def _build_multilabel_adjacency(sparse_labels):
"""Builds multilabel adjacency matrix.
As of March 14th, 2017, there's no op for the dot product between
two sparse tensors in TF. However, there is `sparse_minimum` op which is
equivalent to an AND op between two sparse boolean tensors.
This computes the dot product between two sparse boolean inputs.
Args:
sparse_labels: List of 1-D boolean sparse tensors.
Returns:
adjacency_matrix: 2-D dense `Tensor`.
"""
num_pairs = len(sparse_labels)
adjacency_matrix = array_ops.zeros([num_pairs, num_pairs])
for i in range(num_pairs):
for j in range(num_pairs):
sparse_dot_product = math_ops.to_float(
sparse_ops.sparse_reduce_sum(
sparse_ops.sparse_minimum(sparse_labels[i],
sparse_labels[j])))
sparse_dot_product = array_ops.expand_dims(sparse_dot_product, 0)
sparse_dot_product = array_ops.expand_dims(sparse_dot_product, 1)
one_hot_matrix = array_ops.pad(
sparse_dot_product,
[[i, num_pairs - i - 1], [j, num_pairs - j - 1]], 'CONSTANT')
adjacency_matrix += one_hot_matrix
return adjacency_matrix
def testRandom(self):
np.random.seed(1618)
shapes = [(13, ), (6, 8), (1, 7, 1)]
for shape in shapes:
for dtype in [
np.int32, np.int64, np.float16, np.float32, np.float64
]:
a_np = np.random.randn(*shape).astype(dtype)
b_np = np.random.randn(*shape).astype(dtype)
sp_a, unused_a_nnz = _sparsify(a_np, thresh=-.5)
sp_b, unused_b_nnz = _sparsify(b_np, thresh=-.5)
with self.cached_session(use_gpu=False):
maximum_tf = sparse_ops.sparse_maximum(sp_a, sp_b)
maximum_tf_densified = sparse_ops.sparse_tensor_to_dense(
maximum_tf).eval()
minimum_tf = sparse_ops.sparse_minimum(sp_a, sp_b)
minimum_tf_densified = sparse_ops.sparse_tensor_to_dense(
minimum_tf).eval()
a_densified = sparse_ops.sparse_tensor_to_dense(
sp_a).eval()
b_densified = sparse_ops.sparse_tensor_to_dense(
sp_b).eval()
self.assertAllEqual(np.maximum(a_densified, b_densified),
maximum_tf_densified)
self.assertAllEqual(np.minimum(a_densified, b_densified),
minimum_tf_densified)
def testRandom(self):
np.random.seed(1618)
shapes = [(13,), (6, 8), (1, 7, 1)]
for shape in shapes:
for dtype in [np.int32, np.int64, np.float16, np.float32, np.float64]:
a_np = np.random.randn(*shape).astype(dtype)
b_np = np.random.randn(*shape).astype(dtype)
sp_a, unused_a_nnz = _sparsify(a_np, thresh=-.5)
sp_b, unused_b_nnz = _sparsify(b_np, thresh=-.5)
with self.test_session(use_gpu=False):
maximum_tf = sparse_ops.sparse_maximum(sp_a, sp_b)
maximum_tf_densified = sparse_ops.sparse_tensor_to_dense(
maximum_tf).eval()
minimum_tf = sparse_ops.sparse_minimum(sp_a, sp_b)
minimum_tf_densified = sparse_ops.sparse_tensor_to_dense(
minimum_tf).eval()
a_densified = sparse_ops.sparse_tensor_to_dense(sp_a).eval()
b_densified = sparse_ops.sparse_tensor_to_dense(sp_b).eval()
self.assertAllEqual(
np.maximum(a_densified, b_densified), maximum_tf_densified)
self.assertAllEqual(
np.minimum(a_densified, b_densified), minimum_tf_densified)
def _build_multilabel_adjacency(sparse_labels):
"""Builds multilabel adjacency matrix.
As of March 14th, 2017, there's no op for the dot product between
two sparse tensors in TF. However, there is `sparse_minimum` op which is
equivalent to an AND op between two sparse boolean tensors.
This computes the dot product between two sparse boolean inputs.
Args:
sparse_labels: List of 1-D boolean sparse tensors.
Returns:
adjacency_matrix: 2-D dense `Tensor`.
"""
num_pairs = len(sparse_labels)
adjacency_matrix = array_ops.zeros([num_pairs, num_pairs])
for i in range(num_pairs):
for j in range(num_pairs):
sparse_dot_product = math_ops.to_float(
sparse_ops.sparse_reduce_sum(sparse_ops.sparse_minimum(
sparse_labels[i], sparse_labels[j])))
sparse_dot_product = array_ops.expand_dims(sparse_dot_product, 0)
sparse_dot_product = array_ops.expand_dims(sparse_dot_product, 1)
one_hot_matrix = array_ops.pad(sparse_dot_product,
[[i, num_pairs-i-1],
[j, num_pairs-j-1]], 'CONSTANT')
adjacency_matrix += one_hot_matrix
return adjacency_matrix
def testBasic(self):
with test_util.force_cpu():
# 1-D, values at index 0.
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [7])
max_tf = sparse_ops.sparse_maximum(sp_zero, sp_one)
min_tf = sparse_ops.sparse_minimum(sp_zero, sp_one)
self._assertSparseTensorValueEqual(sp_one, max_tf)
self._assertSparseTensorValueEqual(sp_zero, min_tf)
# Values at different indices.
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_zero_2 = sparse_tensor.SparseTensor([[1]], [0], [7])
expected = sparse_tensor.SparseTensor([[0], [1]], [0, 0], [7])
max_tf = sparse_ops.sparse_maximum(sp_zero, sp_zero_2)
min_tf = sparse_ops.sparse_minimum(sp_zero, sp_zero_2)
self._assertSparseTensorValueEqual(expected, max_tf)
self._assertSparseTensorValueEqual(expected, min_tf)
def testBasic(self):
with self.test_session(use_gpu=False):
# 1-D, values at index 0.
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [7])
max_tf = sparse_ops.sparse_maximum(sp_zero, sp_one).eval()
min_tf = sparse_ops.sparse_minimum(sp_zero, sp_one).eval()
self._assertSparseTensorValueEqual(sp_one.eval(), max_tf)
self._assertSparseTensorValueEqual(sp_zero.eval(), min_tf)
# Values at different indices.
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_zero_2 = sparse_tensor.SparseTensor([[1]], [0], [7])
expected = sparse_tensor.SparseTensor([[0], [1]], [0, 0], [7])
max_tf = sparse_ops.sparse_maximum(sp_zero, sp_zero_2).eval()
min_tf = sparse_ops.sparse_minimum(sp_zero, sp_zero_2).eval()
self._assertSparseTensorValueEqual(expected.eval(), max_tf)
self._assertSparseTensorValueEqual(expected.eval(), min_tf)
def testBasic(self):
with self.test_session(use_gpu=False):
# 1-D, values at index 0.
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [7])
max_tf = sparse_ops.sparse_maximum(sp_zero, sp_one).eval()
min_tf = sparse_ops.sparse_minimum(sp_zero, sp_one).eval()
self._assertSparseTensorValueEqual(sp_one.eval(), max_tf)
self._assertSparseTensorValueEqual(sp_zero.eval(), min_tf)
# Values at different indices.
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_zero_2 = sparse_tensor.SparseTensor([[1]], [0], [7])
expected = sparse_tensor.SparseTensor([[0], [1]], [0, 0], [7])
max_tf = sparse_ops.sparse_maximum(sp_zero, sp_zero_2).eval()
min_tf = sparse_ops.sparse_minimum(sp_zero, sp_zero_2).eval()
self._assertSparseTensorValueEqual(expected.eval(), max_tf)
self._assertSparseTensorValueEqual(expected.eval(), min_tf)
def testBasic(self):
with test_util.force_cpu():
# 1-D, values at index 0.
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [7])
max_tf = sparse_ops.sparse_maximum(sp_zero, sp_one)
min_tf = sparse_ops.sparse_minimum(sp_zero, sp_one)
self._assertSparseTensorValueEqual(sp_one, max_tf)
self._assertSparseTensorValueEqual(sp_zero, min_tf)
# Values at different indices.
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_zero_2 = sparse_tensor.SparseTensor([[1]], [0], [7])
expected = sparse_tensor.SparseTensor([[0], [1]], [0, 0], [7])
max_tf = sparse_ops.sparse_maximum(sp_zero, sp_zero_2)
min_tf = sparse_ops.sparse_minimum(sp_zero, sp_zero_2)
self._assertSparseTensorValueEqual(expected, max_tf)
self._assertSparseTensorValueEqual(expected, min_tf)
