302 - Tuning deep learning hyperparameters using GridSearchCV
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- Опубликовано: 2 окт 2024
- Tuning deep learning hyperparameters using Gridsearch
Code generated in the video can be downloaded from here:
github.com/bns...
All other code:
github.com/bns...
The grid search provided by GridSearchCV exhaustively generates candidates from a grid of parameter values specified with the param_grid parameter.
The GridSearchCV instance when “fitting” on a dataset, all the possible
combinations of parameter values are evaluated, and the best combination is retained.
cv parameter can be defined for the cross-validation splitting strategy.
GridSearch is designed to work with models from sklearn. But, we can also use it to tune deep learning hyper parameters - at least for keras models.
Wisconsin breast cancer example
Dataset link: www.kaggle.com...
I needed this video ! Currently working on finding defects in leather seat but was having trouble finding the optimal values for the hyperparameters. this will save the day ! Thank you !
The tensorflow.keras.wrappers are depreciated and it is recommended to use scikeras package instead.
i have used scikeras to use keras classifier. but i am getting errors."ValueError: Invalid parameter neurons for estimator KerasClassifier.
This issue can likely be resolved by setting this parameter in the KerasClassifier constructor:
KerasClassifier(neurons=2)
Check the list of available parameters with estimator.get_params().keys()"
I have the same problem too
It doesn’t work for me too, I can’t do it.
Thank you. You are sharing your precious knowledge, its really helpful.
great video. can you make a video on hyperparameter tuning on custom data for yolov8 model?
Guys why is the validation loss not monitored? I can not see it in his video. Is the accuracy calculated by the validation set?
Hope U r doing good sir.
Thanks a lot for ur wonderful lectures.
Sir i m a research scholar pursuing PhD.
Sir I face issues in using GPU of my laptop for image processing.
Sir i have gone through all steps u mentioned like cuda, cudnn, py37env...
Support needed.
Thank you sir.
to search optimum parameter total no of epochs are 18*3*2*number of epochs what is 2 here?
why you didn't split data into train and test?
Sir, can you upload the video on how to perform hyperparameter tuning using cuckoo search algorithm in deep learning architecture
Excellent videos. Sir, I am working on image registration (IR and RGB) and facing some issues. How can I contact you?
Great always, thank you Sir.