Network

class nnero.network.NeuralNetwork(name: str)

Bases: Module

A class wrapping py:class:torch.nn.Module for neural network models

Parameters:

name (str) – name of the neural network

name

the name of the model

Type:

str

metadata

metadata on which the model is trained

Type:

Metadata

partition

partitioning of the data on which the model is trained

Type:

DataPartition

train_loss

1D array training loss for each training epoch

Type:

np.ndarray

valid_loss

1D array validation losses for each training epoch

Type:

np.ndarray

train_accuracy

1D array training accuracy for each training epoch

Type:

np.ndarray

valid_accuracy

1D array validation accuracy for each training epoch

Type:

np.ndarray

info()

load_weights_and_extras(path: str) → None

loads the network weights and extra information

Parameters:

path (str) – path to the network to load

Raises:

ValueError – If not all necessary files exists where path points.

print_structure()

prints the list of parameters in the model

save(path: str = '.', save_partition: bool = True) → None

Save the neural network model in a bunch of files.

Parameters:

• path (str, optional) – path where to save the neural network – default is the current directory “.”

• save_partition (bool, optional) – if save_partition is false the partitioning of the data into train, valid and test is not saved (useless for instance once we have a fully trained model that we just want to use) – default is True

set_check_metadata_and_partition(dataset: DataSet, check_only: bool = False) → None

set and check the medatada and partition attributes

Parameters:

• dataset (DataSet) – dataset to compare or to assign to the object

• check_only (bool, optional) – option to only compare the compatibility – default is False

Raises:

ValueError – if the dataset is incompatible with the current metadata or partition