconda3 along with Python 3.6, MSVS 2015, and 
> Cuda 8.0.6.1.
>  
>  I need to know how to implement the .theanorc.txt configurations in 
> the new version of Theano. I found in the document at 
> *http://deeplearning.net/software/theano/library/config.html* 
>  that;
>
> 1) The location[s] of the .theanorc file[s] in ConfigParser format. It 
> defaults to $HOME/.theanorc. On Windows, it defaults to 
> $HOME/.theanorc:$HOME/.theanorc.txt to make Windows users’ life easier.
>
> and 
>
> 2) to load configuration files {.theanorc} in the current working 
> directory, append .theanorc to the list of configuration files, e.g. 
> THEANORC=~/.theanorc:.theanorc.
>
> Therefore in a python shell I did the following:
> import theano
> The python >> prompt returned with no error messages.
> THEANORC="C:\SciSoft\.theanorc.txt"
> The python >> prompt returned with no error messages.
>
> Is this a viable way to modify the .theanorc configurations?
>
> Best,
> Arnold
>
>
>

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[theano-users] Re: .theanorc.txt in theano v9 and python 3.6

[ephi5757 via theano-users]

from http://deeplearning.net/software/theano/library/config.html it appears 
that "to load configuration files {.theanorc} in the current working 
directory, append .theanorc to the list of configuration files, e.g. 
THEANORC=~/.theanorc:.theanorc.
Therefore in python I did the following:
  import theano
  THEANORC="C:\SciSoft\.theanorc.txt"
The python >> prompt returned with no error messages.
Is this the correct implementation to modify the .theanorc configurations?
Best,
Arnold
==.

On Wednesday, October 25, 2017 at 12:07:17 PM UTC-4, ephi...@yahoo.com 
wrote:

> In what folder does the following .theanorc.txt file go now that I am 
> using theano v9 and python 3.6. I want to be sure that the device = gpu 
> when I test my CNN.
> ==
> [global]
> device = gpu
> REM device = cpu
> floatX=float32
> [nvcc]
> flags = -LC:\SciSoft\Python36\libs
> compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin
> ==
>
>
> FYI, I just installed theano-0.9.0-py36_0 and have successfully run the 
> following script to test theano for CPU:
> ==
> import numpy as np
> import time
> import theano
> A=np.random.rand(1000,1).astype(theano.config.floatX)
> B=np.random.rand(1,1000).astype(theano.config.floatX)
> np_start = time.time()
> AB=A.dot(B)
> np_end=time.time()
> X,Y=theano.tensor.matrices ('XY')
> mf = theano.function([X,Y],X.dot(Y))
> t_start=time.time()
> tAB=mf(A,B)
> t_end=time.time()
> print("NP time:%f[s], theano time: %f[s]" %(np_end-np_start, 
> t_end-t_start))
> =
>
>
>

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[theano-users] .theanorc.txt in theano v9 and python 3.6

[ephi5757 via theano-users]

In what folder does the following .theanorc.txt file go now that I am using 
theano v9 and python 3.6. I want to be sure that the device = gpu when I 
test my CNN.
==
[global]
device = gpu
REM device = cpu
floatX=float32
[nvcc]
flags = -LC:\SciSoft\Python36\libs
compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin
==


FYI, I just installed theano-0.9.0-py36_0 and have successfully run the 
following script to test theano for CPU:
==
import numpy as np
import time
import theano
A=np.random.rand(1000,1).astype(theano.config.floatX)
B=np.random.rand(1,1000).astype(theano.config.floatX)
np_start = time.time()
AB=A.dot(B)
np_end=time.time()
X,Y=theano.tensor.matrices ('XY')
mf = theano.function([X,Y],X.dot(Y))
t_start=time.time()
tAB=mf(A,B)
t_end=time.time()
print("NP time:%f[s], theano time: %f[s]" %(np_end-np_start, t_end-t_start))
=


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Re: [theano-users] ImageNet ILSVRC2012 training and validation data sets

[ephi5757 via theano-users]

 

FYI, see email below.

 

- Forwarded Message -

From: E Park 

To:  

Cc: ImageNet Support ; ilsvrc2...@image-net.org 


Sent: ‎Thursday‎, ‎August‎ ‎24‎, ‎2017‎ ‎03‎:‎48‎:‎08‎ ‎PM

Subject: Re: ImageNet ILSVRC2012 validation dataset

 

Hi, validation dataset is not overlapped with training images. Thanks!

 

Best Regards,

E Park


On Monday, August 28, 2017 at 9:06:02 AM UTC-4, nouiz wrote:
>
> I didn't used this dataset myself recently. But it would be a very big 
> error that the validation set is a subset of the training set. This should 
> never be the case.
>
> Fred
>
> On mer. 23 août 2017 18:47 ephi5757 via theano-users <
> theano...@googlegroups.com > wrote:
>
>> Is the ImageNet ILSVRC2012 validation dataset (50,000 images) a subset of 
>> the training dataset (1.2 million images) or are the validation 
>> images new/independent of the training images?
>> .
>> Best,
>> Arnold
>>
>> -- 
>>
>> --- 
>> You received this message because you are subscribed to the Google Groups 
>> "theano-users" group.
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>> email to theano-users...@googlegroups.com .
>> For more options, visit https://groups.google.com/d/optout.
>>
>

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[theano-users] ImageNet ILSVRC2012 training and validation data sets

[ephi5757 via theano-users]

Is the ImageNet ILSVRC2012 validation dataset (50,000 images) a subset of 
the training dataset (1.2 million images) or are the validation 
images new/independent of the training images?
.
Best,
Arnold

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Re: [theano-users] ValueError: dimension mismatch in x,y_idx arguments

[ephi5757 via theano-users]

Problem solved. In one of my configuration files (spec_1gpu.yaml) I forgot 
to reset my batch_size parameter back to 256. I had assigned batch_size = 1 
when I was doing some analyses on single image files. Fortunately, several 
debugging print statements before and after crash points identified this 
mislabeled parameter. I am still grateful to the theano users group. It is 
a generous resource for the community.
Best,
Arnold

On Tuesday, August 22, 2017 at 9:16:11 PM UTC-4, nouiz wrote:
>
> To get better error message from Theano, disable the GPU and use this 
> flag. optimizer=fast_compile
>
> In this way, Theano will probably give you a stack trace where you created 
> the computation that cause problem.
>
> On lun. 21 août 2017 19:15 ephi5757 via theano-users <
> theano...@googlegroups.com > wrote:
>
>> 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 <
>>> theano...@googlegroups.com> 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:\SciSof

Re: [theano-users] ValueError: dimension mismatch in x,y_idx arguments

[ephi5757 via theano-users]

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 <
> theano...@googlegroups.com > 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, 
>> , 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)], []]
>> .
>> _

Re: [theano-users] ValueError: dimension mismatch in x,y_idx arguments

[ephi5757 via theano-users]

Here are the print results before the code crashes:
==.
[(3007, 3007)]
Using gpu device 0: Quadro K4000M (CNMeM is disabled, CuDNN 3007)
[array(5001.4716796875, dtype=float32)]
.
[(3007, 3007)]
Using gpu device 0: Quadro K4000M (CNMeM is disabled, CuDNN 3007)
[array(5058.68408203125, dtype=float32)]
.
===

On Thursday, August 17, 2017 at 6:26:09 PM UTC-4, ephi...@yahoo.com wrote:
>
> the extracted code above is in \theano\compile\function_module.py
>
> On Thursday, August 17, 2017 at 6:24:13 PM UTC-4, ephi...@yahoo.com wrote:
>>
>> I added a print statement before the line where the code crashes, i.e., 
>>   
>>   t0_fn = time.time()
>> print(self.fn())
>> try:
>> outputs = self.fn()
>>
>> and the error is the same, i.e.,  ValueError: dimension mismatch in 
>> x,y_idx arguments.
>> Please recommend where I can find and print out the x, y_idx arguments to 
>> see what's going on.
>> Thank you,
>> Arnold
>>
>

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Re: [theano-users] ValueError: dimension mismatch in x,y_idx arguments

[ephi5757 via theano-users]

the extracted code above is in \theano\compile\function_module.py

On Thursday, August 17, 2017 at 6:24:13 PM UTC-4, ephi...@yahoo.com wrote:
>
> I added a print statement before the line where the code crashes, i.e., 
>   
>   t0_fn = time.time()
> print(self.fn())
> try:
> outputs = self.fn()
>
> and the error is the same, i.e.,  ValueError: dimension mismatch in 
> x,y_idx arguments.
> Please recommend where I can find and print out the x, y_idx arguments to 
> see what's going on.
> Thank you,
> Arnold
>

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Re: [theano-users] ValueError: dimension mismatch in x,y_idx arguments

[ephi5757 via theano-users]

I added a print statement before the line where the code crashes, i.e., 
  
  t0_fn = time.time()
print(self.fn())
try:
outputs = self.fn()

and the error is the same, i.e.,  ValueError: dimension mismatch in x,y_idx 
arguments.
Please recommend where I can find and print out the x, y_idx arguments to 
see what's going on.
Thank you,
Arnold

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Re: [theano-users] ValueError: dimension mismatch in x,y_idx arguments

[ephi5757 via theano-users]

Hi Frederic,
  Good to hear from you. Thanks for your rapid response. I searched my 
code for the index vector as you suggested and found it in 
\theano\compile\function_module.py and in \theano\gof\link.py. I haven't 
found anything out of line, however. Please advise.
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 <
> theano...@googlegroups.com > 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, 
>> , 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)], []]
>> .
>> _.
>>
>> -- 
>>
>> --- 
>> You received this message because you are subscribed to the Google Groups 
>> "theano-users" group.
>> To unsubscribe from this group and stop receiving emails from it, send an 
>> email to theano-users...@googlegroups.com .
>> For more options, visit https://groups.google.com/d/optout.
>>
>

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[theano-users] ValueError: dimension mismatch in x,y_idx arguments

[ephi5757 via theano-users]

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, 
, 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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[theano-users] Re: Theano can not compile with cuDNN. The process cannot access the file because it is being used by another process.

[ephi5757 via theano-users]

Problem solved. I rebooted by computer. I imagine that the fix recommended 
by nouiz on 4/19/15 is still valid.
Sorry for any inconvenience.
Best,
Arnold
=.

On Thursday, June 22, 2017 at 5:53:06 PM UTC-4, ephi...@yahoo.com wrote:
>
> Previously this exception was fixed as recommended by nouiz on 4/19/15 by 
> adding the Theano flag to .theanorc.txt 
> in C:\SciSoft\WinPython-64bit-2.7.9.4\settings\, i.e., 
> cuda.disable_gcc_cudnn_check=True.
> Now it doesn't work and the program crashes.
> Please advise.
> Thank you.
> Arnold
> ==.
>
> File "c:\scisoft\git\theano\theano\sandbox\cuda\dnn.py", line 1099, in 
> dnn_conv
> return GpuDnnConv(algo=algo)(img, kerns, out, desc)
>   File "c:\scisoft\git\theano\theano\sandbox\cuda\dnn.py", line 331, in 
> __init__
> if version() < (3000, 3000):
>   File "c:\scisoft\git\theano\theano\sandbox\cuda\__init__.py", line 408, 
> in dnn_version
> dnn_available.msg)
> Exception: ("We can't determine the cudnn version as it is not available", 
> "Theano can not compile with cuDNN. We got this error:\n\n[Error 32] The 
> process cannot access the file because it is being used by another process: 
> 'c:usersatunickappdatalocaltemp1try_flags_x3gjtg.exe'")
>

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[theano-users] Theano can not compile with cuDNN. The process cannot access the file because it is being used by another process.

[ephi5757 via theano-users]

Previously this exception was fixed as recommended by nouiz on 4/19/15 by 
adding the Theano flag to .theanorc.txt 
in C:\SciSoft\WinPython-64bit-2.7.9.4\settings\, i.e., 
cuda.disable_gcc_cudnn_check=True.
Now it doesn't work and the program crashes.
Please advise.
Thank you.
Arnold
==.

File "c:\scisoft\git\theano\theano\sandbox\cuda\dnn.py", line 1099, in 
dnn_conv
return GpuDnnConv(algo=algo)(img, kerns, out, desc)
  File "c:\scisoft\git\theano\theano\sandbox\cuda\dnn.py", line 331, in 
__init__
if version() < (3000, 3000):
  File "c:\scisoft\git\theano\theano\sandbox\cuda\__init__.py", line 408, 
in dnn_version
dnn_available.msg)
Exception: ("We can't determine the cudnn version as it is not available", 
"Theano can not compile with cuDNN. We got this error:\n\n[Error 32] The 
process cannot access the file because it is being used by another process: 
'c:usersatunickappdatalocaltemp1try_flags_x3gjtg.exe'")

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[theano-users] Re: Theano can not compile with cuDNN

[ephi5757 via theano-users]

Problem Solved by Nouiz on 4/19/15:

Someone made a PR on Theano related to this, but I thought I got another 
fix in. Can you try this PR:

https://github.com/Theano/Theano/pull/2680/ 


And add the Theano flag to .theanorc.txt

cuda.disable_gcc_cudnn_check=True




On Thursday, March 23, 2017 at 1:48:23 PM UTC-4, ephi...@yahoo.com wrote:
>
> I was required to update my notebook computer at work with multiple 
> patches and updates and now my python program in Theano is not working.
> Please recommend a solution to the following exception:
>
> Exception: ("We can't determine the cudnn version as it is not available", 
> "Theano can not compile with cuDNN. We got this error:\n\n[Error 32] The 
> process cannot access the file because it is being used by another process: 
> 'c:usersatunickappdatalocaltemp1try_flags_4wprdr.exe'")
>
> Thank you.
>

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[theano-users] Re: Theano can not compile with cuDNN

[ephi5757 via theano-users]

Can I delete all the contents of C:\Users\atunick\AppData\Local\Temp 
without disabling any other applications?
Will this solve the Exception problem?
Best,
Arnold



On Thursday, March 23, 2017 at 1:48:23 PM UTC-4, ephi...@yahoo.com wrote:
>
> I was required to update my notebook computer at work with multiple 
> patches and updates and now my python program in Theano is not working.
> Please recommend a solution to the following exception:
>
> Exception: ("We can't determine the cudnn version as it is not available", 
> "Theano can not compile with cuDNN. We got this error:\n\n[Error 32] The 
> process cannot access the file because it is being used by another process: 
> 'c:usersatunickappdatalocaltemp1try_flags_4wprdr.exe'")
>
> Thank you.
>

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[theano-users] Theano can not compile with cuDNN

[ephi5757 via theano-users]

I was required to update my notebook computer at work with multiple patches 
and updates and now my python program in Theano is not working.
Please recommend a solution to the following exception:

Exception: ("We can't determine the cudnn version as it is not available", 
"Theano can not compile with cuDNN. We got this error:\n\n[Error 32] The 
process cannot access the file because it is being used by another process: 
'c:usersatunickappdatalocaltemp1try_flags_4wprdr.exe'")

Thank you.

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[theano-users] probability assigned to the top-5 categories labels by CNN models

[ephi5757 via theano-users]

What is the probability assigned to the top-5 categories labels by CNN 
models?  Many authors appear to suggest that these are the confidence 
levels associated with the 5 most probable labels as determined by the CNN. 
Is there a better or more precise definition? 

 

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[theano-users] Re: Theano-AlexNet CNN top-5

[ephi5757 via theano-users]

I am interested to obtain the top-5 labels and probabilities from the 
Theano-AlexNet CNN for individual test images. See the above figure from 
Krizhevsky et al (2012) as an illustrative example how to display such data.

On Monday, August 1, 2016 at 11:53:57 AM UTC-4, AT wrote:
>
> I have successfully trained and validated Theano-AlexNet, where the CNN 
> achieves 56.6% validation accuracy for the top-1 class labels and 79.7% for 
> the top-5, which is in very close agreement with results published in the 
> arXiv in 2015.
>
> At this stage I am exploring the python code in an effort 
> to extract the top-5 class label information for individual test images. 
> So, far I have been able to run the validate_performance.py program in the 
> AlexNet folder, i.e.,  
> https://github.com/uoguelph-mlrg/theano_alexnet/blob/master/validate_performance.py,
>  
> and print out the following items:
>
> 1) filenames for each of the 195 validation hkl image mini-batch files.
>
> 2) their 256 corresponding validation class labels (i.e., val_labels).
>
> 3) the single top_5 error value (i.e., error_top_5) for each of the 195 
> mini-batches.
>
> I wonder if you could help me find some additional 
> information from the CNN:
>
> 1) the top-5 error rate for each of the 256 images in each of the 195 
> mini-batches.
>
> 2) the  for each of the 256 images in each of the 195 mini-batches.
> Any comments that you may offer would be greatly 
> appreciated.
>
>

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