def demo(): demoData = np.load('regression_data.npz') x_val = demoData['x'] # training data y_val = demoData['y'] # training target x_test_val = demoData['xstar'] # test data from gptheano_model import GP_Theano initial_params = {'mean':np.mean(y_val), 'sigma_n':np.log(.1), 'sigma_f':0., 'l_k':0.} #initial_params = {'mean':1.783386007372333, # 'sigma_n':-1.922677410742645, # 'sigma_f':0.3390422274608337, # 'l_k':-1.0909797052150225} model = GP_Theano(initial_params) outputs = model.get_outputs(x_val, y_val, x_test_val) plot_regression(x_val, y_val, x_test_val, outputs['y_test_mu'],outputs['y_test_var'],'Before Optimization') model.train(x_val, y_val, num_epoch = 100, lr = 1e-1,decay=0.99,opt_method='rmsprop', momentum=0., nesterov=False,batch_size=x_val.shape[0]) #model.train(x_val, y_val, num_epoch = 200, # lr = 1e-2,decay=0.99,opt_method='SGD', batch_size=x_val.shape[0]) outputs = model.get_outputs(x_val, y_val, x_test_val) plot_regression(x_val, y_val, x_test_val, outputs['y_test_mu'],outputs['y_test_var'],'After Optimization') plt.show()
def demo_optimizer(): demoData = np.load('regression_data.npz') x_val = demoData['x'] # training data y_val = demoData['y'] # training target x_test_val = demoData['xstar'] # test data from gptheano_model import GP_Theano initial_params = {'mean':np.mean(y_val), 'sigma_n':np.log(.1), 'sigma_f':0., 'l_k':0.} model = GP_Theano(initial_params) outputs = model.get_outputs(x_val, y_val, x_test_val) plot_regression(x_val, y_val, x_test_val, outputs['y_test_mu'],outputs['y_test_var'],'Before Optimization') model.train_by_optimizer(x_val, y_val, number_epoch=100,batch_size=20) outputs = model.get_outputs(x_val, y_val, x_test_val) plot_regression(x_val, y_val, x_test_val, outputs['y_test_mu'],outputs['y_test_var'],'After Optimization') plt.show()