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andrei-stoian-zama avatar andrei-stoian-zama commented on August 23, 2024 1

The code given restricts the maximum number of "active" neuron connections to max_non_zero. An active neuron connection is a non-zero weight.

With unstructured pruning all neurons are still active, but some of their connections are removed.

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andrei-stoian-zama avatar andrei-stoian-zama commented on August 23, 2024

You can check out the step-by-step guide in the documentation!

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maxwellgodv avatar maxwellgodv commented on August 23, 2024

Thanks,and I want to know what ’Active Neurons‘ represent?Is it the same as the max_non_zero parameter in the prune function?

  def prune(self, max_non_zero):
        # Linear layer weight has dimensions NumOutputs x NumInputs
        for name, layer in self.named_modules():
            if isinstance(layer, nn.Linear):
                print(name, layer)
                num_zero_weights = (layer.weight.shape[1] - max_non_zero) * layer.weight.shape[0]
                if num_zero_weights <= 0:
                    continue
                print(f"Pruning layer {name} factor {num_zero_weights}")
                prune.l1_unstructured(layer, "weight", amount=num_zero_weights)
                self.pruned_layers.add(name)

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