If it is a tensor, it will be automatically converted Gradient ( Tensor or None) – Gradient w.r.t. grad attributes or set them to None before calling it.įor details on the memory layout of accumulated gradients. This function accumulates gradients in the leaves - you might need to zero The gradient of the differentiated function w.r.t. It should be a tensor of matching type and location, that contains Gradient, the function additionally requires specifying gradient. its data has more than one element) and requires The graph is differentiated using the chain rule. In-place version of atan2() backward ( gradient=None, retain_graph=None, create_graph=False ) Ĭomputes the gradient of current tensor w.r.t. See torch.atan2() atan2_ ( other ) → Tensor ¶ In-place version of arctan() atan2 ( other ) → Tensor ¶ See torch.arctan() arctan_ ( ) → Tensor ¶ In-place version of atan() arctan ( ) → Tensor ¶ See torch.as_strided() atan ( ) → Tensor ¶ In-place version of arcsin() as_strided ( size, stride, storage_offset=0 ) → Tensor ¶ See torch.arcsin() arcsin_ ( ) → Tensor ¶ In-place version of asin() arcsin ( ) → Tensor ¶ See torch.argmin() argsort ( dim=-1, descending=False ) → LongTensor ¶ See torch.argmax() argmin ( dim=None, keepdim=False ) → LongTensor ¶ argmax ( dim=None, keepdim=False ) → LongTensor ¶
PERMUTE BY ROW TORCH CODE
This function only works with CPU tensors and should not be used in code See torch.angle() apply_ ( callable ) → Tensor ¶Īpplies the function callable to each element in the tensor, replacingĮach element with the value returned by callable. See torch.amax() amin ( dim=None, keepdim=False ) → Tensor ¶ See torch.allclose() amax ( dim=None, keepdim=False ) → Tensor ¶ In-place version of addr() allclose ( other, rtol=1e-05, atol=1e-08, equal_nan=False ) → Tensor ¶ See torch.addr() addr_ ( vec1, vec2, *, beta=1, alpha=1 ) → Tensor ¶ In-place version of addmv() addr ( vec1, vec2, *, beta=1, alpha=1 ) → Tensor ¶ See torch.addmv() addmv_ ( mat, vec, *, beta=1, alpha=1 ) → Tensor ¶ In-place version of addmm() addmv ( mat, vec, *, beta=1, alpha=1 ) → Tensor ¶ See torch.addmm() addmm_ ( mat1, mat2, *, beta=1, alpha=1 ) → Tensor ¶ In-place version of addcmul() addmm ( mat1, mat2, *, beta=1, alpha=1 ) → Tensor ¶ See torch.addcmul() addcmul_ ( tensor1, tensor2, *, value=1 ) → Tensor ¶ In-place version of addcdiv() addcmul ( tensor1, tensor2, *, value=1 ) → Tensor ¶ See torch.addcdiv() addcdiv_ ( tensor1, tensor2, *, value=1 ) → Tensor ¶ In-place version of addbmm() addcdiv ( tensor1, tensor2, *, value=1 ) → Tensor ¶ See torch.addbmm() addbmm_ ( batch1, batch2, *, beta=1, alpha=1 ) → Tensor ¶ In-place version of add() addbmm ( batch1, batch2, *, beta=1, alpha=1 ) → Tensor ¶ See torch.add() add_ ( other, *, alpha=1 ) → Tensor ¶ When other is a tensor, the shape of other must beīroadcastable with the shape of the underlying If both alphaĪnd other are specified, each element of other is scaled by In-place version of arccos() add ( other, *, alpha=1 ) → Tensor ¶Īdd a scalar or tensor to self tensor. See torch.arccos() arccos_ ( ) → Tensor ¶ In-place version of acos() arccos ( ) → Tensor ¶ In-place version of abs() absolute ( ) → Tensor ¶ The returned tensor and self share the same underlying storage. Returns a new tensor containing real values of the self tensor. Is this Tensor with its dimensions reversed. The attribute will then contain the gradients computed and future calls toīackward() will accumulate (add) gradients into it. This attribute is None by default and becomes a Tensor the first time a call to
![permute by row torch permute by row torch](https://i.stack.imgur.com/x4FRy.png)
Meta tensorsĪre like normal tensors, but they carry no data. Is True if the Tensor is a meta tensor, False otherwise.
![permute by row torch permute by row torch](https://media.bizj.us/view/img/10301177/howtopassingtorch*1200xx3865-2178-0-309.jpg)
Is True if the Tensor is quantized, False otherwise.
![permute by row torch permute by row torch](https://natedsanders.com/ItemImages/000041/50054h_lg.jpeg)
Is True if the Tensor is stored on the GPU, False otherwise. new_zeros (( 2, 3 )) tensor(, ], dtype=torch.float64) is_cuda ¶ Torch.Tensor is an alias for the default tensor type ( torch.FloatTensor).Ī tensor can be constructed from a Python list or sequence using the Useful when range is important, since it has the same Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7 Useful when precision is important at the expense of range. Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 Torch defines 10 tensor types with CPU and GPU variants which are as follows: A torch.Tensor is a multi-dimensional matrix containing elements of