hyperparameters

问题 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(hp.Int('num_layers', 2, 20)): model.add(layers.Dense(units=hp.Int('units_' + str(i), min_value=32, max_value=512, step=32), activation='relu')) model.add(layers.Dense(1, activation='linear')) model.compile( optimizer=keras.optimizers.Adam( hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])), loss='mean

问题 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(hp.Int('num_layers', 2, 20)): model.add(layers.Dense(units=hp.Int('units_' + str(i), min_value=32, max_value=512, step=32), activation='relu')) model.add(layers.Dense(1, activation='linear')) model.compile( optimizer=keras.optimizers.Adam( hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])), loss='mean

问题 I'd like to perform a hyperparameter search for selecting preprocessing steps and models in sklearn as follows: pipeline = Pipeline([("combiner", PolynomialFeatures()), ("dimred", PCA()), ("classifier", RandomForestClassifier())]) parameters = [{"combiner": [None]}, {"combiner": [PolynomialFeatures()], "combiner__degree": [2], "combiner__interaction_only": [False, True]}, {"dimred": [None]}, {"dimred": [PCA()], "dimred__n_components": [.95, .75]}, {"classifier": [RandomForestClassifier(n

来源： https://stackoverflow.com/questions/62064068/save-optimization-of-hyperparameter-in-a-convolutional-neural-net

来源： https://stackoverflow.com/questions/62064068/save-optimization-of-hyperparameter-in-a-convolutional-neural-net

问题 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 problem is, grid search api only takes single array as input and the code fails while it checks for the data size dimension.(My input dimension is 5*number of data points while according to sklearn api, it should be number of data points*feature dimension). My code looks something like this: from keras

问题 I'm trying to use tune to tune my model in Pytorch (https://docs.ray.io/en/latest/tune.html), as a starting point; I just tried to run their small example: import torch.optim as optim from ray import tune from ray.tune.examples.mnist_pytorch import get_data_loaders, ConvNet, train, test def train_mnist(config): train_loader, test_loader = get_data_loaders() model = ConvNet() optimizer = optim.SGD(model.parameters(), lr=config["lr"]) for i in range(10): train(model, optimizer, train_loader)

问题 It seems to me that the natural way to integrate hyperband with a bayesian optimization search is to have the search algorithm determine each bracket and have the hyperband scheduler run the bracket. That is to say, the bayesian optimization search runs only once per bracket. Looking at Tune's source code for this, it's not clear to me whether the Tune library applies this strategy or not. In particular, I want to know how the Tune library handles passing between the search algorithm and

问题 It seems to me that the natural way to integrate hyperband with a bayesian optimization search is to have the search algorithm determine each bracket and have the hyperband scheduler run the bracket. That is to say, the bayesian optimization search runs only once per bracket. Looking at Tune's source code for this, it's not clear to me whether the Tune library applies this strategy or not. In particular, I want to know how the Tune library handles passing between the search algorithm and

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':