Jeff, you are the best, simply love your content! going a little deeper, I see that Keras itself comes with a Keras Tuner that offers "Bayesian" as a search model. do you have any experience with it and if yes, how do these two modules compare?
Hi, It's a very nice Content, one small doubt here, Can we pass categorical Hyperparameters to "BayesianOptimization" function? Say(activation functions) I tried passing them but its throwing error, I tried searching online but couldn't find any relevant results.
I can't quantify my gratitude to you for the help you render to us all. Sadly, I have been stuck with hyperparameter optimization for my LSTM regression problem for two months. I have exhausted all possible manual tuning approaches, gridsearched for days but the results are just terrible. I have tried to apply Bayesian optimization to my LSTM regression problem but I experienced this error when I evaluated the model (Found input variables with inconsistent numbers of samples: [10127, 12784]). I guess the LSTM data structure formatting might be wrong. I really need your technical view please.
Can anyone share any content or file about Bayesian optimization of LSTM regression problems (not classification) please? I understand this video addressed neural network model. Thank you.
Jeff, Thanks for your videos. They are awesome. One question, from the following code it appears that layer is always 0? I do not see where this is incremented or how its value ever changes? Am I missing something? Thank you. layer = 0 while neuronCount>25 and layer
Hi, thank you very much for your videos. they are awesome. One question: how would you combine this with cross validtion search for the best number epochs? thanks
Best number of epochs will depend on the hyper-parms. So, I usually search for the hyper-params with an early stop, then once I lock in on the params, I then search for the best number of epochs.
Yes, it works on any type. The trick is representing your CNN hyperparameters as a fixed-length vector. You would do it similar to the method that I did. It would now be filter counts, number of CNN and MaxPool layers, etc.
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Jeff, you are the best, simply love your content! going a little deeper, I see that Keras itself comes with a Keras Tuner that offers "Bayesian" as a search model. do you have any experience with it and if yes, how do these two modules compare?
I’ve seen that option, but have not been doing a lot with Keira’s lately, would love to hear how that goes.
@@HeatonResearch still working on it myself. do be honest, the program runs, but the resulting parameters are not optimal .
Hyperhyperparameter optimization, the next big thing in machine learning
Hi,
It's a very nice Content,
one small doubt here,
Can we pass categorical Hyperparameters to "BayesianOptimization" function? Say(activation functions)
I tried passing them but its throwing error,
I tried searching online but couldn't find any relevant results.
you can but before you need to know what objective function and surrogate function you are using.
This stuff is super cool!
Did not know there is a library :)
Yes, I highly recommend, has save me lots of time/tuning.
What if you want to have a logarithmic search space for learning rate or maybe a categorical for the number of filters? How would you implement that?
How about the Keras Tuner Lib compared with this?
I can't quantify my gratitude to you for the help you render to us all. Sadly, I have been stuck with hyperparameter optimization for my LSTM regression problem for two months. I have exhausted all possible manual tuning approaches, gridsearched for days but the results are just terrible. I have tried to apply Bayesian optimization to my LSTM regression problem but I experienced this error when I evaluated the model (Found input variables with inconsistent numbers of samples: [10127, 12784]). I guess the LSTM data structure formatting might be wrong. I really need your technical view please.
Hello Kola...Have you found a solution? Do you have any resources for how to implement LSTM for regression?
@@JJGhostHunters No Jeremy. Would you like to help?
Can anyone share any content or file about Bayesian optimization of LSTM regression problems (not classification) please? I understand this video addressed neural network model. Thank you.
Amazing. Thank you !!
Jeff, Thanks for your videos. They are awesome. One question, from the following code it appears that layer is always 0? I do not see where this is incremented or how its value ever changes? Am I missing something? Thank you.
layer = 0
while neuronCount>25 and layer
Excellent point, let me take a deeper look at that. I am in the process of reviewing everything for next semester right now.
Hi, thank you very much for your videos. they are awesome. One question: how would you combine this with cross validtion search for the best number epochs? thanks
Best number of epochs will depend on the hyper-parms. So, I usually search for the hyper-params with an early stop, then once I lock in on the params, I then search for the best number of epochs.
@@HeatonResearch Great that's what I was thinking. Thank you very very much again for the awesome videos.
so how to save the tuner? please tell me ,please
Thanks for this content! Just what I looking for on how to actually do it!
Is Bayesian Hyperparameter Optimization similar to Bayesian Neural Network?
One question here...
Can we apply this technique or hyperopt to a CNN model doing image classification?
Yes, it works on any type. The trick is representing your CNN hyperparameters as a fixed-length vector. You would do it similar to the method that I did. It would now be filter counts, number of CNN and MaxPool layers, etc.
Can you do this on google colab please ...
I think keras_optimizer is an easier option
I have nothing against Bayes as long as they don't flaunt their lifestyle around the children.