lp_norm¶

dragon.nn.lp_norm(
  inputs,
  axis=- 1,
  end_axis=None,
  p=2,
  epsilon=1e-12,
  reduction='sum',
  **kwargs
)[source]¶

Apply the lp normalization.

The normalization is defined as:

\[y = \frac{x}{\max(\left\|x\right\|_{p}, \epsilon)} \]

axis could be negative:

x = dragon.constant([[1, 2, 3], [4, 5, 6]], 'float32')
# A negative axis is the last-k axis
print(dragon.nn.lp_norm(x, 1))
print(dragon.nn.lp_norm(x, -1))  # Equivalent

More than one axis could be specified to reduce:

# Along the continuous axes: [axis, end_axis]
print(dragon.nn.lp_norm(x, axis=0, end_axis=1))

Parameters:

• inputs (dragon.Tensor) – The tensor \(x\).
• p (int, optional, default=2) – The order of the normalization.
• axis (int, optional, default=-1) – The first axis to reduce.
• end_axis (int, optional) – The last axis to reduce.
• epsilon (float, optional, default=1e-12) – The value to \(\epsilon\).
• reduction ({'sum', 'mean'}, optional) – The reduction method for norm.

Returns:

dragon.Tensor – The output tensor.