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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for stochastic graphs."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
sv = tf.contrib.bayesflow.stochastic_variables
st = tf.contrib.bayesflow.stochastic_tensor
vi = tf.contrib.bayesflow.variational_inference
dist = tf.contrib.distributions
class StochasticVariablesTest(tf.test.TestCase):
def testStochasticVariables(self):
shape = (10, 20)
with tf.variable_scope(
"stochastic_variables",
custom_getter=sv.make_stochastic_variable_getter(
dist_cls=dist.NormalWithSoftplusSigma)):
v = tf.get_variable("sv", shape)
self.assertTrue(isinstance(v, st.StochasticTensor))
self.assertTrue(isinstance(v.distribution, dist.NormalWithSoftplusSigma))
self.assertEqual(
{"stochastic_variables/sv_mu", "stochastic_variables/sv_sigma"},
set([v.op.name for v in tf.global_variables()]))
self.assertEqual(set(tf.trainable_variables()), set(tf.global_variables()))
v = tf.convert_to_tensor(v)
self.assertEqual(list(shape), v.get_shape().as_list())
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
self.assertEqual(shape, sess.run(v).shape)
def testStochasticVariablesWithConstantInitializer(self):
shape = (10, 20)
with tf.variable_scope(
"stochastic_variables",
custom_getter=sv.make_stochastic_variable_getter(
dist_cls=dist.NormalWithSoftplusSigma,
dist_kwargs={"validate_args": True},
param_initializers={
"mu": np.ones(shape) * 4.,
"sigma": np.ones(shape) * 2.
})):
v = tf.get_variable("sv")
for var in tf.global_variables():
if "mu" in var.name:
mu_var = var
if "sigma" in var.name:
sigma_var = var
v = tf.convert_to_tensor(v)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
self.assertAllEqual(np.ones(shape) * 4., sess.run(mu_var))
self.assertAllEqual(np.ones(shape) * 2., sess.run(sigma_var))
self.assertEqual(shape, sess.run(v).shape)
def testStochasticVariablesWithCallableInitializer(self):
shape = (10, 20)
def sigma_init(shape, dtype, partition_info):
_ = partition_info
return tf.ones(shape, dtype=dtype) * 2.
with tf.variable_scope(
"stochastic_variables",
custom_getter=sv.make_stochastic_variable_getter(
dist_cls=dist.NormalWithSoftplusSigma,
dist_kwargs={"validate_args": True},
param_initializers={
"mu": np.ones(
shape, dtype=np.float32) * 4.,
"sigma": sigma_init
})):
v = tf.get_variable("sv", shape)
for var in tf.global_variables():
if "mu" in var.name:
mu_var = var
if "sigma" in var.name:
sigma_var = var
v = tf.convert_to_tensor(v)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
self.assertAllEqual(np.ones(shape) * 4., sess.run(mu_var))
self.assertAllEqual(np.ones(shape) * 2., sess.run(sigma_var))
self.assertEqual(shape, sess.run(v).shape)
def testStochasticVariablesWithPrior(self):
shape = (10, 20)
prior = dist.Normal(0., 1.)
with tf.variable_scope(
"stochastic_variables",
custom_getter=sv.make_stochastic_variable_getter(
dist_cls=dist.NormalWithSoftplusSigma, prior=prior)):
w = tf.get_variable("weights", shape)
x = tf.random_uniform((8, 10))
y = tf.matmul(x, w)
prior_map = vi._find_variational_and_priors(y, None)
self.assertEqual(prior_map[w], prior)
elbo = vi.elbo(y, keep_batch_dim=False)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(elbo)
def testStochasticVariablesWithCallablePriorInitializer(self):
def prior_init(shape, dtype):
return dist.Normal(tf.zeros(shape, dtype), tf.ones(shape, dtype))
with tf.variable_scope(
"stochastic_variables",
custom_getter=sv.make_stochastic_variable_getter(
dist_cls=dist.NormalWithSoftplusSigma, prior=prior_init)):
w = tf.get_variable("weights", (10, 20))
x = tf.random_uniform((8, 10))
y = tf.matmul(x, w)
prior_map = vi._find_variational_and_priors(y, None)
self.assertTrue(isinstance(prior_map[w], dist.Normal))
elbo = vi.elbo(y, keep_batch_dim=False)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(elbo)
if __name__ == "__main__":
tf.test.main()
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