- MNistNet.register_buffer(name: str, tensor: Optional[Tensor], persistent: bool = True) None #
Adds a buffer to the module.
This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s
running_meanis not a parameter, but is part of the module’s state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting
False. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’s
Buffers can be accessed as attributes using given names.
- name (string): name of the buffer. The buffer can be accessed
from this module using the given name
- tensor (Tensor or None): buffer to be registered. If
None, then operations
- persistent (bool): whether the buffer is part of this module’s
>>> self.register_buffer('running_mean', torch.zeros(num_features))