lanfactory 0.5.1

Package with convenience functions to train LANs

LANfactory

Lightweight python package to help with training LANs (Likelihood approximation networks).

Please find the original documentation here.

Quick Start

The LANfactory package is a light-weight convenience package for training likelihood approximation networks (LANs) in torch (or keras), starting from supplied training data.

LANs, although more general in potential scope of applications, were conceived in the context of sequential sampling modeling to account for cognitive processes giving rise to choice and reaction time data in n-alternative forced choice experiments commonly encountered in the cognitive sciences.

For a basic tutorial on how to use the LANfactory package, please refer to the basic tutorial notebook.

In this quick tutorial we will use the ssms package to generate our training data using such a sequential sampling model (SSM). The use is in no way bound to utilize the ssms package.

Install

To install the ssms package type,

pip install ssm-simulators

To install the LANfactory package type,

pip install lanfactory

Necessary dependency should be installed automatically in the process.

Basic Tutorial

Check the basic tutorial here.

TorchMLP to ONNX Converter

Once you have trained your model, you can convert it to the ONNX format using the transform_onnx.py script.

The transform_onnx.py script converts a TorchMLP model to the ONNX format. It takes a network configuration file (in pickle format), a state dictionary file (Torch model weights), the size of the input tensor, and the desired output ONNX file path.

Usage

python onnx/transform_onnx.py <network_config_file> <state_dict_file> <input_shape> <output_onnx_file>

Replace the placeholders with the appropriate values:

• <network_config_file>: Path to the pickle file containing the network configuration.
• <state_dict_file>: Path to the file containing the state dictionary of the model.
• <input_shape>: The size of the input tensor for the model (integer).
• <output_onnx_file>: Path to the output ONNX file.

For example:

python onnx/transform_onnx.py '0d9f0e94175b11eca9e93cecef057438_lca_no_bias_4_torch__network_config.pickle' '0d9f0e94175b11eca9e93cecef057438_lca_no_bias_4_torch_state_dict.pt' 11 'lca_no_bias_4_torch.onnx'

This onnx file can be used directly with the HSSM package.

We hope this package may be helpful in case you attempt to train LANs for your own research.

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