def argmax(input, dim=None, keepdim=False): """Returns the indices of the maximum values of a tensor across a dimension. This is the second value returned by :meth:`torch.max`. See its documentation for the exact semantics of this method. Args: input (Tensor): the input tensor dim (int): the dimension to reduce. If ``None``, the argmax of the flattened input is returned. keepdim (bool): whether the output tensors have :attr:`dim` retained or not. Ignored if ``dim=None``. Example:: >>> a = torch.randn(4, 4) >>> a 2.3461 0.0056 1.4846 0.3911 -1.3584 -1.0066 0.0530 1.1754 -0.7929 -0.3194 -1.4865 0.4020 0.1101 0.6694 1.3456 0.8235 [torch.FloatTensor of size (4,4)] >>> torch.argmax(a, dim=1) 0 3 3 2 [torch.LongTensor of size (4,)] """ if dim is None: return torch._argmax(input.contiguous().view(-1), dim=0, keepdim=False) return torch._argmax(input, dim, keepdim)
def argmax(input, dim=None, keepdim=False): r"""Returns the indices of the maximum values of a tensor across a dimension. This is the second value returned by :meth:`torch.max`. See its documentation for the exact semantics of this method. Args: input (Tensor): the input tensor dim (int): the dimension to reduce. If ``None``, the argmax of the flattened input is returned. keepdim (bool): whether the output tensors have :attr:`dim` retained or not. Ignored if ``dim=None``. Example:: >>> a = torch.randn(4, 4) >>> a tensor([[ 1.3398, 0.2663, -0.2686, 0.2450], [-0.7401, -0.8805, -0.3402, -1.1936], [ 0.4907, -1.3948, -1.0691, -0.3132], [-1.6092, 0.5419, -0.2993, 0.3195]]) >>> torch.argmax(a, dim=1) tensor([ 0, 2, 0, 1]) """ if dim is None: return torch._argmax(input.contiguous().view(-1), dim=0, keepdim=False) return torch._argmax(input, dim, keepdim)
def argmax(input, dim=None, keepdim=False): """Returns the indices of the maximum values of a tensor across a dimension. This is the second value returned by :meth:`torch.max`. See its documentation for the exact semantics of this method. Args: input (Tensor): the input tensor dim (int): the dimension to reduce. If ``None``, the argmax of the flattened input is returned. keepdim (bool): whether the output tensors have :attr:`dim` retained or not. Ignored if ``dim=None``. Example:: >>> a = torch.randn(4, 4) >>> a tensor([[ 1.3398, 0.2663, -0.2686, 0.2450], [-0.7401, -0.8805, -0.3402, -1.1936], [ 0.4907, -1.3948, -1.0691, -0.3132], [-1.6092, 0.5419, -0.2993, 0.3195]]) >>> torch.argmax(a, dim=1) tensor([ 0, 2, 0, 1]) """ if dim is None: return torch._argmax(input.contiguous().view(-1), dim=0, keepdim=False) return torch._argmax(input, dim, keepdim)