Classifier

class nnero.classifier.Classifier(*, n_input: int = 16, n_hidden_features: int = 32, n_hidden_layers: int = 4, model: Module | None = None, name: str | None = None, dataset: DataSet | None = None)[source]

Bases: NeuralNetwork

Daughter class of NeuralNetwork specialised for classifier.

Parameters:

  • model (torch.nn.Module | None) – If not None, the model that will be used for classifier. Otherwise, a new model is constructed from n_input, n_hidden_features and n_hidden_layers. Default is None.

  • n_input (int, optional) – Number of input on the neural network (corresponds to the number of parameters). Default is 16.

  • n_hidden_features (int, optional) – Number of hidden features per layer. Default is 32.

  • n_hidden_layers (int, optional) – Number of layers. Default is 4.

  • name (str | None) – Name of the neural network. If None, automatically set to DefaultClassifier. Default is None.

  • dataset (Dataset | None) – Dataset on which the model will be trained. If provided, gets n_input from the data and overrides the user input value.

- name

the name of the model

Type:

str

forward(x: Tensor) → Tensor[source]

Forward evaluation of the model.

Parameters:

x (torch.Tensor) – Input features.

classmethod load(path: str | None = None) → Self[source]

Loads a classifier.

Parameters:

path (str | None) – Path to the saved files containing the classifier data. If None automatically fetch the DefaultClassifier.

Return type:

Classifier

test(dataset: DataSet | None = None, x_test: ndarray | None = None, y_test: ndarray | None = None) → tuple[ndarray, ndarray, ndarray][source]

Test the efficiency of the classifier.

Parameters:

  • dataset (DataSet | None) – DataSet containing the training partition and the test partition.

  • x_test (np.ndarray)

  • y_test (np.ndarray)

Returns:

y_pred, y_test, and array of true if rightly classifier, false otherwise

Return type:

tuple(np.ndarray, np.ndarray, np.ndarray)

Raises:

ValueError – Either the dataset or both x_test and y_test must be provided.

validate(dataset: DataSet) → tuple[ndarray, ndarray, ndarray][source]

Validate the efficiency of the classifier.

Parameters:

dataset (DataSet) – DataSet containing the training partition and the test partition.

Returns:

y_pred, y_test, and array of true if rightly classifier, false otherwise

Return type:

tuple(np.ndarray, np.ndarray, np.ndarray)

nnero.classifier.train_classifier(model: Classifier, dataset: DataSet, optimizer: Optimizer, *, epochs: int = 50, learning_rate: float = 0.001, verbose: bool = True, batch_size: int = 64, x_train: ndarray | None = None, y_train: ndarray | None = None, x_valid: ndarray | None = None, y_valid: ndarray | None = None, **kwargs) → None[source]

Trains a given classifier.

Parameters:

  • model (Classifier) – Classifier model to train.

  • dataset (DataSet) – Dataset on which to train the classifier.

  • optimizer (torch.optim.Optimizer) – Optimizer used for training.

  • epochs (int, optional) – Number of epochs, by default 50.

  • learning_rate (float, optional) – Learning rate for training, by default 1e-3.

  • verbose (bool, optional) – If true, outputs a summary of the losses at each epoch, by default True.

  • batch_size (int, optional) – Size of the training batches, by default 64.