def testConcurrentExecutesWithoutError(self):
with self.session(use_gpu=True) as sess:
matrix1 = math_ops.cast(
random_ops.random_normal([5, 5], seed=42), dtypes.complex64)
matrix2 = math_ops.cast(
random_ops.random_normal([5, 5], seed=42), dtypes.complex64)
logm1 = gen_linalg_ops.matrix_logarithm(matrix1)
logm2 = gen_linalg_ops.matrix_logarithm(matrix2)
logm = sess.run([logm1, logm2])
self.assertAllEqual(logm[0], logm[1])
def testConcurrentExecutesWithoutError(self):
with self.session(use_gpu=True) as sess:
matrix1 = math_ops.cast(random_ops.random_normal([5, 5], seed=42),
dtypes.complex64)
matrix2 = math_ops.cast(random_ops.random_normal([5, 5], seed=42),
dtypes.complex64)
logm1 = gen_linalg_ops.matrix_logarithm(matrix1)
logm2 = gen_linalg_ops.matrix_logarithm(matrix2)
logm = self.evaluate([logm1, logm2])
self.assertAllEqual(logm[0], logm[1])
def testConcurrentExecutesWithoutError(self):
matrix_shape = [5, 5]
seed = [42, 24]
matrix1 = math_ops.cast(
stateless_random_ops.stateless_random_normal(matrix_shape, seed=seed),
dtypes.complex64)
matrix2 = math_ops.cast(
stateless_random_ops.stateless_random_normal(matrix_shape, seed=seed),
dtypes.complex64)
self.assertAllEqual(matrix1, matrix2)
logm1 = gen_linalg_ops.matrix_logarithm(matrix1)
logm2 = gen_linalg_ops.matrix_logarithm(matrix2)
logm = self.evaluate([logm1, logm2])
self.assertAllEqual(logm[0], logm[1])
def _verifyLogarithm(self, x, np_type):
inp = x.astype(np_type)
with test_util.use_gpu():
# Verify that expm(logm(A)) == A.
tf_ans = linalg_impl.matrix_exponential(
gen_linalg_ops.matrix_logarithm(inp))
out = self.evaluate(tf_ans)
self.assertAllClose(inp, out, rtol=1e-4, atol=1e-3)
def _verifyLogarithm(self, x, np_type):
inp = x.astype(np_type)
with self.cached_session(use_gpu=True):
# Verify that expm(logm(A)) == A.
tf_ans = linalg_impl.matrix_exponential(
gen_linalg_ops.matrix_logarithm(inp))
out = tf_ans.eval()
self.assertAllClose(inp, out, rtol=1e-4, atol=1e-3)
def _verifyLogarithm(self, x, np_type):
inp = x.astype(np_type)
with self.cached_session(use_gpu=True):
# Verify that expm(logm(A)) == A.
tf_ans = linalg_impl.matrix_exponential(
gen_linalg_ops.matrix_logarithm(inp))
out = tf_ans.eval()
self.assertAllClose(inp, out, rtol=1e-4, atol=1e-3)
def _verifyLogarithm(self, x, np_type):
inp = x.astype(np_type)
with test_util.use_gpu():
# Verify that expm(logm(A)) == A.
tf_ans = linalg_impl.matrix_exponential(
gen_linalg_ops.matrix_logarithm(inp))
out = self.evaluate(tf_ans)
self.assertAllClose(inp, out, rtol=1e-4, atol=1e-3)
def benchmarkMatrixLogarithmOp(self):
for shape in self.shapes:
with ops.Graph().as_default(), \
session.Session(config=benchmark.benchmark_config()) as sess, \
ops.device("/cpu:0"):
matrix = self._GenerateMatrix(shape)
logm = gen_linalg_ops.matrix_logarithm(matrix)
variables.global_variables_initializer().run()
self.run_op_benchmark(
sess,
control_flow_ops.group(logm),
min_iters=25,
name="matrix_logarithm_cpu_{shape}".format(shape=shape))
def benchmarkMatrixLogarithmOp(self):
for shape in self.shapes:
with ops.Graph().as_default(), \
session.Session(config=benchmark.benchmark_config()) as sess, \
ops.device("/cpu:0"):
matrix = self._GenerateMatrix(shape)
logm = gen_linalg_ops.matrix_logarithm(matrix)
variables.global_variables_initializer().run()
self.run_op_benchmark(
sess,
control_flow_ops.group(logm),
min_iters=25,
name="matrix_logarithm_cpu_{shape}".format(
shape=shape))
def testWrongDimensions(self):
# The input to the logarithm should be at least a 2-dimensional tensor.
tensor3 = constant_op.constant([1., 2.], dtype=dtypes.complex64)
with self.assertRaises(ValueError):
gen_linalg_ops.matrix_logarithm(tensor3)
def testNonSquareMatrix(self):
# When the logarithm of a non-square matrix is attempted we should return
# an error
with self.assertRaises(ValueError):
gen_linalg_ops.matrix_logarithm(
np.array([[1., 2., 3.], [3., 4., 5.]], dtype=np.complex64))
def testWrongDimensions(self):
# The input to the logarithm should be at least a 2-dimensional tensor.
tensor3 = constant_op.constant([1., 2.], dtype=dtypes.complex64)
with self.assertRaises(ValueError):
gen_linalg_ops.matrix_logarithm(tensor3)
def testNonSquareMatrix(self):
# When the logarithm of a non-square matrix is attempted we should return
# an error
with self.assertRaises(ValueError):
gen_linalg_ops.matrix_logarithm(
np.array([[1., 2., 3.], [3., 4., 5.]], dtype=np.complex64))