# Data scaling X = (X / 16).astype(np.float32) # Splitting data X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0) # Training classifier = SupervisedDBNClassification( hidden_layers_structure=[1000, 1000, 1000], learning_rate_rbm=0.05, learning_rate=0.1, n_epochs_rbm=15, n_iter_backprop=50, batch_size=32, activation_function='relu', dropout_p=0.2) classifier.fit(X_train, Y_train) # Save the model classifier.save('model.pkl') # Restore it classifier = SupervisedDBNClassification.load('model.pkl') # Test Y_pred = classifier.predict(X_test) print 'Done.\nAccuracy: %f' % accuracy_score(Y_test, Y_pred)
#X_scaled_train = preprocessing.scale(X_train) min_max_scaler = preprocessing.MinMaxScaler() X_scaled_train = min_max_scaler.fit_transform(X_train) y_train = train_set[1:, -1] X_test = test_set[:5000, 1:-1] #X_scaled_test = preprocessing.scale(X_test) min_max_scaler = preprocessing.MinMaxScaler() X_scaled_test = min_max_scaler.fit_transform(X_test) y_test = test_set[:5000, -1] # Training clf = SupervisedDBNClassification( hidden_layers_structure=[1024, 512], learning_rate_rbm=0.05, learning_rate=0.1, n_epochs_rbm=3, n_iter_backprop=10, batch_size=128, activation_function='sigmoid', # relu->error dropout_p=0.2) clf.fit(X_train, y_train) # Save the model clf.save('model.pkl') # Restore it classifier = SupervisedDBNClassification.load('model.pkl') # Test y_pred = classifier.predict(X_test) print('Done.\nAccuracy: %f' % accuracy_score(y_test, y_pred))