# 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.

## Contents¶

## Documentation¶

The documentation of the library is hosted on ReadTheDocs.

## 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 for generative problems

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`

nodeContinuous inputs through

`NormalLeaf`

,`CauchyLeaf`

or`LaplaceLeaf`

. Each of these distributions support both univariate as well as*multivariate*inputs.

## Examples / Tutorials¶

**Image Classification**: A Deep Generalized Convolutional Sum-Product Network (DGC-SPN).**Image Completion**: A Deep Generalized Convolutional Sum-Product Network (DGC-SPN).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
_________________________________________________________________
```

## TODOs¶

Structure learning

Advanced regularization e.g. pruning or auxiliary losses on weight accumulators