Optuna TPESampler and RandomSampler try the same suggested integer values (possible floats and loguniforms as well) for any parameter more than once for some reason. I couldn't find a way to stop i...

I'm working with optuna for hyperparameter tuning of ML models in Python. While defining the objective function for tuning a Deep Learning model I tried to define a list of choices from which the t...

I'm using a scikit-learn custom pipeline (sklearn.pipeline.Pipeline) in conjunction with RandomizedSearchCV for hyper-parameter optimization. This works great. Now I would like to insert a keras mo...

In my setting I have an abstract situation like the following, which shall only function as an example case: base = trial.suggest_int(1, 3) power = trial.suggest_int(1, 10) # value = base ** power ...

I am using Keras Tuner to tune the hyperparameters of my neural network. I want to search the optimal number of hidden layers and the optimal number of units in each layer. To avoid overparametrizi...

I am trying to tune an extra tree classifier with Optuna. I am getting this message to all my trials: [W 2022-02-10 12:13:12,501] Trial 2 failed, because the value None could not be cast to float....

I'm working on a multi classification problem with a neural network in scikit-learn and I'm trying to figure out how I can optimize my hyperparameters (amount of layers, perceptrons, other things e...

The only hyperparameter optimization library I've found that works with Keras functional API is Talos. https://github.com/autonomio/talos/blob/9890d71d31451af3d7e8d91a75841bc7904db958/docs/Example...

I want to set optuna's study.optimize verbosity to 0. I thought optuna.logging.set_verbosity(0) might do it, but I still get the Trial 0 finished with value .... updates for every trial What is the...

I am using Hyperparameter using HParams Dashboard in Tensorflow 2.0-beta0 as suggested here https://www.tensorflow.org/tensorboard/r2/hyperparameter_tuning_with_hparams I am confused in step 1, I ...

How do I optimize for multiple metrics simultaneously inside the objective function of Optuna. For example, I am training an LGBM classifier and want to find the best hyperparameter set for all com...

I have scikit-learn 0.24.1 and scikit-optimize 0.8.1, when I tried to use the BayesSearchCV function it gave me this error: TypeError: __init__() got an unexpected keyword argument 'iid' when I se...

I optimized my keras model using hyperopt. Now how do we save the best optimized keras model and its weights to disk. My code: from hyperopt import fmin, tpe, hp, STATUS_OK, Trials from sklearn....

this is the code that I am running. It is a self hyperparameter tuned ANN. The path is to long and I don't know how to shorten it. def build_model(hp): model = keras.Sequential() for i in range(h...

I want to optimize an algorithm that has several variable parameters as input. For machine learning tasks, Sklearn offers the optimization of hyperparameters with the gridsearch functionality....

I am able to successfully improve the performance of my XGBoost model through Bayesian optimization, but the best I can achieve through Bayesian optimization when using Light GBM (my preferred choi...

I am trying to tune a basic neural network as practice. (Based on an example from a coursera course: Neural Networks and Deep Learning - DeepLearning.AI) I face the issue of the random weight...

i want to grid search the parameter of the model with fit_generator as input in keras i find below code in stack overflow and change it 1- but i don't understand how give the fit_generator or fl...

I would like to do hyperparameter training using the kerastuner framework. How can I choose an optimizer and different learning rates which can be passed to the optimizers. This is my model.compile...

Love the speed of the ranger package for random forest model creation, but can't see how to tune mtry or number of trees. I realize I can do this via caret's train() syntax, but I prefer the speed ...

I have a quite simple ANN using Tensorflow and AdamOptimizer for a regression problem and I am now at the point to tune all the hyperparameters. For now, I saw many different hyperparameters that...

I am trying to do a grid search over my hyperparameters for tuning a deep learning architecture. I have multiple input options to the model and I am trying to use sklearn's grid search api. The pro...

I am trying to tune some params and the search space is very large. I have 5 dimensions so far and it will probably increase to about 10. The issue is that I think I can get a significant speedup i...

The problem I am currently working on a project that I sadly can't share with you. The project is about hyper-parameter optimization for neural networks, and it requires that I train multiple neura...

I tried to apply Bayesian Optimisation to a simple CNN for the MNIST handwritten digits dataset and I'm getting little indication that it works. I've tried doing k-fold validation to smooth out noi...