## LibSPN Keras

LibSPN Keras is a library for constructing and training Sum-Product Networks. By leveraging the Keras framework with a TensorFlow backend, it offers both ease-of-use and scalability. Whereas the previously available libspn focused on scalability, libspn-keras offers scalability and a straightforward Keras-compatible interface.

## What are SPNs?

Sum-Product Networks (SPNs) are a probabilistic deep architecture with solid theoretical

foundations, which demonstrated state-of-the-art performance in several domains. Yet, surprisingly,

there are no mature, general-purpose SPN implementations that would serve as a platform for the

community of machine learning researchers centered around SPNs. LibSPN Keras is a new

general-purpose Python library, which aims to become such a platform. The library is designed to

make it straightforward and effortless to apply various SPN architectures to large-scale datasets

and problems. The library achieves scalability and efficiency, thanks to a tight coupling with

TensorFlow and Keras, two frameworks already in use by a large community of researchers and

developers in multiple domains.

## Dependencies

Currently, LibSPN Keras is tested with `tensorflow>=2.0`

and `tensorflow-probability>=0.8.0`

.

## Installation

```
pip install libspn-keras
```

## Note on stability of the repo

Currently, the repo is in an alpha state. Hence, one can expect some sporadic breaking changes.

## Feature Overview

- Gradient based training for generative and discriminative problems
- Hard EM training for generative problems
- Hard EM training with unweighted weights for generative problems
- Soft EM training (experimental) for generative problems
- Deep Generalized Convolutional Sum-Product Networks
- SPNs with arbitrary decompositions
- Fully compatible with Keras and TensorFlow 2.0
- Input dropout
- Sum child dropout
- Image completion
- Model saving
- Discrete inputs through an
`IndicatorLeaf`

node - Continuous inputs through
`NormalLeaf`

,`CauchyLeaf`

or`LaplaceLeaf`

. Each of these distributions support both

univariate as well as*multivariate*inputs.

## Examples / Tutorials

**Benchmark**:`libspn-keras`

and Einsum Networks.**Image Classification**: A Deep Generalized Convolutional Sum-Product Network (DGC-SPN).**Image Completion**: A Deep Generalized Convolutional Sum-Product Network (DGC-SPN).**Understanding region SPNs**- More to come, and if you would like to see a tutorial on anything in particular

please raise an issue!

Check out the way we can build complex DGC-SPNs in a layer-wise fashion:

```
import libspn_keras as spnk
from tensorflow import keras
spnk.set_default_sum_op(spnk.SumOpGradBackprop())
spnk.set_default_accumulator_initializer(
keras.initializers.TruncatedNormal(stddev=0.5, mean=1.0)
)
sum_product_network = keras.Sequential([
spnk.layers.NormalizeStandardScore(input_shape=(28, 28, 1)),
spnk.layers.NormalLeaf(
num_components=16,
location_trainable=True,
location_initializer=keras.initializers.TruncatedNormal(
stddev=1.0, mean=0.0)
),
# Non-overlapping products
spnk.layers.Conv2DProduct(
depthwise=True,
strides=[2, 2],
dilations=[1, 1],
kernel_size=[2, 2],
padding='valid'
),
spnk.layers.Local2DSum(num_sums=16),
# Non-overlapping products
spnk.layers.Conv2DProduct(
depthwise=True,
strides=[2, 2],
dilations=[1, 1],
kernel_size=[2, 2],
padding='valid'
),
spnk.layers.Local2DSum(num_sums=32),
# Overlapping products, starting at dilations [1, 1]
spnk.layers.Conv2DProduct(
depthwise=True,
strides=[1, 1],
dilations=[1, 1],
kernel_size=[2, 2],
padding='full'
),
spnk.layers.Local2DSum(num_sums=32),
# Overlapping products, with dilations [2, 2] and full padding
spnk.layers.Conv2DProduct(
depthwise=True,
strides=[1, 1],
dilations=[2, 2],
kernel_size=[2, 2],
padding='full'
),
spnk.layers.Local2DSum(num_sums=64),
# Overlapping products, with dilations [2, 2] and full padding
spnk.layers.Conv2DProduct(
depthwise=True,
strides=[1, 1],
dilations=[4, 4],
kernel_size=[2, 2],
padding='full'
),
spnk.layers.Local2DSum(num_sums=64),
# Overlapping products, with dilations [2, 2] and 'final' padding to combine
# all scopes
spnk.layers.Conv2DProduct(
depthwise=True,
strides=[1, 1],
dilations=[8, 8],
kernel_size=[2, 2],
padding='final'
),
spnk.layers.SpatialToRegions(),
# Class roots
spnk.layers.DenseSum(num_sums=10),
spnk.layers.RootSum(return_weighted_child_logits=True)
])
sum_product_network.summary(line_length=100)
```

Which produces:

```
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
normal_leaf (NormalLeaf) (None, 28, 28, 16) 25088
_________________________________________________________________
conv2d_product (Conv2DProduc (None, 14, 14, 16) 4
_________________________________________________________________
local2d_sum (Local2DSum) (None, 14, 14, 16) 50176
_________________________________________________________________
conv2d_product_1 (Conv2DProd (None, 7, 7, 16) 4
_________________________________________________________________
local2d_sum_1 (Local2DSum) (None, 7, 7, 32) 25088
_________________________________________________________________
conv2d_product_2 (Conv2DProd (None, 8, 8, 32) 4
_________________________________________________________________
local2d_sum_2 (Local2DSum) (None, 8, 8, 32) 65536
_________________________________________________________________
conv2d_product_3 (Conv2DProd (None, 10, 10, 32) 4
_________________________________________________________________
local2d_sum_3 (Local2DSum) (None, 10, 10, 64) 204800
_________________________________________________________________
conv2d_product_4 (Conv2DProd (None, 14, 14, 64) 4
_________________________________________________________________
local2d_sum_4 (Local2DSum) (None, 14, 14, 64) 802816
_________________________________________________________________
conv2d_product_5 (Conv2DProd (None, 8, 8, 64) 4
_________________________________________________________________
spatial_to_regions (SpatialT (None, 1, 1, 4096) 0
_________________________________________________________________
dense_sum (DenseSum) (None, 1, 1, 10) 40960
_________________________________________________________________
root_sum (RootSum) (None, 10) 10
=================================================================
Total params: 1,214,498
Trainable params: 1,201,930
Non-trainable params: 12,568
_________________________________________________________________
```