def setUpClass(cls):
super(TestMultiFull, cls).setUpClass()
likelihood = likelihoods.Softmax()
kernel = [
kernels.RadialBasis(input_dim=2,
lengthscale=1.0,
std_dev=1.0,
white=0.0) for i in range(2)
]
inducing_locations = np.array([[1.0, 2.0, 3.0, 4.0]])
cls.model = autogp.GaussianProcess(likelihood_func=likelihood,
kernel_funcs=kernel,
inducing_inputs=inducing_locations,
num_components=2,
diag_post=False,
num_samples=1)
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
data, test, _ = datasets.import_mnist()
# Setup initial values for the model.
likelihood = likelihoods.Softmax()
kern = [
kernels.RadialBasis(data.X.shape[1],
lengthscale=10.0,
input_scaling=IS_ARD) for i in xrange(10)
]
# kern = [kernels.ArcCosine(X.shape[1], 2, 3, 5.0, 1.0, input_scaling=True) for i in xrange(10)] #RadialBasis(X.shape[1], input_scaling=True) for i in xrange(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)
import time
otime = time.time()
def predict(self, latent_means, latent_vars):
softmax = likelihoods.Softmax()
return tf.Session().run(
softmax.predict(np.array(latent_means, dtype=np.float32),
np.array(latent_vars, dtype=np.float32)))
def log_prob(self, outputs, latent):
softmax = likelihoods.Softmax()
return tf.Session().run(
softmax.log_cond_prob(np.array(outputs, dtype=np.float32),
np.array(latent, dtype=np.float32)))
