hyperparameters

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 = 0.5 using no cache Reading datafile = training_data.txt num sources = 1 average since example example

The basic equation of square exponential or RBF kernel is as follows: Here l is the length scale and sigma is the variance parameter. The length scale controls how two points appear to be similar as it simply magnifies the distance between x and x'. The variance parameter controls how smooth the function is. I want to optimize/train these parameters (l and sigma) with my training data sets. My training data sets are in the following form: X : 2-D Cartesian coordinate as input data y : radio signal strength (RSS) of Wi-Fi device at the 2-D coordinates points as observed output According to

I am trying to optimize the hyperparameters of my NN using Keras and sklearn. I am wrapping up with KerasClassifier (it´s a classification problem). I am trying to optimize the number of hidden layers. I can´t figure it out how to do it with keras (actually I am wondering how to set up the function create_model in order to maximize the number of hidden layers) Could anyone please help me? My code (just the important part): ## Import `Sequential` from `keras.models` from keras.models import Sequential # Import `Dense` from `keras.layers` from keras.layers import Dense def create_model(optimizer

I have found a set of best hyperparameters for my KNN estimator with Grid Search CV: >>> knn_gridsearch_model.best_params_ {'algorithm': 'auto', 'metric': 'manhattan', 'n_neighbors': 3} So far, so good. I want to train my final estimator with these new-found parameters. Is there a way to feed the above hyperparameter dict to it directly? I tried this: >>> new_knn_model = KNeighborsClassifier(knn_gridsearch_model.best_params_) but instead the hoped result new_knn_model just got the whole dict as the first parameter of the model and left the remaining ones as default: >>> knn_model

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 flow_from_directory to fit function(last line in the code) 2- how can add early stop? thanks from __future__ import print_function import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.wrappers.scikit_learn import KerasClassifier from keras import backend as K