Training

Training utilities for neural networks.

nabla.nn.utils.training.create_dataset(batch_size, input_dim, seed=None)

Create a simple random dataset for testing.

Parameters:

• batch_size (int) – Number of samples

• input_dim (int) – Input dimension

• seed (int | None) – Random seed for reproducibility

Returns:

Tuple of (inputs, targets)

Return type:

tuple[Array, Array]

nabla.nn.utils.training.create_sin_dataset(batch_size=256, sin_periods=8)

Create the complex 8-period sin dataset from mlp_train_jit.py.

Parameters:

• batch_size (int) – Number of samples to generate

• sin_periods (int) – Number of sin periods in [0, 1] interval

Returns:

Tuple of (x, targets) where targets are sin function values

Return type:

tuple[Array, Array]

nabla.nn.utils.training.compute_accuracy(predictions, targets, threshold=0.5)

Compute classification accuracy.

Parameters:

• predictions (Array) – Model predictions

• targets (Array) – True labels

• threshold (float) – Classification threshold

Returns:

Accuracy as a float between 0 and 1

Return type:

float

nabla.nn.utils.training.compute_correlation(predictions, targets)

Compute Pearson correlation coefficient.

Parameters:

• predictions (Array) – Model predictions

• targets (Array) – True values

Returns:

Correlation coefficient as a float

Return type:

float