Neural network models

BaseNeuralNet is the root of all PyTorch models in this package. It wraps torch.nn.Module with higher-level training, prediction and serialization helpers — set_optimizer, train_on, predict, set_data, save_model / load_model — so that subclasses only need to implement forward.

MultiLayerPerceptron is the feed-forward specialization: a configurable stack of Linear → Dropout → Activation blocks, best suited for tabular or sliding-window features (technical indicators, volatility signals). For time-ordered sequences, prefer the recurrent architectures described in Recurrent neural networks.

Inheritance

BaseNeuralNet → MultiLayerPerceptron

BaseNeuralNet → _RecurrentBase → RecurrentNeuralNetwork / GRUCell / LSTMCell (see Recurrent neural networks)

Classes

_base.BaseNeuralNet()	Base object for neural network model with PyTorch.
mlp.MultiLayerPerceptron(X, y[, layers, ...])	Neural network with MultiLayer Perceptron architecture.