def testAddManyTakeManyRoundTripBatched(self):
with self.test_session(use_gpu=False) as sess:
# N == 4 because shape_value == [4, 5]
indices_value_1 = np.array([[0, 0], [0, 1], [2, 0]],
dtype=np.int64)
values_value_1 = np.array([b"a", b"b", b"c"])
shape_value_1 = np.array([4, 5], dtype=np.int64)
sparse_tensor_1 = sparse_tensor.SparseTensor(
array_ops.placeholder(dtypes.int64),
array_ops.placeholder(dtypes.string),
array_ops.placeholder(dtypes.int64))
dict1 = {"key": sparse_tensor_1}
indices_value_2 = np.array([[1, 4], [2, 3]], dtype=np.int64)
values_value_2 = np.array([b"d", b"e"])
shape_value_2 = np.array([4, 5], dtype=np.int64)
sparse_tensor_2 = sparse_tensor.SparseTensor(
array_ops.placeholder(dtypes.int64),
array_ops.placeholder(dtypes.string),
array_ops.placeholder(dtypes.int64))
dict2 = {"key": sparse_tensor_2}
input_seq1, keys1, tensor_list1 = sqss._deconstruct_sparse_tensor_seq(
dict1, shared_name="a")
handles_1 = input_seq1["key"]
input_seq2, _, _ = sqss._deconstruct_sparse_tensor_seq(
dict2, shared_name="a")
handles_2 = input_seq2["key"]
combined_handles = array_ops.stack([
handles_1[1], handles_1[2], handles_1[3], handles_2[1],
handles_2[2], handles_2[3]
])
batched_dict = {"key": combined_handles}
sqss._reconstruct_sparse_tensor_seq(batched_dict,
keys1,
tensor_list1,
batch_size=2,
num_unroll=3)
roundtrip_value, = sess.run(
[batched_dict["key"]],
feed_dict={
sparse_tensor_1.indices: indices_value_1,
sparse_tensor_1.values: values_value_1,
sparse_tensor_1.dense_shape: shape_value_1,
sparse_tensor_2.indices: indices_value_2,
sparse_tensor_2.values: values_value_2,
sparse_tensor_2.dense_shape: shape_value_2
})
self.assertAllEqual(
roundtrip_value.indices,
np.array([[0, 1, 0], [1, 0, 4], [1, 1, 3]], dtype=np.int64))
self.assertAllEqual(roundtrip_value.values,
np.array([b"c", b"d", b"e"]))
self.assertAllEqual(roundtrip_value.dense_shape,
np.array([2, 3, 5], dtype=np.int64))
def testAddManyTakeManyRoundTripBatched(self):
with self.test_session(use_gpu=False) as sess:
# N == 4 because shape_value == [4, 5]
indices_value_1 = np.array([[0, 0], [0, 1], [2, 0]], dtype=np.int64)
values_value_1 = np.array([b"a", b"b", b"c"])
shape_value_1 = np.array([4, 5], dtype=np.int64)
sparse_tensor_1 = sparse_tensor.SparseTensor(
array_ops.placeholder(dtypes.int64),
array_ops.placeholder(dtypes.string),
array_ops.placeholder(dtypes.int64))
dict1 = {"key": sparse_tensor_1}
indices_value_2 = np.array([[1, 4], [2, 3]], dtype=np.int64)
values_value_2 = np.array([b"d", b"e"])
shape_value_2 = np.array([4, 5], dtype=np.int64)
sparse_tensor_2 = sparse_tensor.SparseTensor(
array_ops.placeholder(dtypes.int64),
array_ops.placeholder(dtypes.string),
array_ops.placeholder(dtypes.int64))
dict2 = {"key": sparse_tensor_2}
input_seq1, keys1, tensor_list1 = sqss._deconstruct_sparse_tensor_seq(
dict1, shared_name="a")
handles_1 = input_seq1["key"]
input_seq2, _, _ = sqss._deconstruct_sparse_tensor_seq(
dict2, shared_name="a")
handles_2 = input_seq2["key"]
combined_handles = array_ops.stack(
[handles_1[1], handles_1[2], handles_1[3],
handles_2[1], handles_2[2], handles_2[3]])
batched_dict = {"key": combined_handles}
sqss._reconstruct_sparse_tensor_seq(
batched_dict,
keys1,
tensor_list1,
batch_size=2,
num_unroll=3)
roundtrip_value, = sess.run(
[batched_dict["key"]],
feed_dict={sparse_tensor_1.indices: indices_value_1,
sparse_tensor_1.values: values_value_1,
sparse_tensor_1.dense_shape: shape_value_1,
sparse_tensor_2.indices: indices_value_2,
sparse_tensor_2.values: values_value_2,
sparse_tensor_2.dense_shape: shape_value_2})
self.assertAllEqual(roundtrip_value.indices,
np.array([[0, 1, 0], [1, 0, 4], [1, 1, 3]],
dtype=np.int64))
self.assertAllEqual(roundtrip_value.values,
np.array([b"c", b"d", b"e"]))
self.assertAllEqual(roundtrip_value.dense_shape,
np.array([2, 3, 5], dtype=np.int64))
