bayesflow.helper_functions module

bayesflow.helper_functions module#

bayesflow.helper_functions.check_tensor_sanity(tensor, logger)[source]#

Tests for the presence of NaNs and Infs in a tensor.

bayesflow.helper_functions.merge_left_into_right(left_dict, right_dict)[source]#

Function to merge nested dict left_dict into nested dict right_dict.

bayesflow.helper_functions.build_meta_dict(user_dict: dict, default_setting: MetaDictSetting) → dict[source]#

Integrates a user-defined dictionary into a default dictionary.

Takes a user-defined dictionary and a default dictionary.

  1. Scan the user_dict for violations by unspecified mandatory fields.

  2. Merge user_dict entries into the default_dict. Considers nested dict structure.

Parameters:
user_dictdict

The user’s dictionary

default_settingMetaDictSetting

The specified default setting with attributes:

  • meta_dict: dictionary with default values.

  • mandatory_fields: list(str) keys that need to be specified by the user_dict

Returns:
merged_dict: dict

Merged dictionary.

bayesflow.helper_functions.extract_current_lr(optimizer)[source]#

Extracts current learning rate from optimizer.

Parameters:
optimizerinstance of subclass of tf.keras.optimizers.Optimizer

Optimizer to extract the learning rate from

Returns:
current_lrnp.float or NoneType

Current learning rate, or None if it can’t be determined

bayesflow.helper_functions.format_loss_string(ep, it, loss, avg_dict, slope=None, lr=None, ep_str='Epoch', it_str='Iter', scalar_loss_str='Loss')[source]#

Prepare loss string for displaying on progress bar.

bayesflow.helper_functions.loss_to_string(ep, loss, ep_str='Epoch', scalar_loss_str='Loss')[source]#

Converts output from an amortizer into a string. For instance, if a dict is provided, it will be converted as, e.g.,: dictionary = {k1: v1, k2: v2} -> ‘k1: v1, k2: v2’

bayesflow.helper_functions.backprop_step(input_dict, amortizer, optimizer, **kwargs)[source]#

Computes the loss of the provided amortizer given an input dictionary and applies gradients.

Parameters:
input_dictdict

The configured output of the generative model

amortizertf.keras.Model

The custom amortizer. Needs to implement a compute_loss method.

optimizertf.keras.optimizers.Optimizer

The optimizer used to update the amortizer’s parameters.

**kwargsdict

Optional keyword arguments passed to the network’s compute_loss method

Returns:
lossdict

The outputs of the compute_loss() method of the amortizer comprising all loss components, such as divergences or regularization.

bayesflow.helper_functions.check_posterior_prior_shapes(post_samples, prior_samples)[source]#

Checks requirements for the shapes of posterior and prior draws as necessitated by most diagnostic functions.

Parameters:
post_samplesnp.ndarray of shape (n_data_sets, n_post_draws, n_params)

The posterior draws obtained from n_data_sets

prior_samplesnp.ndarray of shape (n_data_sets, n_params)

The prior draws obtained for generating n_data_sets

Raises:
ShapeError

If there is a deviation form the expected shapes of post_samples and prior_samples.

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