test_nn.py 文件源码

def test_sparse_default_std(self):
        for as_variable in [True, False]:
            for use_random_std in [True, False]:
                input_tensor = self._create_random_nd_tensor(2, size_min=30, size_max=35, as_variable=as_variable)
                rows, cols = input_tensor.size(0), input_tensor.size(1)
                sparsity = self._random_float(0.1, 0.2)

                std = 0.01  # default std
                if use_random_std:
                    std = self._random_float(0.01, 0.2)
                    init.sparse(input_tensor, sparsity=sparsity, std=std)
                else:
                    init.sparse(input_tensor, sparsity=sparsity)

                if as_variable:
                    input_tensor = input_tensor.data

                for col_idx in range(input_tensor.size(1)):
                    column = input_tensor[:, col_idx]
                    assert column[column == 0].nelement() >= math.ceil(sparsity * cols)

                assert self._is_normal(input_tensor[input_tensor != 0], 0, std)