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())
def test_discretedense_saving_3class_externallatent_indicators(self):
model1 = spn.DiscreteDenseModel(
num_classes=3,
num_decomps=2,
num_subsets=3,
num_mixtures=2,
input_dist=spn.DenseSPNGenerator.InputDist.MIXTURE,
num_input_mixtures=None,
weight_initializer=tf.initializers.random_uniform(0.0, 1.0))
sample_latent_indicators1 = spn.IndicatorLeaf(num_vars=6, num_vals=2)
class_latent_indicators1 = spn.IndicatorLeaf(num_vars=1, num_vals=3)
model1.build(sample_latent_indicators1,
class_input=class_latent_indicators1)
init1 = spn.initialize_weights(model1.root)
feed_samples = list(itertools.product(range(2), repeat=6))
feed_class = np.array([
i for i in range(3) for _ in range(len(feed_samples))
]).reshape(-1, 1)
feed_samples = np.array(feed_samples * 3)
with self.test_session() as sess:
# Initialize
init1.run()
# Save
path = self.out_path(self.cid() + ".spn")
model1.save_to_json(path,
pretty=True,
save_param_vals=True,
sess=sess)
# Reset graph
tf.reset_default_graph()
with self.test_session() as sess:
# Load
model2 = spn.Model.load_from_json(path,
load_param_vals=True,
sess=sess)
self.assertIs(type(model2), spn.DiscreteDenseModel)
val_marginal2 = model2.root.get_value(
inference_type=spn.InferenceType.MARGINAL)
# Check model after loading
self.assertTrue(model2.root.is_valid())
out_marginal2 = sess.run(val_marginal2,
feed_dict={
model2.sample_inputs[0].node:
feed_samples,
model2.class_input.node: feed_class
})
self.assertAlmostEqual(out_marginal2.sum(), 1.0, places=6)
# Writing graph
self.write_tf_graph(sess, self.sid(), self.cid())
def test_discretedense_1class_internalivs(self):
model = spn.DiscreteDenseModel(
num_classes=1,
num_decomps=2,
num_subsets=3,
num_mixtures=2,
input_dist=spn.DenseSPNGenerator.InputDist.MIXTURE,
num_input_mixtures=None,
weight_init_value=spn.ValueType.RANDOM_UNIFORM(0, 1))
root = model.build(num_vars=6, num_vals=2)
self.generic_model_test("1class", root, model.sample_ivs, None)
def test_discretedense_1class_internalivs(self):
model = spn.DiscreteDenseModel(
num_classes=1,
num_decomps=2,
num_subsets=3,
num_mixtures=2,
input_dist=spn.DenseSPNGenerator.InputDist.MIXTURE,
num_input_mixtures=None,
weight_initializer=tf.initializers.random_uniform(0.0, 1.0))
root = model.build(num_vars=6, num_vals=2)
self.generic_model_test("1class", root, model.sample_ivs, None)
def test_discretedense_3class_externallatent_indicators(self):
model = spn.DiscreteDenseModel(
num_classes=3,
num_decomps=2,
num_subsets=3,
num_mixtures=2,
input_dist=spn.DenseSPNGenerator.InputDist.MIXTURE,
num_input_mixtures=None,
weight_initializer=tf.initializers.random_uniform(0.0, 1.0))
sample_latent_indicators = spn.IndicatorLeaf(num_vars=6, num_vals=2)
class_latent_indicators = spn.IndicatorLeaf(num_vars=1, num_vals=3)
root = model.build(sample_latent_indicators,
class_input=class_latent_indicators)
self.generic_model_test("3class", root, sample_latent_indicators,
class_latent_indicators)
def test_discretedense_saving_1class_externalivs(self):
model1 = spn.DiscreteDenseModel(
num_classes=1,
num_decomps=2,
num_subsets=3,
num_mixtures=2,
input_dist=spn.DenseSPNGenerator.InputDist.MIXTURE,
num_input_mixtures=None,
weight_init_value=spn.ValueType.RANDOM_UNIFORM(0, 1))
sample_ivs1 = spn.IVs(num_vars=6, num_vals=2)
model1.build(sample_ivs1)
init1 = spn.initialize_weights(model1.root)
feed_samples = np.array(list(itertools.product(range(2), repeat=6)))
with tf.Session() as sess:
# Initialize
init1.run()
# Save
path = self.out_path(self.cid() + ".spn")
model1.save_to_json(path,
pretty=True,
save_param_vals=True,
sess=sess)
# Reset graph
tf.reset_default_graph()
with tf.Session() as sess:
# Load
model2 = spn.Model.load_from_json(path,
load_param_vals=True,
sess=sess)
self.assertIs(type(model2), spn.DiscreteDenseModel)
val_marginal2 = model2.root.get_value(
inference_type=spn.InferenceType.MARGINAL)
# Check model after loading
self.assertTrue(model2.root.is_valid())
out_marginal2 = sess.run(
val_marginal2,
feed_dict={model2.sample_inputs[0].node: feed_samples})
self.assertAlmostEqual(out_marginal2.sum(), 1.0, places=6)
# Writing graph
self.write_tf_graph(sess, self.sid(), self.cid())
