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ValueError #8
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Thank you for your question
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Thank you for your answer, after modifying the code as you mentioned a list of errors appeared to me similar to the asked in (kk7nc/HDLTex#5) I read your answer, so have you any advice for me to overcome this error. Thanks again. |
Thank you for your email, this is not an error, sometimes your memory and hardware of your computer does not capacity to run a model so it tries to reduce it, can you check with only RNN and CNN? please inform us about your hardware,
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I am having the some problem with 32 Gb of RAM and a 1070 Graphic Memory. Any suggestions? |
can you try by the following option:
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Hello I also have this problem running on Google Colab: I even decreased it to only 1 CNN and batch_size 4 Random_Deep = [0,0,1] |
I also faced the same problem when using Tesla P100 on Azure |
This is the error I get: Error in model 0 try to re-generate an other model So I think it is failing at the optimizer? |
Thank you, |
I also have this problem |
Hi, Thank you for your methods and code. I am also getting this error:
I am simply running your example (on IMBD data) on a server with 128GB memory. So I guess the memory should not be the issue? Haven't changed anything in the code from your example but pointed to my GloVe folder. What could be the reason? |
Can you test only on DNN the only CNN?
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Thanks, |
Hi, also the same problem in Colab and in my local machine in 16 GB RAM and 8GB GTX1070 Random_Deep = [0,0,3] (28, 28, 1) (28, 28, 1) |
Same problem here .. :( <keras.optimizers.Adagrad object at 0x7f36106ccd30> |
ISSUE: FIX: I didn't see a definitive solution to this here, so I am posting my solution which worked for me: Found this on GitHub... This quote is referring to, in this case, values set in files "RMDL_Image" and "RMDL_Text" for "ModelCheckpoint". Change the 3 instances in each file from >> to: monitor="val_acc" >>>> monitor="val_accuracy" |
Answer from @gegilligan works well:
Environment:
Complete setup:
Then copy Try this for a quick test:
This error still appears before generating the
Looking through the code, it is trying different convolutional network configurations until it finds one that works. Try changing the shape of the image from 28x28 to something else, and it will try more configurations before settling on one that fits. If shape is too awkward (i.e. 112x7) then it will fail altogether, and only the DNN and RNN will work. |
Hi, I'm still having the same issue with the following message:
Even with the current fix of setting monitor="val_acc" to monitor="val_accuracy". I'm running the MNIST_example.py on Google Colab notebook, on the 2020 MacBook Air with M1 chip, 8GB memory and 256GB storage. |
First of all thank you so much for your efforts in this paper, it is very interesting. But when I tried to run the code for text classification I got this error ("ValueError: max_features=[20, 500, 50], neither a positive integer nor None"). This error occurs when I run [RMDL.Text_Classification(X_train, y_train, X_test, y_test, batch_size, sparse_categorical, Random_Deep, n_epochs]. So can you please help me to overcome that.
Regards,
Saja
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