def main(args):
print('setting up distribution')
def load_data(folder, test_frac=0.2):
X = np.load(folder + '/data.npy')
y = np.load(folder + '/labels.npy')
N, D = X.shape
data = np.concatenate([X, y], axis=1)
np.random.shuffle(data)
train_data = data[:int(N * (1 - test_frac))]
test_data = data[int(N * (1 - test_frac)):]
return train_data[:, :-1], train_data[:,
-1], test_data[:, :
-1], test_data[:,
-1]
X_train, y_train, X_test, y_test = load_data(args.data)
data_dim = X_train.shape[1] + 1
dist = BayesLogRegPost(X_train, y_train, X_test, y_test, args.prior)
print('setting up sampler')
with tf.variable_scope('sampler', reuse=tf.AUTO_REUSE):
sampler = VS(dist.log_prob_func(), data_dim, args.aux_dim,
args.hidden_units, args.num_layers, args.train_samples,
args.num_chains, args.activation, args.num_mix,
args.perturb)
print('setting up and running experiment')
exp = Experiment(log_dir=args.logdir,
sampler=sampler,
params=vars(args),
dist=dist)
exp.run()
def main(args):
print('setting up distribution')
dist = MixtureOfGaussians(means=args.means, stds=args.stds, pis=[0.5, 0.5])
print('setting up sampler')
with tf.variable_scope('sampler', reuse=tf.AUTO_REUSE):
sampler = VS(dist.log_prob_func(), args.data_dim, args.aux_dim,
args.hidden_units, args.num_layers, args.train_samples,
args.num_chains, args.activation, args.num_mix,
args.perturb)
print('setting up and running experiment')
exp = Experiment(log_dir=args.logdir,
sampler=sampler,
params=vars(args),
dist=dist)
exp.run()