def test_withoutparams_initfixed(self):
# Build an SPN
model = spn.Poon11NaiveMixtureModel()
root1 = model.build()
# Save
path = self.out_path(self.cid() + ".spn")
saver = spn.JSONSaver(path, pretty=True)
saver.save(root1, save_param_vals=False)
# Reset graph
tf.reset_default_graph()
# Load
loader = spn.JSONLoader(path)
root2 = loader.load()
latent_indicators2 = loader.find_node('IndicatorLeaf')
init2 = spn.initialize_weights(root2)
val_mpe2 = root2.get_value(inference_type=spn.InferenceType.MPE)
val_marginal2 = root2.get_value(inference_type=spn.InferenceType.MARGINAL)
# Check model after loading
self.assertTrue(root2.is_valid())
with self.test_session() as sess:
init2.run()
out_marginal2 = sess.run(val_marginal2, feed_dict={latent_indicators2: model.feed})
out_mpe2 = sess.run(val_mpe2, feed_dict={latent_indicators2: model.feed})
self.assertAlmostEqual(out_marginal2[np.all(model.feed >= 0, axis=1), :].sum(),
1.0, places=6)
np.testing.assert_array_almost_equal(out_marginal2, model.true_values)
np.testing.assert_array_almost_equal(out_mpe2, model.true_mpe_values)
def test_withoutparams_initrandom(self):
# Build an SPN
feed = np.array(list(itertools.product(range(2), repeat=6)))
model = spn.DiscreteDenseModel(
num_classes=1, num_decomps=1, num_subsets=3,
num_mixtures=2, weight_initializer=tf.initializers.random_uniform(0.0, 1.0))
root1 = model.build(num_vars=6, num_vals=2)
# Save
path = self.out_path(self.cid() + ".spn")
saver = spn.JSONSaver(path, pretty=True)
saver.save(root1, save_param_vals=False)
# Reset graph
tf.reset_default_graph()
# Load
loader = spn.JSONLoader(path)
root2 = loader.load()
latent_indicators2 = loader.find_node('SampleIndicatorLeaf')
init2 = spn.initialize_weights(root2)
val_marginal2 = root2.get_value(inference_type=spn.InferenceType.MARGINAL)
# Check model after loading
self.assertTrue(root2.is_valid())
with self.test_session() as sess:
init2.run()
out_marginal2 = sess.run(val_marginal2, feed_dict={latent_indicators2: feed})
self.assertAlmostEqual(out_marginal2.sum(), 1.0, places=6)
def test_withparams_initrandom(self):
# Build an SPN
feed = np.array(list(itertools.product(range(2), repeat=6)))
model = spn.DiscreteDenseModel(
num_classes=1, num_decomps=1, num_subsets=3,
num_mixtures=2, weight_init_value=spn.ValueType.RANDOM_UNIFORM(0, 1))
root1 = model.build(num_vars=6, num_vals=2)
init1 = spn.initialize_weights(root1)
with tf.Session() as sess:
# Initialize
init1.run()
# Save
path = self.out_path(self.cid() + ".spn")
saver = spn.JSONSaver(path, pretty=True)
saver.save(root1, save_param_vals=True)
# Reset graph
tf.reset_default_graph()
with tf.Session() as sess:
# Load
loader = spn.JSONLoader(path)
root2 = loader.load(load_param_vals=True)
ivs2 = loader.find_node('SampleIVs')
val_marginal2 = root2.get_value(inference_type=spn.InferenceType.MARGINAL)
# Check model after loading
self.assertTrue(root2.is_valid())
out_marginal2 = sess.run(val_marginal2, feed_dict={ivs2: feed})
self.assertAlmostEqual(out_marginal2.sum(), 1.0, places=6)
# Writing graph
self.write_tf_graph(sess, self.sid(), self.cid())