def setUpClass(cls):
super(TestMultiFull, cls).setUpClass()
likelihood = lik.Softmax()
kernel = cov.SquaredExponential(input_dim=2,
output_dim=2,
length_scale=1.,
std_dev=1.,
white=0.)
inducing_inputs = np.array([[1.0, 2.0, 3.0, 4.0]])
cls.inf = inf.VariationalInference(num_samples=10,
lik_func=likelihood,
cov_func=kernel,
num_components=2)
cls.model = autogp.GaussianProcess(lik_func=likelihood,
cov_func=kernel,
inducing_inputs=inducing_inputs,
num_components=2,
diag_post=False,
inf_func=cls.inf)
cls.session.run(tf.global_variables_initializer())
if __name__ == '__main__':
FLAGS = util.util.get_flags()
BATCH_SIZE = FLAGS.batch_size
LEARNING_RATE = FLAGS.learning_rate
DISPLAY_STEP = FLAGS.display_step
EPOCHS = FLAGS.n_epochs
NUM_SAMPLES = FLAGS.mc_train
NUM_INDUCING = FLAGS.n_inducing
IS_ARD = FLAGS.is_ard
train_X, train_Y, test_X, test_Y = load_cifar()
data = datasets.DataSet(train_X, train_Y)
test = datasets.DataSet(test_X, test_Y)
# Setup initial values for the model.
likelihood = lik.Softmax()
kern = [
cov.SquaredExponential(data.X.shape[1],
length_scale=10.0,
input_scaling=IS_ARD) for i in range(10)
]
# kern = [kernels.ArcCosine(X.shape[1], 2, 3, 5.0, 1.0, input_scaling=True) for i in range(10)] #RadialBasis(X.shape[1], input_scaling=True) for i in range(10)]
Z = init_z(data.X, NUM_INDUCING)
m = autogp.GaussianProcess(likelihood, kern, Z, num_samples=NUM_SAMPLES)
# setting up loss to be reported during training
error_rate = losses.ZeroOneLoss(data.Dout)
o = tf.train.RMSPropOptimizer(LEARNING_RATE)
m.fit(data,
def predict(self, latent_means, latent_vars):
softmax = lik.Softmax()
return tf.Session().run(
softmax.predict(tf.constant(latent_means, dtype=tf.float32),
tf.constant(latent_vars, dtype=tf.float32)))
def log_prob(self, outputs, latent):
softmax = lik.Softmax()
return tf.Session().run(
softmax.log_cond_prob(tf.constant(outputs, dtype=tf.float32),
tf.constant(latent, dtype=tf.float32)))