def test_vocab_list_sparse_input(self):
layer = categorical.CategoryLookup(
vocabulary=self._wire_vocabulary_file_name, num_oov_tokens=0)
inp = np.asarray([['omar', ''], ['stringer', 'marlo'], ['marlo', 'omar']])
indices = array_ops.where_v2(math_ops.not_equal(inp, ''))
sp_inp = sparse_tensor.SparseTensor(
indices,
array_ops.gather_nd_v2(inp, indices),
dense_shape=array_ops.shape_v2(inp, out_type=dtypes.int64))
output = layer(sp_inp)
self.assertIsInstance(output, sparse_tensor.SparseTensor)
self.assertAllClose(
np.asarray([[0, 0], [1, 0], [1, 1], [2, 0], [2, 1]]), output.indices)
self.assertAllClose(np.asarray([0, 1, 2, 2, 0]), output.values)
def test_vocab_list_sparse_input(self):
vocabulary_list = ['A', 'B', 'C', 'D', 'E']
layer = categorical.CategoryLookup(
vocabulary=vocabulary_list, num_oov_tokens=0)
inp = np.asarray([['A', ''], ['E', 'C'], ['D', 'A']])
indices = array_ops.where_v2(math_ops.not_equal(inp, ''))
sp_inp = sparse_tensor.SparseTensor(
indices,
array_ops.gather_nd_v2(inp, indices),
dense_shape=array_ops.shape_v2(inp, out_type=dtypes.int64))
output = layer(sp_inp)
self.assertIsInstance(output, sparse_tensor.SparseTensor)
self.assertAllClose(
np.asarray([[0, 0], [1, 0], [1, 1], [2, 0], [2, 1]]), output.indices)
self.assertAllClose(np.asarray([0, 4, 2, 3, 0]), output.values)