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])
dist_irr = MixtureOfGaussians(means=args.means,
stds=args.stds,
pis=[0.5, 0.5])
def noise(bs):
return np.random.normal(0.0, 1.0, [bs, 2])
print('setting up sampler')
sampler = NiceSampler(gen_arch=args.gen_arch,
log_prob_func=dist.log_prob_func(),
log_prob_func_irr=dist_irr.log_prob_func(),
disc_arch=args.disc_arch,
init_dist=noise,
b=args.b,
m=args.m)
print('setting up and running experiment')
exp = Experiment(log_dir=args.logdir,
sampler=sampler,
params=vars(args),
dist=dist,
debug=args.debug)
exp.run()
def main(args):
print(args)
print('setting up distribution')
dist = Ring(args.scale)
def noise(bs):
return np.random.normal(0.0, 1.0, [bs, 2])
print('setting up sampler')
sampler = NiceSampler(gen_arch=args.gen_arch,
log_prob_func=dist.log_prob_func(),
disc_arch=args.disc_arch,
init_dist=noise,
b=args.b,
m=args.m)
print('setting up and running experiment')
exp = Experiment(log_dir=args.logdir,
sampler=sampler,
params=vars(args),
dist=dist,
debug=args.debug)
exp.run()
def main(args):
if args.learning_rate < 0:
u = np.random.uniform(2.5, 6)
args.learning_rate = np.float32(np.power(10, -u))
if args.scale < 0:
args.scale = np.float32(np.random.uniform(0.1, 5.0))
if args.lambda_b < 0:
args.lambda_b = np.float32(np.random.uniform(0.0, 0.5))
if args.leap_size < 0:
u = np.random.uniform(1, 2)
args.leap_size = np.float32(np.power(10, -u))
if args.leap_steps < 0.0:
args.leap_steps = np.random.choice([10, 25, 50])
if args.init_temp < 0.0:
args.init_temp = np.random.choice([100, 50])
print(args)
print('setting up distribution')
mu_1 = np.array([-10., 0.])
mu_2 = np.array([10., 0.])
mus = np.array([mu_1, mu_2])
cov_1 = 1 * np.eye(2)
cov_2 = 1 * np.eye(2)
covs = np.array([cov_1, cov_2])
pis = np.array([0.5, 0.5])
dist = GMM(mus, covs, pis)
def init_dist(bs):
return np.random.randn(bs, args.data_dim)
sampler = L2HMC(energy_function=dist.get_energy_function(),
arch=args.arch,
tar_dim=args.data_dim,
scale=args.scale,
init_dist=init_dist,
leap_steps=args.leap_steps,
leap_size=args.leap_size,
lambda_b=args.lambda_b,
use_temp=True)
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')
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
arch = [data_dim] + args.arch
target_dim = np.sum([arch[i] * arch[i + 1] for i in range(len(arch) - 1)])
gen_arch = 2 * [target_dim] + args.gen_arch
dist = FeedForwardNetPost(X_train,
y_train,
X_test,
y_test,
arch,
prec=args.prior)
def noise(bs):
return np.random.normal(0.0, 1.0, [bs, target_dim])
print('setting up sampler')
sampler = NiceSampler(gen_arch=gen_arch,
log_prob_func=dist.log_prob_func(),
disc_arch=args.disc_arch,
init_dist=noise,
b=args.b,
m=args.m)
print('setting up and running experiment')
exp = Experiment(log_dir=args.logdir,
sampler=sampler,
params=vars(args),
dist=dist,
debug=args.debug)
exp.run()
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
arch = [data_dim] + args.dist_arch
target_dim = np.sum([arch[i] * arch[i + 1] for i in range(len(arch) - 1)])
dist = FeedForwardNetPost(X_train,
y_train,
X_test,
y_test,
arch,
prec=args.prior)
print('setting up sampler')
def init_dist(bs):
return np.random.randn(bs, target_dim)
sampler = L2HMC(log_prob_func=dist.log_prob_func(),
arch=args.arch,
sample_dim=target_dim,
scale=args.scale,
init_dist=init_dist,
leap_steps=args.leap_steps,
eps=args.eps)
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()
def main(args):
print('setting up distribution')
dist = MixtureOfGaussians(means=args.means, stds=args.stds, pis=[0.5, 0.5])
def init_dist(bs):
return np.random.randn(bs, 2).astype(np.float32)
print('setting up sampler')
sampler = HMC(dist.log_prob_func(), init_dist, args.step_size,
args.num_leap_steps, 2)
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')
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 = Australian(X_train, y_train, X_test, y_test, args.prior)
dist_irr = Australian(X_train, y_train, X_test, y_test, args.prior)
def noise(bs):
return np.random.normal(0.0, 1.0, [bs, data_dim])
gen_arch = [data_dim, data_dim] + args.gen_arch
print('setting up sampler')
sampler = NiceSampler(gen_arch=gen_arch,
log_prob_func=dist.log_prob_func(),
log_prob_func_irr=dist_irr.log_prob_func(),
disc_arch=args.disc_arch,
init_dist=noise,
b=args.b,
m=args.m)
print('setting up and running experiment')
exp = Experiment(log_dir=args.logdir,
sampler=sampler,
params=vars(args),
dist=dist,
debug=args.debug)
exp.run()
def main(args):
if args.learning_rate < 0:
u = np.random.uniform(2.5, 6)
args.learning_rate = np.power(10, -np.float32(u))
if args.scale < 0:
args.scale = np.float32(np.random.uniform(0.1, 5.0))
if args.lambda_b < 0:
args.lambda_b = np.float32(np.random.uniform(0.0, 0.5))
if args.leap_size < 0:
u = np.float32(np.random.uniform(1, 2))
args.leap_size = np.float32(np.power(10, -u))
if args.leap_steps < 0.0:
args.leap_steps = np.random.choice([10, 25, 50])
print(args)
print('setting up distribution')
dist = Ring(args.ring_scale)
energy_func = lambda x: -dist.log_prob_func()(x)
print('setting up sampler')
def init_dist(bs):
return np.random.randn(bs, args.data_dim)
sampler = L2HMC(energy_function=energy_func,
arch=args.arch,
tar_dim=args.data_dim,
scale=args.scale,
init_dist=init_dist,
leap_steps=args.leap_steps,
leap_size=args.leap_size)
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')
def init_dist(bs):
o = np.ones((bs, 1))
a = np.array([[0.75, 0.75]])
return o * a
sampler = RwmSampler(dist.log_prob_func(), args.data_dim, init_dist,
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(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)
def init_dist(bs):
return np.random.randn(bs, data_dim).astype(np.float32)
print('setting up sampler')
sampler = HMC(dist.log_prob_func(),
init_dist,
args.step_size,
args.num_leap_steps,
data_dim
)
print('setting up and running experiment')
exp = Experiment(log_dir=args.logdir,
sampler=sampler,
params=vars(args),
dist=dist)
exp.run()
def main(args):
# If negative parameters provided, select them at random from a suitable range
if args.learning_rate < 0:
u = np.random.uniform(2.5, 6)
args.learning_rate = np.float32(np.power(10.0, -u))
if args.scale < 0:
args.scale = np.float32(np.random.uniform(0.1, 5.0))
if args.lambda_b < 0:
args.lambda_b = np.float32(np.random.uniform(0.0, 0.5))
if args.eps < 0:
u = np.random.uniform(1, 2)
args.eps = np.float32(np.power(10.0, -u))
if args.leap_steps < 0.0:
args.leap_steps = np.random.choice([10, 25, 50])
print(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
with tf.name_scope('distribution'):
dist = BayesLogRegPost(X_train, y_train, X_test, y_test, args.prior)
energy = lambda x: -dist.log_prob_func()(x)
print('setting up sampler')
def init_dist(bs):
return np.random.randn(bs, data_dim)
with tf.name_scope('sampler'):
sampler = L2HMC(energy_function=energy,
arch=args.arch,
tar_dim=data_dim,
scale=args.scale,
init_dist=init_dist,
leap_steps=args.leap_steps,
leap_size=args.eps,
lambda_b=args.lambda_b)
print('setting up and running experiment')
exp = Experiment(log_dir=args.logdir,
sampler=sampler,
params=vars(args),
dist=dist,
debug=args.debug)
exp.run()