Open Bolu98 opened 3 years ago
layer 1 input: 5 layer 1 output: 4 Bias: 1 (We just have to provide true or false for this so 1 will show it's true) layer 2 input: 4 layer 2 output: 3 Bias: 1 (We just have to provide true or false for this so 1 will show it's true) Then select 3 which stands for None and you are good to go. I'm sorry for the late response. Hope this helps you!
Hi,
Thank you very much for getting back to me with this information.
When I try to run this on my Mac, I get a zsh: sementation fault error from the line model = XBNETClassifier(X_train,y_train,2)
.
My package versions are given below:
Python: 3.9.5 torch: 1.9.0 numpy: 1.20.2 pandas: 1.2.5 scikit-learn: 0.24.2 XBNet: 1.3.1 xgboost: 1.4.2
Do you have any ideas for why this is happening?
No, I'm not really sure why this is happening exactly. Try creating a new virtual environment and try installing XBNet in that environment. Check out this link to see how XBNet works on the Kaggle notebook and try to implement it there or google colab. Also please attach the screenshot of the entire error message so that I can get to the crux of this issue and try to solve it.
Hello,
Thank you very much for all your help so far.
When I use Google colab to run the code given in the Kaggle notebook, everything works and the output is very similar to yours.
However, I created a new virtual environment and installed XBNet there and tried to run the code in your example READ ME and I get a segmentation fault error. I have attached the screenshot showing my error message.
Happy to help! Unfortunately, I have no idea about that segmentation fault so I'll label this issue so that someone familiar with such an issue can help you out
Hello,
I am trying to recreate your results for the Iris dataset. I have installed XBNET and all the required libraries.
I am following the example code provided in the READ ME and I am having some trouble with running the code.
Could you please let me know what input and output dimensions you used for layers 1 and 2, with their respective biases, as well as what your last layer was?
Thank you very much.