Gent

A library for the easy construction, training, and running of artifical neural networks, with the ability to serialize and load arbitrary network architecture definitions and pre-trained weights. Built on top of pytorch.

This is very much a work in progress and is being used for currently active research; expect the interface to change drastically!

Eventually, this library will be "An algorithm for Genetic Evolution of Network Topologies".

Using Gent

Only 4 steps are needed to make and run a neural network:

import Gent as ann

gen = ann.NNGenome(geneseq)
network = ann.NeuralNetwork(gen.allGenes, connections, inputs, outputs) 
runner = ann.NetworkRunner(network, xtr, ytr)
runner.train()

Where the arguments are:

• geneseq: A list of dicts/sequences to define the layers of a network where the required/allowed keys are defined in ./NNGenome.
• connections ({int: ([int], [int])}): Dict with key= gid, value= tuple of lists ([in], [out]) of gids, for each corresponding gene in the genes list, giving the input and output for that gid key
• inputs (optional): Which layers the data is expected to go to.
• outputs (optional): List of output. Loss during training is applied to these.
• xtr, ytr: Data that is of the type torch.Tensor; can be lists of Tensors or a single Tensor.

See ./NetworkRunner for potential args to give to the runner.

Files saved by Gent:

• ~/model: The torch-serialized weights for the network created by calling network.state_dict(), which outputs all saved gradients and values in registered Parameters.
• ~/stats: Test-level data; a pickled dict where {"loss"= list of loss per epoch, "err" = list of error per epoch}
• ~/stats_per_epoch: Train-level data; a pickled list, length of number of epochs, where each value is a dict of {"loss"= list of loss per batch in epoch, "avg_err"/"perplexity" = list of that type of err per batch in epoch}

Other files saved in the directory:

• ~/model_def.json: Saved by ./examples/ModelDefs.py, a json serialized list of 4 values: geneseq, connections, inputs, outputs. To properly load call ModelDefs.from_json(model_name, *json.loads(file.read())).
• ~/test_guids.json: List of the guids of the matches used in the most recent test data.

Pytorch Version

is 0.2.0, more specifically 0.2.0_1