mednet.models.classify.logistic_regression

Logistic regression model for multi-class classification.

Classes

LogisticRegression([loss_type, ...])

Logistic regression model for multi-class classification.

class mednet.models.classify.logistic_regression.LogisticRegression(loss_type=<class 'torch.nn.modules.loss.BCEWithLogitsLoss'>, loss_arguments={}, optimizer_type=<class 'torch.optim.adam.Adam'>, optimizer_arguments={'lr': 0.01}, num_classes=1, input_size=14)

Bases: Model

Logistic regression model for multi-class classification.

Parameters:

• loss_type (type[Module]) –

  The loss to be used for training and evaluation.

  Warning

  The loss should be set to always return batch averages (as opposed to the batch sum), as our logging system expects it so.

• loss_arguments (dict[str, Any]) – Arguments to the loss.

• optimizer_type (type[Optimizer]) – The type of optimizer to use for training.

• optimizer_arguments (dict[str, Any]) – Arguments to the optimizer after params.

• num_classes (int) – Number of outputs (classes) for this model.

• input_size (int) – The number of inputs this classifer shall process.

forward(x)

Same as torch.nn.Module.forward().

Parameters:

• *args – Whatever you decide to pass into the forward method.

• **kwargs – Keyword arguments are also possible.

Returns:

Your model’s output