A framework based on TensorFlow, developed and maintained by ProjectAGI, for building a selective memory system based on convolutional, hierarchical sparse autoencoder-like components.

The objective of this project is to create general purpose Machine Learning algorithms inspired by neuroscience. Our philosophy is to build components that can learn in an unsupervised manner and rely on local (as opposed to long range) error signals.

Terminology:

• A dataset is a representation of a dataset using the tf.data.Dataset APIs
• A component is a TensorFlow graph component - i.e. a fragment of the complete graph. Components may be nested or composited to build a complete system.
• A workflow is the tooling required to train and evaluate components (e.g. training/eval loops, feed_dict building)

The framework comes built-in with multiple datasets (e.g. MNIST, Omniglot, smallNORB), multiple components (e.g. convolutional k-sparse autoencoder), and the base workflow to train and test components. These foundations can be built upon in your own custom projects through class inheritance.

• Python 3.6+
• TensorFlow 1.10

We recommend setting up your development environment using Anaconda.

Clone the repository using git clone https://github.com/ProjectAGI/pagi.git, then run pip install -e . to install the pagi package and its dependencies.

Documentation can be found in the documentation directory. A template for the experiment definition file can be found in definitions/default-template.json.

Get comfy, open your favourite Python editor and away you go.

• The entry point is the pagi script, available upon installation
• It instantiates a Workflow, Component and Dataset, and then runs an experiment
• A Workflow describes the setup and conditions of an experiment. The algorithm being tested is represented by the Component, and Datasets are self-explanatory
• The base workflow has a run loop that consists of optionally ‘train’ and ‘test’ steps
• The default is that ‘train’ trains an unsupervised component inheriting Component. The component transforms the input data into a new feature set that is tested in ‘test’ with a supervised classifier using Harness

• pagi provides a number options for how an experiment is run, including setting options for the Workflow and for the Component
• You can set these options via the command line, or using a definitions.json file
• We recommend the definitions.json as most convenient (a template is provided in the definitions folder)
• Default parameters/hyperparameters are defined in the Component and Workflow classes respectively
• There are other default experiment parameters defined in pagi

• Component: A subset of the complete Tensorflow compute graph that acts as an algorithm to test.
• Workflow: Creates and manages the conditions for training and testing a Component.
• Dual: Tensorflow graphs cannot have cycles (loops). Many of our algorithms require recurrent, loopy calculations. To break the loop, we use placeholders to reload updated version of these tensors. The Dual pattern conveniently manages the various forms of a Tensor in its on-graph and off-graph lifecycle, namely: off-graph-numpy-tensor --> placeholder --> on-graph-op --> off-graph numpy result
• Batch types: Tensorflow updates a set of graph vertices in batches. Typically, to update the algorithm completely, many graph ops, with dependent placeholder input and outputs, and summaries - must all be updated simultaneously. The batch-types concept provides a way for components to internalize the complexity of these updates, providing a simpler external interface of supported batch types to choose from.

We encourage people to build and experiment with pagi. We are happy to receive pull requests to continue to improve and expand the codebase.

Our Style Guide can be found here at ./documentation/style-guide

Unit tests can be executed in multiple ways:

1. Execute an individual test: python -m components.autoencoder_component_test
2. Execute all tests in a specific directory: python -m unittest discover -s ./components -p '*_test.py'
3. Execute all the tests in the project: python -m unittest discover -p '*_test.py'