mednet.models.classify.mlp

Multi-layer perceptron model for multi-class classification.

Classes

MultiLayerPerceptron([loss_type, ...])

Multi-layer perceptron model for multi-class classification.
class mednet.models.classify.mlp.MultiLayerPerceptron(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, hidden_size=10)[source]

Bases: Model

Multi-layer perceptron model for multi-class classification.

This implementation has a variable number of inputs, one single hidden layer with a variable number of hidden neurons, and can be used for binary or 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.

  • hidden_size (int) – The number of neurons on the single hidden layer.

forward(x)[source]

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