def __init__(self, model, data=Data(), transform=tf.identity):
if hasattr(model, 'num_vars'):
variational = Variational()
variational.add(PointMass(model.num_vars, transform))
else:
variational = Variational()
variational.add(PointMass(0, transform))
VariationalInference.__init__(self, model, variational, data)
def __init__(self, model, data=None, params=None):
with tf.variable_scope("variational"):
if hasattr(model, 'n_vars'):
variational = Variational()
variational.add(PointMass(model.n_vars, params))
else:
variational = Variational()
variational.add(PointMass(0))
super(MAP, self).__init__(model, variational, data)
def main():
data = ed.Data(np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1]))
model = BetaBernoulli()
variational = Variational()
variational.add(Beta())
# mean-field variational inference.
inference = ed.MFVI(model, variational, data)
inference.run(n_iter=10000)
def _test(self, sess, data, n_minibatch, x=None, is_file=False):
model = NormalModel()
variational = Variational()
variational.add(Normal())
inference = ed.MFVI(model, variational, data)
inference.initialize(n_minibatch=n_minibatch)
if x is not None:
# Placeholder setting.
# Check data is same as data fed to it.
feed_dict = {inference.data['x']: x}
# avoid directly fetching placeholder
data_id = {
k: tf.identity(v)
for k, v in six.iteritems(inference.data)
}
val = sess.run(data_id, feed_dict)
assert np.all(val['x'] == x)
elif is_file:
# File reader setting.
# Check data varies by session run.
val = sess.run(inference.data)
val_1 = sess.run(inference.data)
assert not np.all(val['x'] == val_1['x'])
elif n_minibatch is None:
# Preloaded full setting.
# Check data is full data.
val = sess.run(inference.data)
assert np.all(val['x'] == data['x'])
elif n_minibatch == 1:
# Preloaded batch setting, with n_minibatch=1.
# Check data is randomly shuffled.
assert not np.all([
sess.run(inference.data)['x'] == data['x'][i]
for i in range(10)
])
else:
# Preloaded batch setting.
# Check data is randomly shuffled.
val = sess.run(inference.data)
assert not np.all(val['x'] == data['x'][:n_minibatch])
# Check data varies by session run.
val_1 = sess.run(inference.data)
assert not np.all(val['x'] == val_1['x'])
inference.finalize()
def build_toy_dataset(N=40, noise_std=0.1):
ed.set_seed(0)
x = np.concatenate(
[np.linspace(0, 2, num=N / 2),
np.linspace(6, 8, num=N / 2)])
y = 0.075 * x + norm.rvs(0, noise_std, size=N)
x = (x - 4.0) / 4.0
x = x.reshape((N, 1))
return {'x': x, 'y': y}
ed.set_seed(42)
model = LinearModel()
variational = Variational()
variational.add(Normal(model.n_vars))
data = build_toy_dataset()
# Set up figure
fig = plt.figure(figsize=(8, 8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.ion()
plt.show(block=False)
sess = ed.get_session()
inference = ed.MFVI(model, variational, data)
inference.initialize(n_samples=5, n_print=5)
for t in range(250):
loss = inference.update()
if t % inference.n_print == 0:
