snntorch.utils

snntorch.utils contains a handful of utility functions for handling datasets.

snntorch.utils.data_subset(dataset, subset, idx=0)[source]

Partition the dataset by a factor of 1/subset without removing access to data and target attributes.

Example:

from snntorch import utils
from torchvision import datasets

data_path = "path/to/data"
subset = 10

#  Download MNIST training set
mnist_train = datasets.MNIST(data_path, train=True, download=True)
print(len(mnist_train))
>>> 60000

#  Reduce size of MNIST training set
utils.data_subset(mnist_train, subset)
print(len(mnist_train))
>>> 6000
Parameters:

  • dataset (torchvision dataset) – Dataset

  • subset (int) – Factor to reduce dataset by

  • idx (int, optional) – Which subset of the train and test sets to index into, defaults to 0

Returns:

Partitioned dataset

Return type:

list of torch.utils.data

snntorch.utils.reset(net)[source]

Check for the types of LIF neurons contained in net. Reset their hidden parameters to zero and detach them from the current computation graph.

snntorch.utils.valid_split(ds_train, ds_val, split, seed=0)[source]

Randomly split a dataset into non-overlapping new datasets of given lengths. Optionally fix the generator for reproducible results. Operates similarly to random_split from torch.utils.data.dataset but retains data and target attributes.

Example

from snntorch import utils
from torchvision import datasets

data_path = "path/to/data"
val_split = 0.1

#  Download MNIST training set into mnist_val and mnist_train
mnist_train = datasets.MNIST(data_path, train=True, download=True)
mnist_val = datasets.MNIST(data_path, train=True, download=True)

print(len(mnist_train))
>>> 60000

print(len(mnist_val))
>>> 60000

#  Validation split
mnist_train, mnist_val = utils.valid_split(mnist_train,
mnist_val, val_split)

print(len(mnist_train))
>>> 54000

print(len(mnist_val))
>>> 6000
Parameters:

  • ds_train (torchvision dataset) – Training set

  • ds_val (torchvision dataset) – Validation set

  • split (Float) – Proportion of samples assigned to the validation set from the training set

  • seed (int, optional) – Fix to generate reproducible results, defaults to 0

Returns:

Randomly split train and validation sets

Return type:

list of torch.utils.data