def test_normalnormal_run(self):
with self.test_session() as sess:
x_data = np.array([0.0] * 50, dtype=np.float32)
mu = Normal(loc=0.0, scale=1.0)
x = Normal(loc=tf.ones(50) * mu, scale=1.0)
qmu_loc = tf.Variable(tf.random_normal([]))
qmu_scale = tf.nn.softplus(tf.Variable(tf.random_normal([])))
qmu = Normal(loc=qmu_loc, scale=qmu_scale)
# analytic solution: N(loc=0.0, scale=\sqrt{1/51}=0.140)
n_iter = 5000
inference = ed.KLqp({mu: qmu}, data={x: x_data})
inference.run(n_iter=n_iter)
self.assertAllClose(qmu.mean().eval(), 0, rtol=1e-1, atol=1e-1)
self.assertAllClose(qmu.stddev().eval(),
np.sqrt(1 / 51),
rtol=1e-1,
atol=1e-1)
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='optimizer')
old_t, old_variables = sess.run([inference.t, variables])
self.assertEqual(old_t, n_iter)
sess.run(inference.reset)
new_t, new_variables = sess.run([inference.t, variables])
self.assertEqual(new_t, 0)
self.assertNotEqual(old_variables, new_variables)
def _test_normal_normal(self, Inference, default, *args, **kwargs):
with self.test_session() as sess:
x_data = np.array([0.0] * 50, dtype=np.float32)
mu = Normal(loc=0.0, scale=1.0)
x = Normal(loc=mu, scale=1.0, sample_shape=50)
if not default:
qmu_loc = tf.Variable(tf.random_normal([]))
qmu_scale = tf.nn.softplus(tf.Variable(tf.random_normal([])))
qmu = Normal(loc=qmu_loc, scale=qmu_scale)
# analytic solution: N(loc=0.0, scale=\sqrt{1/51}=0.140)
inference = Inference({mu: qmu}, data={x: x_data})
else:
inference = Inference([mu], data={x: x_data})
qmu = inference.latent_vars[mu]
inference.run(*args, **kwargs)
self.assertAllClose(qmu.mean().eval(), 0, rtol=0.15, atol=0.5)
self.assertAllClose(qmu.stddev().eval(),
np.sqrt(1 / 51),
rtol=0.15,
atol=0.5)
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='optimizer')
old_t, old_variables = sess.run([inference.t, variables])
self.assertEqual(old_t, inference.n_iter)
sess.run(inference.reset)
new_t, new_variables = sess.run([inference.t, variables])
self.assertEqual(new_t, 0)
self.assertNotEqual(old_variables, new_variables)
def _test_normal_normal(self, Inference, default, *args, **kwargs):
with self.test_session() as sess:
x_data = np.array([0.0] * 50, dtype=np.float32)
mu = Normal(loc=0.0, scale=1.0)
x = Normal(loc=mu, scale=1.0, sample_shape=50)
if not default:
qmu_loc = tf.Variable(tf.random_normal([]))
qmu_scale = tf.nn.softplus(tf.Variable(tf.random_normal([])))
qmu = Normal(loc=qmu_loc, scale=qmu_scale)
# analytic solution: N(loc=0.0, scale=\sqrt{1/51}=0.140)
inference = Inference({mu: qmu}, data={x: x_data})
else:
inference = Inference([mu], data={x: x_data})
qmu = inference.latent_vars[mu]
inference.run(*args, **kwargs)
self.assertAllClose(qmu.mean().eval(), 0, rtol=0.1, atol=0.6)
self.assertAllClose(qmu.stddev().eval(), np.sqrt(1 / 51),
rtol=0.15, atol=0.5)
variables = tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES, scope='optimizer')
old_t, old_variables = sess.run([inference.t, variables])
self.assertEqual(old_t, inference.n_iter)
sess.run(inference.reset)
new_t, new_variables = sess.run([inference.t, variables])
self.assertEqual(new_t, 0)
self.assertNotEqual(old_variables, new_variables)
def test_normalnormal_run(self):
with self.test_session() as sess:
x_data = np.array([0.0] * 50, dtype=np.float32)
mu = Normal(loc=0.0, scale=1.0)
x = Normal(loc=tf.ones(50) * mu, scale=1.0)
qmu_loc = tf.Variable(tf.random_normal([]))
qmu_scale = tf.nn.softplus(tf.Variable(tf.random_normal([])))
qmu = Normal(loc=qmu_loc, scale=qmu_scale)
# analytic solution: N(loc=0.0, scale=\sqrt{1/51}=0.140)
inference = ed.KLpq({mu: qmu}, data={x: x_data})
inference.run(n_samples=25, n_iter=100)
self.assertAllClose(qmu.mean().eval(), 0, rtol=1e-1, atol=1e-1)
self.assertAllClose(qmu.stddev().eval(), np.sqrt(1 / 51),
rtol=1e-1, atol=1e-1)
def test_normalnormal_run(self):
with self.test_session() as sess:
x_data = np.array([0.0] * 50, dtype=np.float32)
mu = Normal(loc=0.0, scale=1.0)
x = Normal(loc=tf.ones(50) * mu, scale=1.0)
qmu_loc = tf.Variable(tf.random_normal([]))
qmu_scale = tf.nn.softplus(tf.Variable(tf.random_normal([])))
qmu = Normal(loc=qmu_loc, scale=qmu_scale)
# analytic solution: N(loc=0.0, scale=\sqrt{1/51}=0.140)
inference = ed.KLqp({mu: qmu}, data={x: x_data})
inference.run(n_iter=5000)
self.assertAllClose(qmu.mean().eval(), 0, rtol=1e-1, atol=1e-1)
self.assertAllClose(qmu.stddev().eval(),
np.sqrt(1 / 51),
rtol=1e-1,
atol=1e-1)
def main(_):
def ratio_estimator(data, local_vars, global_vars):
"""Takes as input a dict of data x, local variable samples z, and
global variable samples beta; outputs real values of shape
(x.shape[0] + z.shape[0],). In this example, there are no local
variables.
"""
# data[y] has shape (M,); global_vars[w] has shape (D,)
# we concatenate w to each data point y, so input has shape (M, 1 + D)
input = tf.concat([
tf.reshape(data[y], [FLAGS.M, 1]),
tf.tile(tf.reshape(global_vars[w], [1, FLAGS.D]), [FLAGS.M, 1])
], 1)
hidden = tf.layers.dense(input, 64, activation=tf.nn.relu)
output = tf.layers.dense(hidden, 1, activation=None)
return output
ed.set_seed(42)
# DATA
w_true = np.ones(FLAGS.D) * 5.0
X_train, y_train = build_toy_dataset(FLAGS.N, w_true)
X_test, y_test = build_toy_dataset(FLAGS.N, w_true)
data = generator([X_train, y_train], FLAGS.M)
# MODEL
X = tf.placeholder(tf.float32, [FLAGS.M, FLAGS.D])
y_ph = tf.placeholder(tf.float32, [FLAGS.M])
w = Normal(loc=tf.zeros(FLAGS.D), scale=tf.ones(FLAGS.D))
y = Normal(loc=ed.dot(X, w), scale=tf.ones(FLAGS.M))
# INFERENCE
qw = Normal(loc=tf.get_variable("qw/loc", [FLAGS.D]) + 1.0,
scale=tf.nn.softplus(tf.get_variable("qw/scale", [FLAGS.D])))
inference = ed.ImplicitKLqp({w: qw},
data={y: y_ph},
discriminator=ratio_estimator,
global_vars={w: qw})
inference.initialize(n_iter=5000,
n_print=100,
scale={y: float(FLAGS.N) / FLAGS.M})
sess = ed.get_session()
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
X_batch, y_batch = next(data)
for _ in range(5):
info_dict_d = inference.update(variables="Disc",
feed_dict={
X: X_batch,
y_ph: y_batch
})
info_dict = inference.update(variables="Gen",
feed_dict={
X: X_batch,
y_ph: y_batch
})
info_dict['loss_d'] = info_dict_d['loss_d']
info_dict[
't'] = info_dict['t'] // 6 # say set of 6 updates is 1 iteration
t = info_dict['t']
inference.print_progress(info_dict)
if t == 1 or t % inference.n_print == 0:
# Check inferred posterior parameters.
mean, std = sess.run([qw.mean(), qw.stddev()])
print("\nInferred mean & std:")
print(mean)
print(std)
inference.initialize(n_iter=5000, n_print=100, scale={y: float(N) / M})
sess = ed.get_session()
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
X_batch, y_batch = next(data)
for _ in range(5):
info_dict_d = inference.update(variables="Disc",
feed_dict={
X: X_batch,
y_ph: y_batch
})
info_dict = inference.update(variables="Gen",
feed_dict={
X: X_batch,
y_ph: y_batch
})
info_dict['loss_d'] = info_dict_d['loss_d']
info_dict['t'] = info_dict['t'] // 6 # say set of 6 updates is 1 iteration
t = info_dict['t']
inference.print_progress(info_dict)
if t == 1 or t % inference.n_print == 0:
# Check inferred posterior parameters.
mean, std = sess.run([qw.mean(), qw.stddev()])
print("\nInferred mean & std:")
print(mean)
print(std)
# VISUALIZATION
def visualise(X_data, y_data, w, b, ax, n_samples=10):
w_samples = w.sample(n_samples)[:, 0].eval()
b_samples = b.sample(n_samples).eval()
ax.scatter(X_data[:, 0],
y_data) # Note, only the 1st input dimension is plotted.
inputs = np.linspace(-8, 8, num=400)
for ns in range(n_samples):
output = inputs * w_samples[ns] + b_samples[ns]
ax.plot(inputs, output)
fig = plt.figure()
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
visualise(X_train, y_train, w, b, ax1) # Models sampled from the prior
visualise(X_train, y_train, qw, qb, ax2) # Models sampled from the posterior
plt.show()
# EXPLORE THE LEARNED MODEL
print('Point estimate for STD of weights:', w_prior_std.eval())
# Retrieve the means and STDs of the estimated regression coefficients
w_est_mean = qw.mean().eval()
w_est_std = qw.stddev().eval()
print('Correlation between estimated and learned weights: ',
np.corrcoef(w_est_mean, w_true)[0, 1])
for _ in range(20):
plt.scatter(x_train[:,0], np.log(yhat.eval()[:,which_species]), alpha=0.1, c="black", s=10);
# In[ ]:
# Predictions using the approximate posterior on z
plt.figure(figsize=(10, 10))
plt.scatter(x_train[:,0], np.log(mu[:,which_species]));
for _ in range(20):
plt.scatter(x_train[:,0], np.log(qyhat.eval()[:,which_species]), alpha=0.1, c="darkred", s=10);
# In[ ]:
plt.hist(qz.stddev().eval(), bins=50);
# In[ ]:
# Approximate posterior distributions of z, given x. Should be no big (low-frequency?) trends or gaps
for i in range(n_z):
for _ in range(25):
plt.scatter(x_train[:,0], qz.eval()[:,i], alpha=0.1, c="darkred", s=5)
plt.show()
# In[ ]:
nn = 1000
xx = 0
def main(_):
def ratio_estimator(data, local_vars, global_vars):
"""Takes as input a dict of data x, local variable samples z, and
global variable samples beta; outputs real values of shape
(x.shape[0] + z.shape[0],). In this example, there are no local
variables.
"""
# data[y] has shape (M,); global_vars[w] has shape (D,)
# we concatenate w to each data point y, so input has shape (M, 1 + D)
input = tf.concat([
tf.reshape(data[y], [FLAGS.M, 1]),
tf.tile(tf.reshape(global_vars[w], [1, FLAGS.D]), [FLAGS.M, 1])], 1)
hidden = tf.layers.dense(input, 64, activation=tf.nn.relu)
output = tf.layers.dense(hidden, 1, activation=None)
return output
ed.set_seed(42)
# DATA
w_true = np.ones(FLAGS.D) * 5.0
X_train, y_train = build_toy_dataset(FLAGS.N, w_true)
X_test, y_test = build_toy_dataset(FLAGS.N, w_true)
data = generator([X_train, y_train], FLAGS.M)
# MODEL
X = tf.placeholder(tf.float32, [FLAGS.M, FLAGS.D])
y_ph = tf.placeholder(tf.float32, [FLAGS.M])
w = Normal(loc=tf.zeros(FLAGS.D), scale=tf.ones(FLAGS.D))
y = Normal(loc=ed.dot(X, w), scale=tf.ones(FLAGS.M))
# INFERENCE
qw = Normal(loc=tf.get_variable("qw/loc", [FLAGS.D]) + 1.0,
scale=tf.nn.softplus(tf.get_variable("qw/scale", [FLAGS.D])))
inference = ed.ImplicitKLqp(
{w: qw}, data={y: y_ph},
discriminator=ratio_estimator, global_vars={w: qw})
inference.initialize(n_iter=5000, n_print=100,
scale={y: float(FLAGS.N) / FLAGS.M})
sess = ed.get_session()
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
X_batch, y_batch = next(data)
for _ in range(5):
info_dict_d = inference.update(
variables="Disc", feed_dict={X: X_batch, y_ph: y_batch})
info_dict = inference.update(
variables="Gen", feed_dict={X: X_batch, y_ph: y_batch})
info_dict['loss_d'] = info_dict_d['loss_d']
info_dict['t'] = info_dict['t'] // 6 # say set of 6 updates is 1 iteration
t = info_dict['t']
inference.print_progress(info_dict)
if t == 1 or t % inference.n_print == 0:
# Check inferred posterior parameters.
mean, std = sess.run([qw.mean(), qw.stddev()])
print("\nInferred mean & std:")
print(mean)
print(std)
def main(_):
# setting up output directory
outdir = os.path.expanduser(FLAGS.outdir)
os.makedirs(outdir, exist_ok=True)
N, M, D, R_true, I_train, I_test = get_data()
debug('N, M, D', N, M, D)
# Solution components
weights, qUVt_components = [], []
# Files to log metrics
times_filename = os.path.join(outdir, 'times.csv')
mse_train_filename = os.path.join(outdir, 'mse_train.csv')
mse_test_filename = os.path.join(outdir, 'mse_test.csv')
ll_test_filename = os.path.join(outdir, 'll_test.csv')
ll_train_filename = os.path.join(outdir, 'll_train.csv')
elbos_filename = os.path.join(outdir, 'elbos.csv')
gap_filename = os.path.join(outdir, 'gap.csv')
step_filename = os.path.join(outdir, 'steps.csv')
# 'adafw', 'ada_afw', 'ada_pfw'
if FLAGS.fw_variant.startswith('ada'):
lipschitz_filename = os.path.join(outdir, 'lipschitz.csv')
iter_info_filename = os.path.join(outdir, 'iter_info.txt')
start = 0
if FLAGS.restore:
#start = 50
#qUVt_components = get_random_components(D, N, M, start)
#weights = np.random.dirichlet([1.] * start).astype(np.float32)
#lipschitz_estimate = opt.adafw_linit()
parameters = np.load(os.path.join(outdir, 'qt_latest.npz'))
weights = list(parameters['weights'])
start = parameters['fw_iter']
qUVt_components = list(parameters['comps'])
assert len(weights) == len(qUVt_components), "Inconsistent storage"
# get lipschitz estimate from the file, could've stored it
# in params but that would mean different saved file for
# adaptive variants
if FLAGS.fw_variant.startswith('ada'):
lipschitz_filename = os.path.join(outdir, 'lipschitz.csv')
if not os.path.isfile(lipschitz_filename):
raise ValueError("Inconsistent storage")
with open(lipschitz_filename, 'r') as f:
l = f.readlines()
lipschitz_estimate = float(l[-1].strip())
else:
# empty the files present in the folder already
open(times_filename, 'w').close()
open(mse_train_filename, 'w').close()
open(mse_test_filename, 'w').close()
open(ll_test_filename, 'w').close()
open(ll_train_filename, 'w').close()
open(elbos_filename, 'w').close()
open(gap_filename, 'w').close()
open(step_filename, 'w').close()
# 'adafw', 'ada_afw', 'ada_pfw'
if FLAGS.fw_variant.startswith('ada'):
open(lipschitz_filename, 'w').close()
open(iter_info_filename, 'w').close()
for t in range(start, start + FLAGS.n_fw_iter):
g = tf.Graph()
with g.as_default():
tf.set_random_seed(FLAGS.seed)
sess = tf.InteractiveSession()
with sess.as_default():
# MODEL
I = tf.placeholder(tf.float32, [N, M])
scale_uv = tf.concat(
[tf.ones([D, N]), tf.ones([D, M])], axis=1)
mean_uv = tf.concat(
[tf.zeros([D, N]), tf.zeros([D, M])], axis=1)
UV = Normal(loc=mean_uv, scale=scale_uv)
R = Normal(loc=tf.matmul(tf.transpose(UV[:, :N]), UV[:, N:]),
scale=tf.ones([N, M])) # generator dist. for matrix
R_mask = R * I # generated masked matrix
p_joint = Joint(R_true, I_train, sess, D, N, M)
if t == 0:
fw_iterates = {}
else:
# Current solution
prev_components = [
coreutils.base_loc_scale('mvn0',
c['loc'],
c['scale'],
multivariate=False)
for c in qUVt_components
]
qUV_prev = coreutils.get_mixture(weights, prev_components)
fw_iterates = {UV: qUV_prev}
# LMO (via relbo INFERENCE)
mean_suv = tf.concat([
tf.get_variable("qU/loc", [D, N]),
tf.get_variable("qV/loc", [D, M])
],
axis=1)
scale_suv = tf.concat([
tf.nn.softplus(tf.get_variable("qU/scale", [D, N])),
tf.nn.softplus(tf.get_variable("qV/scale", [D, M]))
],
axis=1)
sUV = Normal(loc=mean_suv, scale=scale_suv)
#inference = relbo.KLqp({UV: sUV}, data={R: R_true, I: I_train},
inference = relbo.KLqp({UV: sUV},
data={
R_mask: R_true,
I: I_train
},
fw_iterates=fw_iterates,
fw_iter=t)
inference.run(n_iter=FLAGS.LMO_iter)
loc_s = sUV.mean().eval()
scale_s = sUV.stddev().eval()
# sUV is batched distrbution, there are issues making
# Mixture with batch distributions. mvn0
# with event size (D, N + M) and batch size ()
# NOTE log_prob(sample) still returns tensor
# mvn and multivariatenormaldiag work for 1-D not 2-D shapes
sUV_mv = coreutils.base_loc_scale('mvn0',
loc_s,
scale_s,
multivariate=False)
# TODO send sUV or sUV_mv as argument to step size? sample
# works the same way. same with log_prob
total_time = 0.
data = {R: R_true, I: I_train}
if t == 0:
gamma = 1.
lipschitz_estimate = opt.adafw_linit()
step_type = 'init'
elif FLAGS.fw_variant == 'fixed':
start_step_time = time.time()
step_result = opt.fixed(weights, qUVt_components, qUV_prev,
loc_s, scale_s, sUV, p_joint, data,
t)
end_step_time = time.time()
total_time += float(end_step_time - start_step_time)
elif FLAGS.fw_variant == 'line_search':
start_step_time = time.time()
step_result = opt.line_search_dkl(weights, qUVt_components,
qUV_prev, loc_s, scale_s,
sUV, p_joint, data, t)
end_step_time = time.time()
total_time += float(end_step_time - start_step_time)
elif FLAGS.fw_variant == 'adafw':
start_step_time = time.time()
step_result = opt.adaptive_fw(weights, qUVt_components,
qUV_prev, loc_s, scale_s,
sUV, p_joint, data, t,
lipschitz_estimate)
end_step_time = time.time()
total_time += float(end_step_time - start_step_time)
step_type = step_result['step_type']
if step_type == 'adaptive':
lipschitz_estimate = step_result['l_estimate']
elif FLAGS.fw_variant == 'ada_pfw':
start_step_time = time.time()
step_result = opt.adaptive_pfw(weights, qUVt_components,
qUV_prev, loc_s, scale_s,
sUV, p_joint, data, t,
lipschitz_estimate)
end_step_time = time.time()
total_time += float(end_step_time - start_step_time)
step_type = step_result['step_type']
if step_type in ['adaptive', 'drop']:
lipschitz_estimate = step_result['l_estimate']
elif FLAGS.fw_variant == 'ada_afw':
start_step_time = time.time()
step_result = opt.adaptive_pfw(weights, qUVt_components,
qUV_prev, loc_s, scale_s,
sUV, p_joint, data, t,
lipschitz_estimate)
end_step_time = time.time()
total_time += float(end_step_time - start_step_time)
step_type = step_result['step_type']
if step_type in ['adaptive', 'away', 'drop']:
lipschitz_estimate = step_result['l_estimate']
if t == 0:
gamma = 1.
weights.append(gamma)
qUVt_components.append({'loc': loc_s, 'scale': scale_s})
new_components = [sUV_mv]
else:
qUVt_components = step_result['params']
weights = step_result['weights']
gamma = step_result['gamma']
new_components = [
coreutils.base_loc_scale('mvn0',
c['loc'],
c['scale'],
multivariate=False)
for c in qUVt_components
]
qUV_new = coreutils.get_mixture(weights, new_components)
#qR = Normal(
# loc=tf.matmul(
# tf.transpose(qUV_new[:, :N]), qUV_new[:, N:]),
# scale=tf.ones([N, M]))
qR = ed.copy(R, {UV: qUV_new})
cR = ed.copy(R_mask, {UV: qUV_new}) # reconstructed matrix
# Log metrics for current iteration
logger.info('total time %f' % total_time)
append_to_file(times_filename, total_time)
logger.info('iter %d, gamma %.4f' % (t, gamma))
append_to_file(step_filename, gamma)
if t > 0:
gap_t = step_result['gap']
logger.info('iter %d, gap %.4f' % (t, gap_t))
append_to_file(gap_filename, gap_t)
# CRITICISM
if FLAGS.fw_variant.startswith('ada'):
append_to_file(lipschitz_filename, lipschitz_estimate)
append_to_file(iter_info_filename, step_type)
logger.info('lt = %.5f, iter_type = %s' %
(lipschitz_estimate, step_type))
test_mse = ed.evaluate('mean_squared_error',
data={
cR: R_true,
I: I_test
})
logger.info("iter %d ed test mse %.5f" % (t, test_mse))
append_to_file(mse_test_filename, test_mse)
train_mse = ed.evaluate('mean_squared_error',
data={
cR: R_true,
I: I_train
})
logger.info("iter %d ed train mse %.5f" % (t, train_mse))
append_to_file(mse_train_filename, train_mse)
# very slow
#train_ll = log_likelihood(qUV_new, R_true, I_train, sess, D, N,
# M)
train_ll = ed.evaluate('log_lik',
data={
qR: R_true.astype(np.float32),
I: I_train
})
logger.info("iter %d train log lik %.5f" % (t, train_ll))
append_to_file(ll_train_filename, train_ll)
#test_ll = log_likelihood(qUV_new, R_true, I_test, sess, D, N, M)
test_ll = ed.evaluate('log_lik',
data={
qR: R_true.astype(np.float32),
I: I_test
})
logger.info("iter %d test log lik %.5f" % (t, test_ll))
append_to_file(ll_test_filename, test_ll)
# elbo_loss might be meaningless
elbo_loss = elboModel.KLqp({UV: qUV_new},
data={
R: R_true,
I: I_train
})
elbo_t = elbo(qUV_new, p_joint)
res_update = elbo_loss.run()
logger.info('iter %d -elbo loss %.2f or %.2f' %
(t, res_update['loss'], elbo_t))
append_to_file(elbos_filename,
"%f,%f" % (elbo_t, res_update['loss']))
# serialize the current iterate
np.savez(os.path.join(outdir, 'qt_latest.npz'),
weights=weights,
comps=qUVt_components,
fw_iter=t + 1)
sess.close()
tf.reset_default_graph()
class BayesianRegression:
def __init__(self, in_dim=1, n_classes=2):
"""
Bayesian Logistic regression based on Edward lib (http://edwardlib.org).
y = W * x + b
:param in_dim:
:param n_classes:
"""
self.in_dim = in_dim
self.n_classes = n_classes
self.X = tf.placeholder(tf.float32, [None, self.in_dim])
self.W = Normal(loc=tf.zeros([self.in_dim, self.n_classes]),
scale=tf.ones([self.in_dim, self.n_classes]))
self.b = Normal(loc=tf.zeros(self.n_classes),
scale=tf.ones(self.n_classes))
h = tf.matmul(self.X, self.W) + self.b
self.y = Normal(loc=tf.sigmoid(-h), scale=0.1)
self.qW = Normal(loc=tf.get_variable("qW/loc",
[self.in_dim, self.n_classes]),
scale=tf.nn.softplus(
tf.get_variable("qW/scale",
[self.in_dim, self.n_classes])))
self.qb = Normal(loc=tf.get_variable("qb/loc", [self.n_classes]),
scale=tf.nn.softplus(
tf.get_variable("qb/scale", [self.n_classes])))
def infer(self, X, y, n_samples=5, n_iter=250):
inference = ed.KLqp({
self.W: self.qW,
self.b: self.qb,
},
data={
self.y: y,
self.X: X
})
inference.run(n_samples=n_samples, n_iter=n_iter)
def predict(self, X):
self.qW_mean = self.qW.mean().eval()
self.qb_mean = self.qb.mean().eval()
h = tf.matmul(X, self.qW_mean) + self.qb_mean
return tf.sigmoid(-h).eval()
def sample_boudary(self, X):
qW = self.qW.eval()
qb = self.qb.eval()
w = -qW[0][0] / qW[1][0]
b = (0.5 - qb[0]) / qW[0][0]
return w, b
def predict_std(self, X):
self.qW_stddev = self.qW.stddev().eval()
self.qb_stddev = self.qb.stddev().eval()
h = tf.matmul(X, self.qW_stddev) + self.qb_stddev
return tf.sigmoid(-h).eval()
def get_coef(self):
return self.qW.mean().eval().T[0]
qw = Normal(loc=tf.Variable(tf.random_normal([D]) + 1.0),
scale=tf.nn.softplus(tf.Variable(tf.random_normal([D]))))
inference = ed.ImplicitKLqp(
{w: qw}, data={y: y_ph},
discriminator=ratio_estimator, global_vars={w: qw})
inference.initialize(n_iter=5000, n_print=100, scale={y: float(N) / M})
sess = ed.get_session()
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
X_batch, y_batch = next(data)
for _ in range(5):
info_dict_d = inference.update(
variables="Disc", feed_dict={X: X_batch, y_ph: y_batch})
info_dict = inference.update(
variables="Gen", feed_dict={X: X_batch, y_ph: y_batch})
info_dict['loss_d'] = info_dict_d['loss_d']
info_dict['t'] = info_dict['t'] // 6 # say set of 6 updates is 1 iteration
t = info_dict['t']
inference.print_progress(info_dict)
if t == 1 or t % inference.n_print == 0:
# Check inferred posterior parameters.
mean, std = sess.run([qw.mean(), qw.stddev()])
print("\nInferred mean & std:")
print(mean)
print(std)