Tutorial 1b: Building the SPN Graph Using Multi-Op Nodes

We can also build SPNs using bigger building blocks: e.g. ParallelSums which compute multiple sums with identical child inputs or PermuteProducts which compute all permutations of children with disjoint scopes.

In [1]:
import libspn as spn
import tensorflow as tf

Build the SPN

In [2]:
indicator_leaves = spn.IndicatorLeaf(
    num_vars=2, num_vals=2, name="indicator_x")

# Connect first two sums to indicators of first variable
sums_1 = spn.ParallelSums((indicator_leaves, [0,1]), num_sums=2, name="sums_1")
# Connect another two sums to indicators of second variable
sums_2 = spn.ParallelSums((indicator_leaves, [2,3]), num_sums=2, name="sums_2")

# Connect 2 * 2 == 4 product nodes
prods_1 = spn.PermuteProducts(sums_1, sums_2, name="prod_1")

# Connect a root sum
root = spn.Sum(prods_1, 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

In [3]:
# Inspect
print(root.get_num_nodes())
print(root.get_scope())
print(root.is_valid())

9
[Scope({indicator_x:0, indicator_x:1, indicator_y: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.

In [4]:
# Visualize SPN graph
spn.display_spn_graph(root)