Source code for tensorly.tenalg.core_tenalg.outer_product

import tensorly as tl

# TODO : add batched_modes as in batched_tensor_dot? 
[docs]def batched_outer(tensors): """Returns a generalized outer product of the two tensors Parameters ---------- tensor1 : tensor of shape (n_samples, J1, ..., JN) tensor2 : tensor of shape (n_samples, K1, ..., KM) Returns ------- outer product of tensor1 and tensor2 of shape (n_samples, J1, ..., JN, K1, ..., KM) """ for i, tensor in enumerate(tensors): if i: shape = tl.shape(tensor) size = len(shape) - 1 n_samples = shape[0] if n_samples != shape_res[0]: raise ValueError(f'Tensor {i} has a batch-size of {n_samples} but those before had a batch-size of {shape_res[0]}, ' 'all tensors should have the same batch-size.') shape_1 = shape_res + (1, )*size shape_2 = (n_samples, ) + (1, )*size_res + shape[1:] res = tl.reshape(res, shape_1) * tl.reshape(tensor, shape_2) else: res = tensor shape_res = tl.shape(res) size_res = len(shape_res) - 1 return res
[docs]def outer(tensors): """Returns a generalized outer product of the two tensors Parameters ---------- tensor1 : tensor of shape (J1, ..., JN) tensor2 : tensor of shape (K1, ..., KM) Returns ------- outer product of tensor1 and tensor2 of shape (J1, ..., JN, K1, ..., KM) """ for i, tensor in enumerate(tensors): if i: shape = tl.shape(tensor) s1 = len(shape) shape_1 = shape_res + (1, )*s1 shape_2 = (1, )*sres + shape res = tl.reshape(res, shape_1) * tl.reshape(tensor, shape_2) else: res = tensor shape_res = tl.shape(res) sres = len(shape_res) return res