hyperopt

How to save the best hyperopt optimized keras models and its weights?

由 拟墨画扇 提交于 2020-05-13 03:07:02
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':

How to save the best hyperopt optimized keras models and its weights?

由 青春壹個敷衍的年華 提交于 2020-05-13 03:04:07
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':

How to save the best hyperopt optimized keras models and its weights?

由 ╄→尐↘猪︶ㄣ 提交于 2020-05-13 03:03:04
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':

Hyperopt tuning parameters get stuck

由 寵の児 提交于 2020-04-17 21:39:17
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

How can I pass the Hyperopt params to KerasClassifier if I set conditional search space

由 こ雲淡風輕ζ 提交于 2019-12-23 04:38:08
问题 Thanks to the good answer in my last post (How to put KerasClassifier, Hyperopt and Sklearn cross-validation together), it is great help. I have further questions: if I set conditional search space like: second_layer_search_space = \ hp.choice('second_layer', [ { 'include': False, }, { 'include': True, 'layer_size': hp.choice('layer_size', np.arange(5, 26, 5)), } ]) space = { 'second_layer': second_layer_search_space, 'units1': hp.choice('units1', [12, 64]), 'dropout': hp.choice('dropout1',

How can I pass the Hyperopt params to KerasClassifier if I set conditional search space

由 孤街醉人 提交于 2019-12-23 04:38:05
问题 Thanks to the good answer in my last post (How to put KerasClassifier, Hyperopt and Sklearn cross-validation together), it is great help. I have further questions: if I set conditional search space like: second_layer_search_space = \ hp.choice('second_layer', [ { 'include': False, }, { 'include': True, 'layer_size': hp.choice('layer_size', np.arange(5, 26, 5)), } ]) space = { 'second_layer': second_layer_search_space, 'units1': hp.choice('units1', [12, 64]), 'dropout': hp.choice('dropout1',

ap_uniform_sampler() missing 1 required positional argument: 'high' in Ray Tune package for python

由 萝らか妹 提交于 2019-12-12 13:07:11
问题 I am trying to use the Ray Tune package for hyperparameter tuning of a LSTM implemented using pure Tensorflow. I used the hyperband scheduler and HyperOptSearch algorithms for this and I am also using the trainable class method. When I try to run it I get the following error: TypeError: ap_uniform_sampler() missing 1 required positional argument: 'high' shown below is the stack trace: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In

How to put KerasClassifier, Hyperopt and Sklearn cross-validation together

由 泪湿孤枕 提交于 2019-12-11 12:50:30
问题 I am performing a hyperparameter tuning optimization (hyperopt) tasks with sklearn on a Keras models. I am trying to optimize KerasClassifiers using the Sklearn cross-validation, Some code follows: def create_model(): model = Sequential() model.add( Dense(output_dim=params['units1'], input_dim=features_.shape[1], kernel_initializer="glorot_uniform")) model.add(Activation(params['activation'])) model.add(Dropout(params['dropout1'])) model.add(BatchNormalization()) ... model.compile(loss=

Best parameters solved by Hyperopt is unsuitable

由 北城余情 提交于 2019-12-09 11:43:02
问题 I used hyperopt to search best parameters for SVM classifier, but Hyperopt says best 'kernel' is '0'. {'kernel': '0'} is obviously unsuitable. Does anyone know whether it's caused by my fault or a bag of hyperopt ? Code is below. from hyperopt import fmin, tpe, hp, rand import numpy as np from sklearn.metrics import accuracy_score from sklearn import svm from sklearn.cross_validation import StratifiedKFold parameter_space_svc = { 'C':hp.loguniform("C", np.log(1), np.log(100)), 'kernel':hp

Best parameters solved by Hyperopt is unsuitable

由 好久不见. 提交于 2019-12-03 15:58:53
I used hyperopt to search best parameters for SVM classifier, but Hyperopt says best 'kernel' is '0'. {'kernel': '0'} is obviously unsuitable. Does anyone know whether it's caused by my fault or a bag of hyperopt ? Code is below. from hyperopt import fmin, tpe, hp, rand import numpy as np from sklearn.metrics import accuracy_score from sklearn import svm from sklearn.cross_validation import StratifiedKFold parameter_space_svc = { 'C':hp.loguniform("C", np.log(1), np.log(100)), 'kernel':hp.choice('kernel',['rbf','poly']), 'gamma': hp.loguniform("gamma", np.log(0.001), np.log(0.1)), } from
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