def testMismatchedShapes(self):
with test_util.force_cpu():
sp_zero = sparse_tensor.SparseTensor([[0, 0]], [0], [1, 1])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [2])
with self.assertRaisesOpError("Operands do not have the same ranks"):
self.evaluate(sparse_ops.sparse_maximum(sp_zero, sp_one))
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [1])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [2])
with self.assertRaisesOpError("Operands' shapes do not match"):
self.evaluate(sparse_ops.sparse_maximum(sp_zero, sp_one))
def testMismatchedShapes(self):
with self.test_session(use_gpu=False):
sp_zero = sparse_tensor.SparseTensor([[0, 0]], [0], [1, 1])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [2])
with self.assertRaisesOpError("Operands do not have the same ranks"):
sparse_ops.sparse_maximum(sp_zero, sp_one).eval()
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [1])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [2])
with self.assertRaisesOpError("Operands' shapes do not match"):
sparse_ops.sparse_maximum(sp_zero, sp_one).eval()
def testMismatchedShapes(self):
with self.test_session(use_gpu=False):
sp_zero = sparse_tensor.SparseTensor([[0, 0]], [0], [1, 1])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [2])
with self.assertRaisesOpError("Operands do not have the same ranks"):
sparse_ops.sparse_maximum(sp_zero, sp_one).eval()
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [1])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [2])
with self.assertRaisesOpError("Operands' shapes do not match"):
sparse_ops.sparse_maximum(sp_zero, sp_one).eval()
def testMismatchedShapes(self):
with test_util.force_cpu():
sp_zero = sparse_tensor.SparseTensor([[0, 0]], [0], [1, 1])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [2])
with self.assertRaisesOpError("Operands do not have the same ranks"):
self.evaluate(sparse_ops.sparse_maximum(sp_zero, sp_one))
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [1])
sp_one = sparse_tensor.SparseTensor([[0]], [1], [2])
with self.assertRaisesOpError("Operands' shapes do not match"):
self.evaluate(sparse_ops.sparse_maximum(sp_zero, sp_one))
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 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)
