def load_data(hps):
from examples.utils import dataset
from examples import conf
data_path = os.path.join(conf.data_dir, hps.dataset + '.data')
data_func = getattr(dataset, 'load_uci_' + hps.dataset)
x_train, y_train, x_valid, y_valid, x_test, y_test = data_func(data_path)
x_train = np.vstack([x_train, x_valid])
y_train = np.hstack([y_train, y_valid])
n_train, hps.n_x = x_train.shape
x_train, x_test, _, _ = dataset.standardize(x_train, x_test)
y_train, y_test, mean_y_train, std_y_train = dataset.standardize(
y_train, y_test)
return x_train, y_train, x_test, y_test, n_train, mean_y_train, std_y_train
if __name__ == '__main__':
tf.set_random_seed(1237)
np.random.seed(1234)
# Load UCI Boston housing data
data_path = os.path.join(conf.data_dir, 'housing.data')
x_train, y_train, x_valid, y_valid, x_test, y_test = \
dataset.load_uci_boston_housing(data_path)
x_train = np.vstack([x_train, x_valid])
y_train = np.hstack([y_train, y_valid])
N, n_x = x_train.shape
# Standardize data
x_train, x_test, _, _ = dataset.standardize(x_train, x_test)
y_train, y_test, mean_y_train, std_y_train = dataset.standardize(
y_train, y_test)
# Define model parameters
n_hiddens = [50]
# Define training/evaluation parameters
lb_samples = 10
ll_samples = 5000
epochs = 500
batch_size = 10
iters = int(np.floor(x_train.shape[0] / float(batch_size)))
test_freq = 10
learning_rate = 0.01
anneal_lr_freq = 100
def main():
np.random.seed(1234)
tf.set_random_seed(1237)
# Load UCI Boston housing data
data_path = os.path.join(conf.data_dir, 'housing.data')
x_train, y_train, x_valid, y_valid, x_test, y_test = \
dataset.load_uci_boston_housing(data_path)
N, n_x = x_train.shape
# Standardize data
x_train, x_test, _, _ = dataset.standardize(x_train, x_test)
y_train, y_test, mean_y_train, std_y_train = dataset.standardize(
y_train, y_test)
# Define model parameters
n_hiddens = [50]
@zs.reuse('model')
def bayesianNN(observed, x, n_x, layer_sizes, n_particles):
with zs.BayesianNet(observed=observed) as model:
ws = []
for i, (n_in,
n_out) in enumerate(zip(layer_sizes[:-1],
layer_sizes[1:])):
w_mu = tf.zeros([1, n_out, n_in + 1])
ws.append(
zs.Normal('w' + str(i),
w_mu,
std=1.,
n_samples=n_particles,
group_event_ndims=2))
# forward
ly_x = tf.expand_dims(
tf.tile(tf.expand_dims(x, 0), [n_particles, 1, 1]), 3)
for i in range(len(ws)):
w = tf.tile(ws[i], [1, tf.shape(x)[0], 1, 1])
ly_x = tf.concat(
[ly_x, tf.ones([n_particles,
tf.shape(x)[0], 1, 1])], 2)
ly_x = tf.matmul(w, ly_x) / \
tf.sqrt(tf.to_float(tf.shape(ly_x)[2]))
if i < len(ws) - 1:
ly_x = tf.nn.relu(ly_x)
y_mean = tf.squeeze(ly_x, [2, 3])
y_logstd = tf.get_variable('y_logstd',
shape=[],
initializer=tf.constant_initializer(0.))
y = zs.Normal('y', y_mean, logstd=y_logstd)
return model, y_mean
def mean_field_variational(layer_sizes, n_particles):
with zs.BayesianNet() as variational:
ws = []
for i, (n_in,
n_out) in enumerate(zip(layer_sizes[:-1],
layer_sizes[1:])):
w_mean = tf.get_variable(
'w_mean_' + str(i),
shape=[1, n_out, n_in + 1],
initializer=tf.constant_initializer(0.))
w_logstd = tf.get_variable(
'w_logstd_' + str(i),
shape=[1, n_out, n_in + 1],
initializer=tf.constant_initializer(0.))
ws.append(
zs.Normal('w' + str(i),
w_mean,
logstd=w_logstd,
n_samples=n_particles,
group_event_ndims=2))
return variational
# Build the computation graph
n_particles = tf.placeholder(tf.int32, shape=[], name='n_particles')
x = tf.placeholder(tf.float32, shape=[None, n_x])
y = tf.placeholder(tf.float32, shape=[None])
layer_sizes = [n_x] + n_hiddens + [1]
w_names = ['w' + str(i) for i in range(len(layer_sizes) - 1)]
def log_joint(observed):
model, _ = bayesianNN(observed, x, n_x, layer_sizes, n_particles)
log_pws = model.local_log_prob(w_names)
log_py_xw = model.local_log_prob('y')
return tf.add_n(log_pws) + log_py_xw * N
variational = mean_field_variational(layer_sizes, n_particles)
qw_outputs = variational.query(w_names, outputs=True, local_log_prob=True)
latent = dict(zip(w_names, qw_outputs))
y_obs = tf.tile(tf.expand_dims(y, 0), [n_particles, 1])
lower_bound = tf.reduce_mean(
zs.sgvb(log_joint, {'y': y_obs}, latent, axis=0))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
grads = optimizer.compute_gradients(-lower_bound)
infer = optimizer.apply_gradients(grads)
# prediction: rmse & log likelihood
observed = dict((w_name, latent[w_name][0]) for w_name in w_names)
observed.update({'y': y_obs})
model, y_mean = bayesianNN(observed, x, n_x, layer_sizes, n_particles)
y_pred = tf.reduce_mean(y_mean, 0)
rmse = tf.sqrt(tf.reduce_mean((y_pred - y)**2)) * std_y_train
log_py_xw = model.local_log_prob('y')
log_likelihood = tf.reduce_mean(zs.log_mean_exp(log_py_xw, 0)) - \
tf.log(std_y_train)
# Define training/evaluation parameters
lb_samples = 10
ll_samples = 5000
epochs = 500
batch_size = 10
iters = int(np.floor(x_train.shape[0] / float(batch_size)))
test_freq = 10
# Run the inference
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(1, epochs + 1):
lbs = []
for t in range(iters):
x_batch = x_train[t * batch_size:(t + 1) * batch_size]
y_batch = y_train[t * batch_size:(t + 1) * batch_size]
_, lb = sess.run([infer, lower_bound],
feed_dict={
n_particles: lb_samples,
x: x_batch,
y: y_batch
})
lbs.append(lb)
print('Epoch {}: Lower bound = {}'.format(epoch, np.mean(lbs)))
if epoch % test_freq == 0:
test_lb, test_rmse, test_ll = sess.run(
[lower_bound, rmse, log_likelihood],
feed_dict={
n_particles: ll_samples,
x: x_test,
y: y_test
})
print('>> TEST')
print('>> lower bound = {}, rmse = {}, log_likelihood = {}'.
format(test_lb, test_rmse, test_ll))
n_samples=n_particles,
group_ndims=1)
return variational
if __name__ == '__main__':
tf.set_random_seed(1234)
np.random.seed(1234)
# Load MNIST
data_path = os.path.join(conf.data_dir, 'mnist.pkl.gz')
x_train, y_train, x_valid, y_valid, x_test, y_test = \
dataset.load_mnist_realval(data_path, one_hot=False)
x_train = np.vstack([x_train, x_valid]).astype('float32')
y_train = np.concatenate([y_train, y_valid]).astype('int32')
x_train, x_test, _, _ = dataset.standardize(x_train, x_test)
n_x = x_train.shape[1]
# Define training/evaluation parameters
epochs = 500
batch_size = 1000
lb_samples = 10
ll_samples = 100
iters = int(np.floor(x_train.shape[0] / float(batch_size)))
test_freq = 3
learning_rate = 0.001
anneal_lr_freq = 100
anneal_lr_rate = 0.75
# placeholders
n_particles = tf.placeholder(tf.int32, shape=[], name='n_particles')
def main():
tf.set_random_seed(1237)
np.random.seed(2345)
# Load UCI protein data
data_path = os.path.join(conf.data_dir, "protein.data")
x_train, y_train, x_valid, y_valid, x_test, y_test = \
dataset.load_uci_protein_data(data_path)
x_train = np.vstack([x_train, x_valid])
y_train = np.hstack([y_train, y_valid])
n_train, x_dim = x_train.shape
# Standardize data
x_train, x_test, _, _ = dataset.standardize(x_train, x_test)
y_train, y_test, mean_y_train, std_y_train = dataset.standardize(
y_train, y_test)
# Define model parameters
n_hiddens = [50]
# Build the computation graph
n_particles = 20
x = tf.placeholder(tf.float32, shape=[None, x_dim])
y = tf.placeholder(tf.float32, shape=[None])
layer_sizes = [x_dim] + n_hiddens + [1]
w_names = ["w" + str(i) for i in range(len(layer_sizes) - 1)]
wv = []
logstds = []
for i, (n_in, n_out) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):
wv.append(
tf.Variable(
tf.random_uniform([n_particles, n_out, n_in + 1]) * 4 - 2))
logstds.append(tf.Variable(tf.zeros([n_out, n_in + 1])))
model = build_bnn(x, layer_sizes, logstds, n_particles)
def log_joint(bn):
log_pws = bn.cond_log_prob(w_names)
log_py_xw = bn.cond_log_prob('y')
return tf.add_n(log_pws) + tf.reduce_mean(log_py_xw, 1) * n_train
model.log_joint = log_joint
# sgmcmc = zs.SGLD(learning_rate=4e-6)
sgmcmc = zs.SGHMC(learning_rate=2e-6,
friction=0.2,
n_iter_resample_v=1000,
second_order=True)
# sgmcmc = zs.SGNHT(learning_rate=1e-5, variance_extra=0., tune_rate=50.,
# second_order=True)
latent = dict(zip(w_names, wv))
observed = {'y': y}
# E step: Sample the parameters
sample_op, sgmcmc_info = sgmcmc.sample(model,
observed=observed,
latent=latent)
mean_k = sgmcmc_info.mean_k
# M step: Update the logstd hyperparameters
esti_logstds = [0.5 * tf.log(tf.reduce_mean(w * w, axis=0)) for w in wv]
output_logstds = dict(
zip(w_names, [0.5 * tf.log(tf.reduce_mean(w * w)) for w in wv]))
assign_ops = [
logstds[i].assign(logstd) for (i, logstd) in enumerate(esti_logstds)
]
assign_op = tf.group(assign_ops)
# prediction: rmse & log likelihood
bn = model.observe(**merge_dicts(latent, observed))
y_mean = bn["y_mean"]
y_pred = tf.reduce_mean(y_mean, 0)
# Define training/evaluation parameters
epochs = 500
batch_size = 100
iters = (n_train - 1) // batch_size + 1
preds = []
epochs_ave_pred = 1
# Run the inference
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(1, epochs + 1):
perm = np.random.permutation(x_train.shape[0])
x_train = x_train[perm, :]
y_train = y_train[perm]
for t in range(iters):
x_batch = x_train[t * batch_size:(t + 1) * batch_size]
y_batch = y_train[t * batch_size:(t + 1) * batch_size]
_, mean_k_value = sess.run([sample_op, mean_k],
feed_dict={
x: x_batch,
y: y_batch
})
# print("Epoch {} mean_k = {}".format(epoch, mean_k_value))
sess.run(assign_op)
test_pred = sess.run(y_pred, feed_dict={x: x_test})
preds.append(test_pred)
pred = np.mean(preds[-epochs_ave_pred:], axis=0)
test_rmse = np.sqrt(np.mean((pred - y_test)**2)) * std_y_train
print('>> Epoch {} Test = {} logstds = {}'.format(
epoch, test_rmse, sess.run(output_logstds)))
def main():
# tf.set_random_seed(1237)
# np.random.seed(1234)
hps = parser.parse_args()
# Load data
data_path = os.path.join(conf.data_dir, hps.dataset + '.data')
data_func = getattr(dataset, 'load_uci_' + hps.dataset)
x_train, y_train, x_valid, y_valid, x_test, y_test = data_func(data_path)
x_train = np.vstack([x_train, x_valid])
y_train = np.hstack([y_train, y_valid])
n_train, n_covariates = x_train.shape
hps.dtype = getattr(tf, hps.dtype)
# Standardize data
x_train, x_test, _, _ = dataset.standardize(x_train, x_test)
y_train, y_test, mean_y_train, std_y_train = dataset.standardize(
y_train, y_test)
# Build model
kernel = RBFKernel(n_covariates)
x_ph = tf.placeholder(hps.dtype, [None, n_covariates], 'x')
y_ph = tf.placeholder(hps.dtype, [None], 'y')
z_pos = tf.get_variable('z/pos', [hps.n_z, n_covariates],
hps.dtype,
initializer=tf.random_uniform_initializer(-1, 1))
n_particles_ph = n_particles_ph = tf.placeholder(tf.int32, [],
'n_particles')
batch_size = tf.cast(tf.shape(x_ph)[0], hps.dtype)
model = build_model(hps, kernel, z_pos, x_ph, n_particles_ph)
variational = build_variational(hps, kernel, z_pos, x_ph, n_particles_ph)
# ELBO = E_q log (p(y|fx)p(fx|fz)p(fz) / p(fx|fz)q(fz))
# So we remove p(fx|fz) in both log_joint and latent
def log_joint(bn):
prior, log_py_given_fx = bn.cond_log_prob(['fz', 'y'])
return prior + log_py_given_fx / batch_size * n_train
model.log_joint = log_joint
[var_fz, var_fx] = variational.query(['fz', 'fx'],
outputs=True,
local_log_prob=True)
var_fx = (var_fx[0], tf.zeros_like(var_fx[1]))
lower_bound = zs.variational.elbo(model,
observed={'y': y_ph},
latent={
'fz': var_fz,
'fx': var_fx
},
axis=0)
cost = lower_bound.sgvb()
optimizer = tf.train.AdamOptimizer(learning_rate=hps.lr)
infer_op = optimizer.minimize(cost)
# Prediction ops
model = model.observe(fx=var_fx[0], y=y_ph)
log_likelihood = model.cond_log_prob('y')
std_y_train = tf.cast(std_y_train, hps.dtype)
log_likelihood = zs.log_mean_exp(log_likelihood, 0) / batch_size - \
tf.log(std_y_train)
y_pred_mean = tf.reduce_mean(model['y'].distribution.mean, axis=0)
pred_mse = tf.reduce_mean((y_pred_mean - y_ph)**2) * std_y_train**2
def infer_step(sess, x_batch, y_batch):
fd = {x_ph: x_batch, y_ph: y_batch, n_particles_ph: hps.n_particles}
return sess.run([infer_op, lower_bound], fd)[1]
def predict_step(sess, x_batch, y_batch):
fd = {
x_ph: x_batch,
y_ph: y_batch,
n_particles_ph: hps.n_particles_test
}
return sess.run([log_likelihood, pred_mse], fd)
iters = int(np.ceil(x_train.shape[0] / float(hps.batch_size)))
test_freq = 100
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(1, hps.n_epoch + 1):
lbs = []
indices = np.arange(x_train.shape[0])
np.random.shuffle(indices)
x_train = x_train[indices]
y_train = y_train[indices]
for t in range(iters):
lb = infer_step(
sess, x_train[t * hps.batch_size:(t + 1) * hps.batch_size],
y_train[t * hps.batch_size:(t + 1) * hps.batch_size])
lbs.append(lb)
if 10 * epoch % test_freq == 0:
print('Epoch {}: Lower bound = {}'.format(epoch, np.mean(lbs)))
if epoch % test_freq == 0:
test_lls = []
test_mses = []
for t in range(0, x_test.shape[0], hps.batch_size):
ll, mse = predict_step(sess, x_test[t:t + hps.batch_size],
y_test[t:t + hps.batch_size])
test_lls.append(ll)
test_mses.append(mse)
print('>> TEST')
print('>> Test log likelihood = {}, rmse = {}'.format(
np.mean(test_lls), np.sqrt(np.mean(test_mses))))
def main():
tf.set_random_seed(1237)
np.random.seed(2345)
# Load UCI Boston housing data
data_path = os.path.join(conf.data_dir, "housing.data")
x_train, y_train, x_valid, y_valid, x_test, y_test = \
dataset.load_uci_boston_housing(data_path)
x_train = np.vstack([x_train, x_valid])
y_train = np.hstack([y_train, y_valid])
n_train, x_dim = x_train.shape
# Standardize data
x_train, x_test, _, _ = dataset.standardize(x_train, x_test)
y_train, y_test, mean_y_train, std_y_train = dataset.standardize(
y_train, y_test)
# Define model parameters
n_hiddens = [50]
# Build the computation graph
n_particles = tf.placeholder(tf.int32, shape=[], name="n_particles")
x = tf.placeholder(tf.float32, shape=[None, x_dim])
y = tf.placeholder(tf.float32, shape=[None])
layer_sizes = [x_dim] + n_hiddens + [1]
w_names = ["w" + str(i) for i in range(len(layer_sizes) - 1)]
model = build_bnn(x, layer_sizes, n_particles)
variational = build_mean_field_variational(layer_sizes, n_particles)
def log_joint(bn):
log_pws = bn.cond_log_prob(w_names)
log_py_xw = bn.cond_log_prob('y')
return tf.add_n(log_pws) + tf.reduce_mean(log_py_xw, 1) * n_train
model.log_joint = log_joint
lower_bound = zs.variational.elbo(model, {'y': y},
variational=variational,
axis=0)
cost = lower_bound.sgvb()
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
infer_op = optimizer.minimize(cost)
# prediction: rmse & log likelihood
y_mean = lower_bound.bn["y_mean"]
y_pred = tf.reduce_mean(y_mean, 0)
rmse = tf.sqrt(tf.reduce_mean((y_pred - y)**2)) * std_y_train
log_py_xw = lower_bound.bn.cond_log_prob("y")
log_likelihood = tf.reduce_mean(zs.log_mean_exp(log_py_xw,
0)) - tf.log(std_y_train)
# Define training/evaluation parameters
lb_samples = 10
ll_samples = 5000
epochs = 500
batch_size = 10
iters = (n_train - 1) // batch_size + 1
test_freq = 10
# Run the inference
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(1, epochs + 1):
perm = np.random.permutation(x_train.shape[0])
x_train = x_train[perm, :]
y_train = y_train[perm]
lbs = []
for t in range(iters):
x_batch = x_train[t * batch_size:(t + 1) * batch_size]
y_batch = y_train[t * batch_size:(t + 1) * batch_size]
_, lb = sess.run([infer_op, lower_bound],
feed_dict={
n_particles: lb_samples,
x: x_batch,
y: y_batch
})
lbs.append(lb)
print('Epoch {}: Lower bound = {}'.format(epoch, np.mean(lbs)))
if epoch % test_freq == 0:
test_rmse, test_ll = sess.run([rmse, log_likelihood],
feed_dict={
n_particles: ll_samples,
x: x_test,
y: y_test
})
print('>> TEST')
print('>> Test rmse = {}, log_likelihood = {}'.format(
test_rmse, test_ll))
def main():
tf.set_random_seed(1237)
np.random.seed(1234)
# Load UCI Boston housing data
data_path = os.path.join(conf.data_dir, 'housing.data')
x_train, y_train, x_valid, y_valid, x_test, y_test = \
dataset.load_uci_boston_housing(data_path)
x_train = np.vstack([x_train, x_valid])
y_train = np.hstack([y_train, y_valid])
N, n_x = x_train.shape
# Standardize data
x_train, x_test, _, _ = dataset.standardize(x_train, x_test)
y_train, y_test, mean_y_train, std_y_train = dataset.standardize(
y_train, y_test)
# Define model parameters
n_hiddens = [50]
# Build the computation graph
n_particles = tf.placeholder(tf.int32, shape=[], name='n_particles')
x = tf.placeholder(tf.float32, shape=[None, n_x])
y = tf.placeholder(tf.float32, shape=[None])
layer_sizes = [n_x] + n_hiddens + [1]
w_names = ['w' + str(i) for i in range(len(layer_sizes) - 1)]
def log_joint(observed):
model, _ = bayesianNN(observed, x, n_x, layer_sizes, n_particles)
log_pws = model.local_log_prob(w_names)
log_py_xw = model.local_log_prob('y')
return tf.add_n(log_pws) + log_py_xw * N
variational = mean_field_variational(layer_sizes, n_particles)
qw_outputs = variational.query(w_names, outputs=True, local_log_prob=True)
latent = dict(zip(w_names, qw_outputs))
lower_bound = zs.variational.elbo(log_joint,
observed={'y': y},
latent=latent,
axis=0)
cost = tf.reduce_mean(lower_bound.sgvb())
lower_bound = tf.reduce_mean(lower_bound)
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
infer_op = optimizer.minimize(cost)
# prediction: rmse & log likelihood
observed = dict((w_name, latent[w_name][0]) for w_name in w_names)
observed.update({'y': y})
model, y_mean = bayesianNN(observed, x, n_x, layer_sizes, n_particles)
y_pred = tf.reduce_mean(y_mean, 0)
rmse = tf.sqrt(tf.reduce_mean((y_pred - y)**2)) * std_y_train
log_py_xw = model.local_log_prob('y')
log_likelihood = tf.reduce_mean(zs.log_mean_exp(log_py_xw, 0)) - \
tf.log(std_y_train)
# Define training/evaluation parameters
lb_samples = 10
ll_samples = 5000
epochs = 500
batch_size = 10
iters = int(np.floor(x_train.shape[0] / float(batch_size)))
test_freq = 10
# Run the inference
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(1, epochs + 1):
lbs = []
for t in range(iters):
x_batch = x_train[t * batch_size:(t + 1) * batch_size]
y_batch = y_train[t * batch_size:(t + 1) * batch_size]
_, lb = sess.run([infer_op, lower_bound],
feed_dict={
n_particles: lb_samples,
x: x_batch,
y: y_batch
})
lbs.append(lb)
print('Epoch {}: Lower bound = {}'.format(epoch, np.mean(lbs)))
if epoch % test_freq == 0:
test_lb, test_rmse, test_ll = sess.run(
[lower_bound, rmse, log_likelihood],
feed_dict={
n_particles: ll_samples,
x: x_test,
y: y_test
})
print('>> TEST')
print(
'>> Test lower bound = {}, rmse = {}, log_likelihood = {}'.
format(test_lb, test_rmse, test_ll))
