# Batch matrix multiplication: (b*m*n) * (b*n*p) -> (b*m*p)result = torch.bmm(tensor1, tensor2) # Element-wise multiplication.result = tensor1 * tensor2 计算两组数据之间的两两欧式距离 利用broadcast机制 dist = torch.sqrt(torch.sum((X1[:,None,:] - X2) ** 2, dim=2)) 3 ...
# This computes the matrix multiplication between two tensors. y1, y2, y3 will have the same value# ``tensor.T`` returns the transpose of a tensor#矩阵乘法y1=tensor@tensor.Ty2=tensor.matmul(tensor.T)y3=torch.rand_like(y1)torch.matmul(tensor,tensor.T,out=y3)# This computes the elemen...
Performs the element-wise division oftensor1bytensor2, multiply the result by the scalarvalueand add it toinput. outi=inputi+value×tensor1itensor2i\text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i} outi=inputi+value×tensor2i...
Element-wise Mathematical Operations[res] torch.abs([res,] x)y = torch.abs(x) returns a new Tensor with the absolute values of the elements of x.x:abs() replaces all elements in-place with the absolute values of the elements of x....
torch.addcmul(input, value=1, tensor1, tensor2, out=None)→ Tensor Performs the element-wise multiplication of tensor1 by tensor2, multiply the result by the scalar value and add it to input. outi=inputi+value×tensor1i×tensor2i\text{out}_i = \text{input}_i + \text{value} \tim...
assertEqual(actual_output, output) failing_inputs = [ ('4', AssertionError), ([6.8], AssertionError), (torch.Tensor([1, 3.8]), ValueError), # Tensor has more than 1 element ] for input_, error in failing_inputs: with self.assertRaises(error): SumMetric(input_) Example #10...
I create this new issue because I think it might have a different cause than the previous one. The repro script is the same as#120478. I just changed the shapes of test tensors in it. Error logs No response Minified repro import torch ...
# This computes the matrix multiplication between two tensors. y1, y2, y3 will have the same value y1 = tensor @ tensor.T y2 = tensor.matmul(tensor.T) y3 = torch.rand_like(tensor) torch.matmul(tensor, tensor.T, out=y3) # This computes the element-wise product. z...
adjacency) for edge in edges + [node] ] values = torch.zeros(len(indices)) for idx, index in enumerate(indices): values[idx] = self._laplacian_element(*index) indices = torch.Tensor(indices).t() self._laplacian = torch.sparse_coo_tensor( indices, values, (len(self.adjacency), len(...
A bool indicating whether the loss should be averaged across the batch, or returned as a vector of losses per batch element. Returns --- A torch.FloatTensor representing the cross entropy loss. If ``batch_average == True``, the returned loss is a scalar. If ``...