recognition_utils.py 文件源码

def fit(self, X, y, test_size=0.3):
        # Grid search cross-val (best C param)
        cv = ShuffleSplit(len(X), n_iter=1, test_size=0.3, random_state=self.seed_)
        clf_cv = GridSearchCV(self.clf_base_, self.clf_hyparams_, cv=cv, n_jobs=-1, verbose=4)

        print('====> Training Classifier (with grid search hyperparam tuning) .. ')
        print('====> BATCH Training (in-memory): {:4.3f} MB'.format(X.nbytes / 1024.0 / 1024.0) )
        clf_cv.fit(X, y)
        print('BEST: {}, {}'.format(clf_cv.best_score_, clf_cv.best_params_))

        # Setting clf to best estimator
        self.clf_ = clf_cv.best_estimator_

        # # Calibrating classifier
        # print('Calibrating Classifier ... ')
        # self.clf_prob_ = CalibratedClassifierCV(self.clf_, cv=cv, method='sigmoid')
        # self.clf_prob_.fit(X, y)        

        # # Setting clf to best estimator
        # self.clf_ = clf_cv.best_estimator_
        # pred_targets = self.clf_.predict(X)

        if self.epoch_no_ % 10 == 0: 
            self.save(self.filename_.replace('.h5', '_iter_{}.h5'.format(self.epoch_no_)))
        self.save(self.filename_)
        self.epoch_no_ += 1