TensorDictBase.min#

TensorDictBase.min(dim: int | NO_DEFAULT = NO_DEFAULT, keepdim: bool = False, *, reduce: bool | None = None, return_indices: bool = True) → Self | Tensor#

Returns the minimum values of all elements in the input tensordict.

Parameters:

dim (int, optional) – if None, returns a dimensionless tensordict containing the min value of all leaves (if this can be computed). If integer, min is called upon the dimension specified if and only if this dimension is compatible with the tensordict shape. A tuple reduces only its first dim; passing one is deprecated and will be removed in TensorDict 0.17.
keepdim (bool) – whether the output tensor has dim retained or not.

Keyword Arguments:

reduce (bool, optional) – if True, the reduction will occur across all TensorDict values and a single reduced tensor will be returned. Defaults to False. With feature dims, a negative dim counts from the end of each leaf. Starting with TensorDict 0.17, the default will change to counting it from the end of the batch dims, as with reduce=False.
return_indices (bool, optional) – min() returns a named tuple with values and indices when the dim argument is passed. The TensorDict equivalent of this is to return a named tuple with values and indices tensordicts of identical structure. If False, only the values are returned. Defaults to True.

Examples

>>> from tensordict import TensorDict
>>> import torch
>>> _ = torch.manual_seed(0)
>>> td = TensorDict(
...     a=torch.randn(3, 4, 5),
...     b=TensorDict(
...         c=torch.randn(3, 4, 5, 6),
...         d=torch.randn(3, 4, 5),
...         batch_size=(3, 4, 5),
...     ),
...     batch_size=(3, 4)
... )
>>> td.min(dim=0)
torch.return_types.min(
values=TensorDict(
    fields={
        a: Tensor(shape=torch.Size([4, 5]), device=cpu, dtype=torch.float32, is_shared=False),
        b: TensorDict(
            fields={
                c: Tensor(shape=torch.Size([4, 5, 6]), device=cpu, dtype=torch.float32, is_shared=False),
                d: Tensor(shape=torch.Size([4, 5]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([4]),
            device=None,
            is_shared=False)},
    batch_size=torch.Size([4]),
    device=None,
    is_shared=False),
indices=TensorDict(
    fields={
        a: Tensor(shape=torch.Size([4, 5]), device=cpu, dtype=torch.int64, is_shared=False),
        b: TensorDict(
            fields={
                c: Tensor(shape=torch.Size([4, 5, 6]), device=cpu, dtype=torch.int64, is_shared=False),
                d: Tensor(shape=torch.Size([4, 5]), device=cpu, dtype=torch.int64, is_shared=False)},
            batch_size=torch.Size([4]),
            device=None,
            is_shared=False)},
    batch_size=torch.Size([4]),
    device=None,
    is_shared=False))
>>> td.min()
TensorDict(
    fields={
        a: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        b: TensorDict(
            fields={
                c: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                d: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([]),
            device=None,
            is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False)
>>> td.min(reduce=True)
tensor(-3.1537)

Every tensordict class and every tensorclass has this method. For the other methods, see Methods by task.

TensorDictBase.min#

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