rbm = BernoulliRBM(Saver(), random_state=0, verbose=True) rbm_features_classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)]) # ############################################################################# # Training # Hyper-parameters. These were set by cross-validation, # using a GridSearchCV. Here we are not performing cross-validation to # save time. rbm.learning_rate = 0.06 rbm.n_iter = 10 # More components tend to give better prediction performance, but larger # fitting time rbm.n_components = 100 logistic.C = 6000 # Training RBM-Logistic Pipeline rbm_features_classifier.fit(X_train, Y_train) # Training the Logistic regression classifier directly on the pixel raw_pixel_classifier = clone(logistic) raw_pixel_classifier.C = 100. raw_pixel_classifier.fit(X_train, Y_train) # ############################################################################# # Evaluation Y_pred = rbm_features_classifier.predict(X_test) print("Logistic regression using RBM features:\n%s\n" %