def evaluation(landmarks, print_less=True):
landmarks = np.reshape(landmarks, [1, -1])
landmarks = DataSet(landmarks, landmarks)
tf.set_random_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
data, test = import_dataset(FLAGS.dataset, FLAGS.k_fold)
error_rate = losses.RootMeanSqError(test.Dout)
like = likelihoods.Gaussian()
optimizer = utils.get_optimizer(FLAGS.optimizer, FLAGS.learning_rate)
## Main dgp object
dgp = DgpRff(like, data.num_examples, data.X.shape[1], data.Y.shape[1],
FLAGS.nl, FLAGS.n_rff, FLAGS.df, FLAGS.kernel_type,
FLAGS.kernel_arccosine_degree, FLAGS.is_ard,
FLAGS.feed_forward, FLAGS.q_Omega_fixed, FLAGS.theta_fixed,
FLAGS.learn_Omega)
error_result = dgp.test(landmarks, FLAGS.mc_test, error_rate, print_less)
return error_result
if __name__ == '__main__':
FLAGS = utils.get_flags()
## Set random seed for tensorflow and numpy operations
tf.set_random_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
data, test, _ = import_mnist()
## Here we define a custom loss for dgp to show
error_rate = losses.ZeroOneLoss(data.Dout)
## Likelihood
like = likelihoods.Softmax()
## Optimizer
optimizer = utils.get_optimizer(FLAGS.optimizer, FLAGS.learning_rate)
## Main dgp object
dgp = DgpRff(like, data.num_examples, data.X.shape[1], data.Y.shape[1],
FLAGS.nl, FLAGS.n_rff, FLAGS.df, FLAGS.is_ard,
FLAGS.feed_forward, FLAGS.q_Omega_fixed, FLAGS.theta_fixed,
FLAGS.learn_Omega)
## Learning
dgp.learn(data, FLAGS.learning_rate, FLAGS.mc_train, FLAGS.batch_size,
FLAGS.n_iterations, optimizer, FLAGS.display_step, test,
FLAGS.mc_test, error_rate, FLAGS.duration, FLAGS.less_prints)
tf.set_random_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
data, test = import_dataset(FLAGS.dataset, FLAGS.k_fold)
## Here we define a custom loss for dgp to show
error_rate = losses.RootMeanSqError(data.Dout)
# error_rate = losses.NegLogLikelihood(data.Dout)
## Likelihood
like = likelihoods.Gaussian()
## Optimizer
optimizer = utils.get_optimizer(FLAGS.optimizer, FLAGS.learning_rate)
## Main dgp object
dgp = DgpRff(like, data.num_examples, data.X.shape[1], data.Y.shape[1],
FLAGS.nl, FLAGS.n_rff, FLAGS.df, FLAGS.kernel_type,
FLAGS.kernel_arccosine_degree, FLAGS.is_ard,
FLAGS.feed_forward, FLAGS.q_Omega_fixed, FLAGS.theta_fixed,
FLAGS.learn_Omega)
# Learning
dgp.learn(data, FLAGS.learning_rate, FLAGS.mc_train, FLAGS.batch_size,
FLAGS.n_iterations, optimizer, FLAGS.display_step, test,
FLAGS.mc_test, error_rate, FLAGS.duration, FLAGS.less_prints,
FLAGS.kernel_type, FLAGS.dataset, FLAGS.k_fold)
# Testing
dgp.test(test, FLAGS.mc_test, error_rate, print_less=False)