Comments (4)
Issue could be solved by downgrading to numpy 1.21.0
. Not sure if newer version could also work.
Following warning shown now (also for numpy 1.20.3
):
/home/TS-TCC/dataloader/augmentations.py:42: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
warp = np.concatenate(np.random.permutation(splits)).ravel()
<__array_function__ internals>:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
Data loaded ...
Training started ....
/home/TS-TCC/models/TC.py:52: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
nce += torch.sum(torch.diag(self.lsoftmax(total)))`
from ts-tcc.
It looks like an issue with the data dimensionality or format. What is the source of the data?
from ts-tcc.
I downloaded the HAR-dataset from here and placed it inside data/HAR/(test.pt, train.pt, val.pt)
. When I run python main.py --experiment_description exp1 --run_description run_1 --seed 123 --training_mode self_supervised --selected_dataset HAR
I get the error above.
The training data has after extracting from dictionary a shape of torch.Size([5881, 9, 128]), torch.Size([5881])
for X and y.
from ts-tcc.
I'm not sure, but I guess it could be due to different Numpy versions. Unfortunately, I haven't recorded which version I used. However, you can try downgrading and see if the problem persists.
from ts-tcc.
Related Issues (20)
- Produce macro-averaged F1-score (MF1) results HOT 2
- About training a new dataset HOT 3
- Some Questions
- question regarding the implementation of your temporal contrasting loss HOT 1
- there might be code error for augmentation? HOT 2
- Contextual Contrasting Loss Function HOT 1
- Badly in need of a pretrained model of epilepsy.Could anyone help? HOT 1
- Augmentations and # of training epochs HOT 1
- Obtaining labels on a completly unsupervised dataset HOT 1
- data augment HOT 1
- Can not repeat FD dataset preprocess HOT 3
- the process of self-supervised experiment HOT 5
- Nan question in SupConLoss HOT 4
- Loss cannot decrease HOT 1
- how to handle overfitting problem? HOT 2
- Problem with self_supervised mode training HOT 1
- Request for training logs and detailed settings HOT 5
- Add license HOT 1
- Something wrong with the code when i use self-supervised mode HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from ts-tcc.