Comments (12)
compute_mask (): return None
from textclassifier.
compute_mask (): return None
python 3.7, worked, thanks
from textclassifier.
I have the same problem
from textclassifier.
Me,too
from textclassifier.
compute_mask (): return None
I got that errors when I used the version of python is 3.5,Then I change the version to 2.7, and the error didn't occur.
from textclassifier.
I change the version to 2.7, the error still occurs. How to deal with it?
from textclassifier.
I change the version to 2.7, the error still occurs. How to deal with it?
You need to change the version of your keras to 2.0.8, and don't use the latest version.
from textclassifier.
I met the same issue
from textclassifier.
I met the same issue
Make sure the version of keras is 2.0.8 and the version of python is 2.7
from textclassifier.
compute_mask (): return None
Thanks, this worked for me with Python3.6
from textclassifier.
compute_mask (): return None
Worked, thanks 😃
from textclassifier.
compute_mask (): return None
WORKED!!!!
from textclassifier.
Related Issues (20)
- Consistency with the article (HATT) HOT 5
- Non-exact Implementation of CNN Sentence Classifier HOT 1
- Tokenization performed with validation data (HATT) HOT 1
- y_permute_dim.pop(-2) pop index out of range HOT 5
- mask zero and activation in HATT HOT 6
- some problem about data preprocess HOT 5
- save model error HOT 3
- Not able to train HAN because of the following error. HOT 8
- AttributeError: 'DataFrame' object has no attribute 'review' HOT 3
- the run problem in textClassifierHATT
- Capturing attention weights and seeing which words contributed to the classification HOT 1
- Int to long
- ValueError: Error when checking target: expected dense_1 to have 2 dimensions, but got array with shape (40261, 2, 2) HOT 1
- The performance is worse
- ValueError HOT 1
- Incorporating this model into tensorflow project HOT 3
- Performance on Yelp 2015 (HAN)
- ValueError: Dimensions must be equal, but are 15 and 100 for '{{node Equal}} = Equal[T=DT_BOOL, incompatible_shape_error=true](mask, SequenceMask/Less)' with input shapes: [?,15,100], [?,100,?]. HOT 1
- Getting error on TimeDistributed()
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from textclassifier.