Tutorial 1a: Building the SPN Graph Using Single Op Nodes

The simplest and most readable way to make small SPNs is through 'single op' nodes like Sum or Product.

import libspn as spn
import tensorflow as tf

Build the SPN

indicator_leaves = spn.IndicatorLeaf(
    num_vars=2, num_vals=2, name="indicator_x")

# Connect first two sums to indicators of first variable
sum_11 = spn.Sum((indicator_leaves, [0,1]), name="sum_11")
sum_12 = spn.Sum((indicator_leaves, [0,1]), name="sum_12")

# Connect another two sums to indicators of the second variable
sum_21 = spn.Sum((indicator_leaves, [2,3]), name="sum_21")
sum_22 = spn.Sum((indicator_leaves, [2,3]), name="sum_22")

# Connect three product nodes
prod_1 = spn.Product(sum_11, sum_21, name="prod_1")
prod_2 = spn.Product(sum_11, sum_22, name="prod_2")
prod_3 = spn.Product(sum_12, sum_22, name="prod_3")

# Connect a root sum
root = spn.Sum(prod_1, prod_2, prod_3, name="root")

# Connect a latent indicator
indicator_y = root.generate_latent_indicators(name="indicator_y") # Can be added manually

# Generate weights
spn.generate_weights(root, initializer=tf.initializers.random_uniform()) # Can be added manually

[WARNING] [tensorflow:__getattr__] From /home/jos/spn/libspn/libspn/graph/node.py:40: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

[WARNING] [tensorflow:__getattr__] From /home/jos/spn/libspn/libspn/graph/leaf/indicator.py:63: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

Inspect

# Inspect
print(root.get_num_nodes())
print(root.get_scope())
print(root.is_valid())

15
[Scope({indicator_x:1, indicator_y:0, indicator_x:0})]
True

Visualize the SPN Graph

The visualization below uses graphviz. Depending on your setup (e.g. jupyter lab vs. jupyter notebook) this might fail to show. At least Chrome + jupyter notebook seems to work.

# Visualize SPN graph
spn.display_spn_graph(root)