behavenet.fitting

Model fitting documentation.

behavenet.fitting.eval Module

Utility functions for evaluating model fits.

Functions

export_latents(data_generator, model[, filename])	Export predicted latents using an already initialized data_generator and model.
export_predictions(data_generator, model[, …])	Export decoder predictions using an already initialized data_generator and model.
export_states(hparams, data_generator, model)	Export predicted latents using an already initialized data_generator and model.
export_train_plots(hparams, dtype[, …])	Export plot with MSE/LL as a function of training epochs.
get_reconstruction(model, inputs[, dataset, …])	Reconstruct an image from either image or latent inputs.
get_test_metric(hparams, model_version[, …])	Calculate a single R2 value across all test batches for a decoder.

behavenet.fitting.losses Module

Custom losses for PyTorch models.

Functions

mse(y_pred, y_true[, masks])	Compute mean square error (MSE) loss with masks.
gaussian_ll(y_pred, y_mean[, masks, std])	Compute multivariate Gaussian log-likelihood with a fixed diagonal noise covariance matrix.
gaussian_ll_to_mse(ll, n_dims[, …])	Convert a Gaussian log-likelihood term to MSE by removing constants and swapping variances.
kl_div_to_std_normal(mu, logvar)	Compute element-wise KL(q(z) || N(0, 1)) where q(z) is a normal parameterized by mu, logvar.
index_code_mi(z, mu, logvar)	Estimate index code mutual information in a batch.
total_correlation(z, mu, logvar)	Estimate total correlation in a batch.
dimension_wise_kl_to_std_normal(z, mu, logvar)	Estimate dimensionwise KL divergence to standard normal in a batch.
decomposed_kl(z, mu, logvar)	Decompose KL term in VAE loss.
subspace_overlap(A, B[, C])	Compute inner product between subspaces defined by matrices A and B.
triplet_loss(triplet_loss_obj, z, datasets)	Compute triplet loss to learn separated embedding space.

behavenet.fitting.training Module

Functions and classes for fitting PyTorch models with stochastic gradient descent.

Functions

fit(hparams, model, data_generator, exp[, …])

Fit pytorch models with stochastic gradient descent and early stopping.

Classes

Logger([n_datasets])	Base method for logging loss metrics.
EarlyStopping([patience, min_epochs, delta])	Stop training when a monitored quantity has stopped improving.

behavenet.fitting.utils Module

Utility functions for managing model paths and the hparams dict.

Functions

get_subdirs(path)	Get all first-level subdirectories in a given path (no recursion).
get_session_dir(hparams[, session_source])	Get session-level directory for saving model outputs from hparams dict.
get_expt_dir(hparams[, model_class, …])	Get output directories associated with a particular model class/type/testtube expt name.
read_session_info_from_csv(session_file)	Read csv file that contains lab/expt/animal/session info.
export_session_info_to_csv(session_dir, ids_list)	Export list of sessions to csv file.
contains_session(session_dir, session_id)	Determine if session defined by session_id dict is in the multi-session session_dir.
find_session_dirs(hparams)	Find all session dirs (single- and multi-session) that contain the session in hparams.
experiment_exists(hparams[, which_version])	Search testtube versions to find if experiment with the same hyperparameters has been fit.
get_model_params(hparams)	Returns dict containing all params considered essential for defining a model in that class.
export_hparams(hparams, exp)	Export hyperparameter dictionary.
get_lab_example(hparams, lab, expt)	Helper function to load data-specific hyperparameters and update hparams.
get_region_dir(hparams)	Return brain region string that combines region name and inclusion info.
create_tt_experiment(hparams)	Create test-tube experiment for logging training and storing models.
get_best_model_version(expt_dir[, measure, …])	Get best model version from a test tube experiment.
get_best_model_and_data(hparams[, Model, …])	Load the best model (and data) defined by hparams out of all available test-tube versions.