# tensorplay.ao.pruning.l1_unstructured

Source: https://www.tensorplay.cn/docs/generated/tensorplay.ao.pruning.l1_unstructured.html

```python
tensorplay.ao.pruning.l1_unstructured(module: Module, name: str, amount: int | float, importance_scores: Tensor | None = None) → Module
```

Prune module[name] by removing the units with the smallest
magnitudes.

Removes amount (currently unpruned) units ranked by absolute value.
Modifies the module in place (and also returns it) by:

- adding a named buffer called name + '_mask' holding the binary mask applied to the parameter name;

- replacing the parameter name by its masked version, while the original (unmasked) values are stored in a new parameter named name + '_orig'.

Parameters:

- module – module containing the tensor to prune.

- name – parameter name within module on which pruning acts.

- amount – quantity of units to prune. A float in [0, 1] denotes the fraction of units to prune; an int denotes the absolute number of units to prune.

- importance_scores – tensor of importance scores with the same shape as the parameter; each entry ranks the corresponding element of the parameter. When unspecified, the parameter itself is used.

Returns:

The modified (i.e. pruned) module.

Examples

```
>>> # xdoctest: +SKIP
>>> m = l1_unstructured(nn.Linear(2, 3), "weight", amount=0.2)
>>> list(m.state_dict().keys())
['bias', 'weight_orig', 'weight_mask']
```