def import_mnist():
if os.path.isdir(
DATA_DIR) is False: # directory does not exist, download the data
get_mnist8m_data()
with open(TRAIN_INPUTS) as f:
train_images = extract_images(f)
train_images = process_mnist(train_images)
with open(TRAIN_OUTPUTS) as f:
train_labels = extract_labels(f, one_hot=True)
with open(TEST_INPUTS) as f:
test_images = extract_images(f)
test_images = process_mnist(test_images)
with open(TEST_OUTPUTS) as f:
test_labels = extract_labels(f, one_hot=True)
return datasets.DataSet(train_images, train_labels), datasets.DataSet(
test_images, test_labels)
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
if os.path.exists(
TRAIN_PATH
) is False: # directory does not exist, download the data
get_sarcos_data()
d = sarcos_all_joints_data()
data = datasets.DataSet(d['train_inputs'].astype(np.float32),
d['train_outputs'].astype(np.float32))
test = datasets.DataSet(d['test_inputs'].astype(np.float32),
d['test_outputs'].astype(np.float32))
# Setup initial values for the model.
likelihood = lik.RegressionNetwork(7, 0.1)
kern = [
cov.SquaredExponential(data.X.shape[1],
length_scale=8.0,
input_scaling=IS_ARD) for i in range(8)
]
# kern = [kernels.ArcCosine(data.X.shape[1], 1, 3, 5.0, 1.0, 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)
test_Y = np.array(np.eye(10)[d["labels"]], dtype=np.float32)
return train_X, train_Y, test_X, test_Y
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 = 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
PRED_SAMPLES = FLAGS.mc_test
NLPDS = FLAGS.save_nlpds
MAXTIME = 300
# define GPRN P and Q
output_dim = 10 #P
node_dim = 10 #Q
lag_dim = 3 # for parsing lag features for individual latent functions
# extract dataset
d, d_link = get_inputs()
Ytr, Yte, Xtr, Xte = d['Ytr'], d['Yte'], d['Xtr'], d['Xte']
data = datasets.DataSet(Xtr.astype(np.float32),
Ytr.astype(np.float32),
shuffle=False)
test = datasets.DataSet(Xte.astype(np.float32),
Yte.astype(np.float32),
shuffle=False)
print("dataset created")
# model config block rows (where P=Q): block all w.1, w.2 etc, leave f independent
# order of block_struct is rows, node functions
# lists required: block_struct, link_inputs, kern_link, kern
#block_struct nested list of grouping order
block_struct = [[] for _ in range(output_dim)]
for i in range(output_dim):
row = list(range(i, i + output_dim * (node_dim - 1) + 1, output_dim))
