hyrax.models.hsc_autoencoder

hyrax.models.hsc_autoencoder#

Classes#

HSCAutoencoder

This autoencoder is designed to work with datasets that are prepared with Hyrax's HSC Data Set class.

Module Contents#

class HSCAutoencoder(config, data_sample=None)[source]#

Bases: torch.nn.Module

This autoencoder is designed to work with datasets that are prepared with Hyrax’s HSC Data Set class.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

encoder[source]#

decoder[source]#

config[source]#

forward(x)[source]#

train_batch(batch)[source]#

This function contains the logic for a single training step. i.e. the contents of the inner loop of a ML training process.

Parameters:: batch (tuple) – A tuple containing the input data for the current batch, possibly with labels that are ignored.
Returns:: Current loss value – Dictionary containing the loss value for the current batch.
Return type:: dict

validate_batch(batch)[source]#

This function contains the logic for a single validation step that will process a single batch of data. i.e. the contents of the inner loop of a ML validation process.

Parameters:: batch (tuple) – A tuple containing the input data for the current batch, possibly with labels that are ignored.
Returns:: Current loss value – Dictionary containing the loss value for the current batch.
Return type:: dict

test_batch(batch)[source]#

This function contains the logic for a single testing step that will process a single batch of data. i.e. the contents of the inner loop of a ML testing process. In this case, it is identical to validate_batch.

Parameters:: batch (tuple) – A tuple containing the input data for the current batch, possibly with labels that are ignored.
Returns:: Current loss value – Dictionary containing the loss value for the current batch.
Return type:: dict

infer_batch(batch)[source]#

This function contains the logic for a single inference step that will process a single batch of data. i.e. the contents of the inner loop of a ML inference process.

Parameters:: batch (tuple) – A tuple containing the input data for the current batch, possibly with labels that are ignored.
Returns:: Reconstructed outputs – The reconstructed outputs from the autoencoder.
Return type:: torch.Tensor