Autoencoders

BehaveNet uses convolutional autoencoders to perform nonlinear dimensionality reduction on behavioral videos. The steps below demonstrate how to fit these models on an arbitrary dataset.

Within the behavenet package there is a directory named configs, which contains example config files. First copy the following config files to the .behavenet directory that was automatically created in your home directory: ae_compute.json, ae_model.json, and ae_training.json. You can then update the hyperparameters in these files in a text editor.

To fit a single model with the default CAE BehaveNet architecture (details in paper), edit the ae_model.json file to look like the following:

{
"experiment_name": "ae-example",
"model_type": "conv",
"n_ae_latents": 12,
"l2_reg": 0.0,
"rng_seed_model": 0,
"fit_sess_io_layers": false,
"ae_arch_json": null,
"model_class": "ae"
}

Then to fit the model, cd to the behavenet directory in the terminal and run

$: python behavenet/fitting/ae_grid_search.py --data_config /user_home/.behavenet/musall_vistrained_params.json --model_config /user_home/.behavenet/ae_model.json --training_config /user_home/.behavenet/ae_training.json --compute_config /user_home/.behavenet/ae_compute.json

where ~/.behavenet/musall_vistrained_params.json can be replaced by any dataset config file created by running the behavenet.add_dataset() function (example here).

Performing a search over multiple latents is as simple as editing the ae_model.json as below and rerunning the same command.

{
"experiment_name": "latent-search",
"model_type": "conv",
"n_ae_latents": [6, 9, 12, 15],
"l2_reg": 0.0,
"rng_seed_model": 0,
"fit_sess_io_layers": false,
"ae_arch_json": null,
"model_class": "ae"
}

Training an AE can be slow: you can speed up the training by parallelizing over multiple gpus. To do this, just specify n_parallel_gpus to be the number of gpus you wish to use per model. The code will split up the gpus specified in gpus_viz into groups of size n_parallel_gpus (or less if there are leftover gpus) and run the models accordingly.