def testConcurrentExecutesWithoutError(self):
with self.test_session(use_gpu=True) as sess:
matrix1 = random_ops.random_normal([5, 5], seed=42)
matrix2 = random_ops.random_normal([5, 5], seed=42)
sqrt1 = gen_linalg_ops.matrix_square_root(matrix1)
sqrt2 = gen_linalg_ops.matrix_square_root(matrix2)
all_ops = [sqrt1, sqrt2]
sqrt = sess.run(all_ops)
self.assertAllEqual(sqrt[0], sqrt[1])
def testConcurrentExecutesWithoutError(self):
with self.test_session(use_gpu=True) as sess:
matrix1 = random_ops.random_normal([5, 5], seed=42)
matrix2 = random_ops.random_normal([5, 5], seed=42)
sqrt1 = gen_linalg_ops.matrix_square_root(matrix1)
sqrt2 = gen_linalg_ops.matrix_square_root(matrix2)
all_ops = [sqrt1, sqrt2]
sqrt = sess.run(all_ops)
self.assertAllEqual(sqrt[0], sqrt[1])
def testConcurrentExecutesWithoutError(self):
with test_util.use_gpu():
matrix1 = random_ops.random_normal([5, 5], seed=42)
matrix2 = random_ops.random_normal([5, 5], seed=42)
square1 = math_ops.matmul(matrix1, matrix1)
square2 = math_ops.matmul(matrix2, matrix2)
sqrt1 = gen_linalg_ops.matrix_square_root(square1)
sqrt2 = gen_linalg_ops.matrix_square_root(square2)
all_ops = [sqrt1, sqrt2]
sqrt = self.evaluate(all_ops)
self.assertAllClose(sqrt[0], sqrt[1])
def testConcurrentExecutesWithoutError(self):
with test_util.use_gpu():
matrix1 = random_ops.random_normal([5, 5], seed=42)
matrix2 = random_ops.random_normal([5, 5], seed=42)
square1 = math_ops.matmul(matrix1, matrix1)
square2 = math_ops.matmul(matrix2, matrix2)
sqrt1 = gen_linalg_ops.matrix_square_root(square1)
sqrt2 = gen_linalg_ops.matrix_square_root(square2)
all_ops = [sqrt1, sqrt2]
sqrt = self.evaluate(all_ops)
self.assertAllClose(sqrt[0], sqrt[1])
def testConcurrentExecutesWithoutError(self):
matrix_shape = [5, 5]
seed = [42, 24]
matrix1 = stateless_random_ops.stateless_random_normal(
shape=matrix_shape, seed=seed)
matrix2 = stateless_random_ops.stateless_random_normal(
shape=matrix_shape, seed=seed)
self.assertAllEqual(matrix1, matrix2)
square1 = math_ops.matmul(matrix1, matrix1)
square2 = math_ops.matmul(matrix2, matrix2)
sqrt1 = gen_linalg_ops.matrix_square_root(square1)
sqrt2 = gen_linalg_ops.matrix_square_root(square2)
all_ops = [sqrt1, sqrt2]
sqrt = self.evaluate(all_ops)
self.assertAllClose(sqrt[0], sqrt[1])
def _verifySquareRoot(self, matrix, np_type):
matrix = matrix.astype(np_type)
# Verify that matmul(sqrtm(A), sqrtm(A)) = A
sqrt = gen_linalg_ops.matrix_square_root(matrix)
square = math_ops.matmul(sqrt, sqrt)
self.assertShapeEqual(matrix, square)
self.assertAllClose(matrix, square, rtol=1e-4, atol=1e-3)
def _verifySquareRoot(self, matrix, np_type):
matrix = matrix.astype(np_type)
with self.test_session(use_gpu=True):
# Verify that matmul(sqrtm(A), sqrtm(A)) = A
sqrt = gen_linalg_ops.matrix_square_root(matrix)
square = math_ops.matmul(sqrt, sqrt)
self.assertShapeEqual(matrix, square)
self.assertAllClose(matrix, square, rtol=1e-4, atol=1e-3)
def testWrongDimensions(self):
# The input to the square root should be at least a 2-dimensional tensor.
tensor = constant_op.constant([1., 2.])
with self.assertRaises(ValueError):
gen_linalg_ops.matrix_square_root(tensor)
def testNotSquare(self):
with self.assertRaises(ValueError):
tensor = constant_op.constant([[1., 0., -1.], [-1., 1., 0.]])
self.evaluate(gen_linalg_ops.matrix_square_root(tensor))
def testNotSquare(self):
with self.test_session():
with self.assertRaises(ValueError):
tensor = constant_op.constant([[1., 0., -1.], [-1., 1., 0.]])
gen_linalg_ops.matrix_square_root(tensor).eval()
def testWrongDimensions(self):
# The input to the square root should be at least a 2-dimensional tensor.
tensor = constant_op.constant([1., 2.])
with self.assertRaises(ValueError):
gen_linalg_ops.matrix_square_root(tensor)