mean

dragon.vm.torch.mean(
  input,
  dim=None,
  keepdim=False,
  out=None
)[source]

Compute the mean value of elements along the given dimension.

dim could be negative or None:

x = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.float32)

# A negative dimension is the last-k dimension
print(torch.mean(x, dim=1))
print(torch.mean(x, dim=-1))  # Equivalent

# If dimension is None, reduce input as a vector
# and return a scalar result
print(torch.mean(x))  # 3.5

# Also, dimension could be a sequence of integers
print(torch.mean(x, dim=(0, 1)))  # 3.5
Parameters:
  • input (dragon.vm.torch.Tensor) The input tensor.
  • dim (Union[int, Sequence[int]], optional) The dimension to reduce.
  • keepdim (bool, optional, default=False) Keep the reduced dimension or not.
  • out (dragon.vm.torch.Tensor, optional) The output tensor.
Returns:

dragon.vm.torch.Tensor The output tensor.