def addmm(self, tensor2, mat, beta=1, alpha=1):
"""Performs ((Mat*Beta)+((Tensor1@Tensor2)*Alpha)) and returns the
result as a Tensor
Tensor1.Tensor2 is performed as Matrix product of two array The
behavior depends on the arguments in the following way.
*If both tensors are 1-dimensional, their dot product is returned.
*If both arguments are 2-D they are multiplied like conventional
matrices.
*If either argument is N-D, N > 2, it is treated as a stack of
matrices residing in the last two indexes and broadcast
accordingly.
*If the first argument is 1-D, it is promoted to a matrix by
prepending a 1 to its dimensions. After matrix multiplication the
prepended 1 is removed.
*If the second argument is 1-D, it is promoted to a matrix by
appending a 1 to its dimensions. After matrix multiplication the
appended 1 is removed.
"""
return syft.addmm(self, tensor2, mat, beta, alpha)
def test_addmm_2d(self):
t1 = TensorBase(np.array([[1, 2], [1, 2]]))
t2 = TensorBase(np.array([[1, 2], [1, 2]]))
mat = TensorBase(np.array([[2, 3], [3, 4]]))
out = syft.addmm(t1, t2, mat, beta=2, alpha=2)
self.assertTrue(np.array_equal(out.data, [[10, 18], [12, 20]]))
def test_addmm_1d(self):
t1 = TensorBase(np.array([1, 2, 3]))
t2 = TensorBase(np.array([2, 3, 4]))
mat = TensorBase(np.array([5]))
out = syft.addmm(t1, t2, mat, beta=2, alpha=2)
self.assertTrue(np.array_equal(out.data, [50]))
