hyperparameters

问题 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.metrics import roc_auc_score import sys X = [] y = [] X_val = [] y_val = [] space = {'choice': hp.choice('num_layers', [ {'layers':'two', }, {'layers':'three', 'units3': hp.uniform('units3', 64,1024), 'dropout3': hp.uniform('dropout3', .25,.75)} ]), 'units1': hp.choice('units1', [64,1024]), 'units2':

问题 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.metrics import roc_auc_score import sys X = [] y = [] X_val = [] y_val = [] space = {'choice': hp.choice('num_layers', [ {'layers':'two', }, {'layers':'three', 'units3': hp.uniform('units3', 64,1024), 'dropout3': hp.uniform('dropout3', .25,.75)} ]), 'units1': hp.choice('units1', [64,1024]), 'units2':

问题 What is the best way to perform hyperparameter optimization for a Pytorch model? Implement e.g. Random Search myself? Use Skicit Learn? Or is there anything else I am not aware of? 回答1: Many researchers use RayTune. It's a scalable hyperparameter tuning framework, specifically for deep learning. You can easily use it with any deep learning framework (2 lines of code below), and it provides most state-of-the-art algorithms, including HyperBand, Population-based Training, Bayesian Optimization,

问题 I'm testing to tune parameters of SVM with hyperopt library. Often, when i execute this code, the progress bar stop and the code get stuck. I do not understand why. Here is my code : from hyperopt import fmin, tpe, hp, STATUS_OK, Trials X_train = normalize(X_train) def hyperopt_train_test(params): if 'decision_function_shape' in params: if params['decision_function_shape'] == "ovo": params['break_ties'] = False clf = svm.SVC(**params) y_pred = clf.fit(X_train, y_train).predict(X_test) return

问题 I got a weird result from vw , which uses online learning scheme for logistic regression. And when I add --l1 or --l2 regularization then I got all predictions at 0.5 (that means all features are 0) Here's my command: vw -d training_data.txt --loss_function logistic -f model_l1 --invert_hash model_readable_l1 --l1 0.05 --link logistic ...and here's learning process info: using l1 regularization = 0.05 final_regressor = model_l1 Num weight bits = 18 learning rate = 0.5 initial_t = 0 power_t =

问题 I have built a model that trains using training.py. I want to tune the hyperparameters and run the following script from the notebook in loop by varying the arguments passed. python training.py --cuda --emsize 1500 --nhid 1500 --dropout 0.65 --epochs 10 For eg: If the hyperparameter is dropout, I want to be able to run the script in loop by varying dropout values and plot the graph. 回答1: You can run a shell command using ! in an ipython environment as !ls -l If you want to use it with

问题 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. Is there a standardized way / library in Python that allows the optimization of hyperparameters that is not limited to machine learning topics? 回答1: You can create a custom pipeline/estimator ( see link http://scikit-learn.org/dev/developers/contributing.html#rolling-your-own-estimator) with a score

问题 I have been trying to tune a neural net for some time now but unfortunately, I cannot get a good performance out of it. I have a time-series dataset and I am using RandomizedSearchCV for binary classification. My code is below. Any suggestions or help will be appreciated. One thing is that I am still trying to figure out how to incorporate is early stopping. EDIT: Forgot to add that I am measuring the performance based on F1-macro metric and I cannot get a scoring higher that 0.68. Another

问题 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 model as a first step into the pipeline. Parameters of the model should be optimized. The computed (fitted) Keras model should then be used later on in the pipeline by other steps, so I think I have to store the model as a global variable so that the other pipeline steps can use it. Is this right? I

问题 I am trying to fit a logistic regression model in R using the caret package . I have done the following: model <- train(dec_var ~., data=vars, method="glm", family="binomial", trControl = ctrl, tuneGrid=expand.grid(C=c(0.001, 0.01, 0.1, 1,10,100, 1000))) However, I am unsure what the tuning parameter should be for this model and I am having a difficult time finding it. I assumed it is C because C is the parameter used in sklearn . Currently, I am getting the following error - Error: The