本文整理汇总了Python中tensorflow.python.ops.gradients.hessians函数的典型用法代码示例。如果您正苦于以下问题：Python hessians函数的具体用法？Python hessians怎么用？Python hessians使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了hessians函数的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: testHessianInvalidDimension
def testHessianInvalidDimension(self):
for shape in [(10, 10), None]:
with self.test_session(use_gpu=True):
x = array_ops.placeholder(tf.float32, shape)
# Expect a ValueError because the dimensions are wrong
with self.assertRaises(ValueError):
gradients.hessians(x, x)示例2: testHessian1D
def testHessian1D(self):
# Manually compute the Hessian explicitly for a low-dimensional problem
# and check that `hessian` matches. Specifically, the Hessian of
# f(x) = x^T A x is H = A + A^T.
m = 4
rng = np.random.RandomState([1, 2, 3])
mat_value = rng.randn(m, m).astype("float32")
x_value = rng.randn(m).astype("float32")
hess_value = mat_value + mat_value.T
with self.test_session(use_gpu=True):
mat = constant_op.constant(mat_value)
x = constant_op.constant(x_value)
x_mat_x = math_ops.reduce_sum(x[:, None] * mat * x[None, :])
hess = gradients.hessians(x_mat_x, x)[0]
hess_actual = hess.eval()
self.assertAllClose(hess_value, hess_actual)示例3: testHessian1D_multi
def testHessian1D_multi(self):
# Test the computation of the hessian with respect to multiple tensors
m = 4
n = 3
rng = np.random.RandomState([1, 2, 3])
mat_values = [rng.randn(m, m).astype("float32") for _ in range(n)]
x_values = [rng.randn(m).astype("float32") for _ in range(n)]
hess_values = [mat_value + mat_value.T for mat_value in mat_values]
with self.test_session(use_gpu=True):
mats = [constant_op.constant(mat_value) for mat_value in mat_values]
xs = [constant_op.constant(x_value) for x_value in x_values]
xs_mats_xs = [math_ops.reduce_sum(x[:, None] * mat * x[None, :]) for x, mat in zip(xs, mats)]
hessians = gradients.hessians(xs_mats_xs, xs)
hessians_actual = [hess.eval() for hess in hessians]
for hess_value, hess_actual in zip(hess_values, hessians_actual):
self.assertAllClose(hess_value, hess_actual)示例4: testHessian2D_non_square_matrix
def testHessian2D_non_square_matrix(self):
m = 3
n = 4
rng = np.random.RandomState([1, 2, 3])
x_value = rng.randn(m, n).astype("float32")
with self.test_session(use_gpu=True):
x = constant_op.constant(x_value)
x_square = math_ops.reduce_sum(
math_ops.matmul(array_ops.transpose(x), x) * 0.5
)
hess = gradients.hessians(x_square, x)[0]
hess_actual = hess.eval()
hess_value = np.bmat([
[elem*np.ones((n, n)) for elem in vec]
for vec in np.eye(m)
]).astype("float32")
self.assertAllEqual((m, n, m, n), hess_actual.shape)
self.assertAllClose(hess_value, hess_actual.reshape((m * n, m * n)))示例5: testHessian2D_square_matrix
def testHessian2D_square_matrix(self):
# Manually compute the Hessian explicitly for a low-dimensional problem
# and check that `hessian` matches. Specifically, the Hessian of
# f(x) = 1/2 * x^T * x is H = constant (block identity matrix)
m = 3
rng = np.random.RandomState([1, 2, 3])
x_value = rng.randn(m, m).astype("float32")
with self.test_session(use_gpu=True):
x = constant_op.constant(x_value)
x_square = math_ops.reduce_sum(
math_ops.matmul(array_ops.transpose(x), x) * 0.5
)
hess = gradients.hessians(x_square, x)[0]
hess_actual = hess.eval()
hess_value = np.bmat([
[elem*np.ones((m, m)) for elem in vec]
for vec in np.eye(m)
]).astype("float32")
self.assertAllEqual((m, m, m, m), hess_actual.shape)
self.assertAllClose(hess_value, hess_actual.reshape((m * m, m * m)))