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# torch.optim.Optimizer.state_dict

Optimizer.state_dict()

  Returns the state of the optimizer as a `dict`.

  It contains two entries:

  - `state`: a Dict holding current optimization state. Its content

      differs between optimizer classes, but some common characteristics hold. For example, state is saved per parameter, and the parameter itself is NOT saved. `state` is a Dictionary mapping parameter ids to a Dict with state corresponding to each parameter.

  - `param_groups`: a List containing all parameter groups where each

      parameter group is a Dict. Each parameter group contains metadata specific to the optimizer, such as learning rate and weight decay, as well as a List of parameter IDs of the parameters in the group.

  NOTE: The parameter IDs may look like indices but they are just IDs associating state with param_group. When loading from a state_dict, the optimizer will zip the param_group `params` (int IDs) and the optimizer `param_groups` (actual `nn.Parameter` s) in order to match state WITHOUT additional verification.

  A returned state dict might look something like:

      {
          'state': {
              0: {'momentum_buffer': tensor(...), ...},
              1: {'momentum_buffer': tensor(...), ...},
              2: {'momentum_buffer': tensor(...), ...},
              3: {'momentum_buffer': tensor(...), ...}
          },
          'param_groups': [
              {
                  'lr': 0.01,
                  'weight_decay': 0,
                  ...
                  'params': [0]
              },
              {
                  'lr': 0.001,
                  'weight_decay': 0.5,
                  ...
                  'params': [1, 2, 3]
              }
          ]
      }

  Return type:

    Dict[str, Any]

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