Step 4: Training — lambeq 0.3.1 [git latest] documentation

In lambeq, all low-level processing that takes place in training is hidden in the training package, which provides convenient high-level abstractions for all important supervised learning scenarios with the toolkit, classical and quantum. More specifically, the training package contains the following high-level/abstract classes and several concrete implementations for them: Dataset: A class that provides functionality for easy management and manipulation of datasets, including batching, shuffling, and preparation based on the selected backend (tket, NumPy, PyTorch). Model: The abstract interface for lambeq models. A model bundles the basic attributes and methods used for training, given a specific backend. It stores the symbols and the corresponding weights, and implements the forward pass of the model. Concrete implementations are the PytorchModel, TketModel, NumpyModel, and PennyLaneModel classes (for more details see Section Choosing a model below). LossFunction: Implementations of

In lambeq, all low-level processing that takes place in training is hidden in the training package, which provides convenient high-level abstractions for all important supervised learning scenarios with the toolkit, classical and quantum. More specifically, the training package contains the following high-level/abstract classes and several concrete implementations for them: Dataset: A class that provides functionality for easy management and manipulation of datasets, including batching, shuffling, and preparation based on the selected backend (tket, NumPy, PyTorch). Model: The abstract interfa