本文整理汇总了Python中tensorflow.python.eager.backprop.gradients_function函数的典型用法代码示例。如果您正苦于以下问题：Python gradients_function函数的具体用法？Python gradients_function怎么用？Python gradients_function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gradients_function函数的15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: testErrors
def testErrors(self):
@custom_gradient.custom_gradient
def f(x):
def grad(_):
raise RuntimeError('x')
return x, grad
# TODO(apassos) raise the right error here
with self.assertRaises(RuntimeError):
backprop.gradients_function(f)(constant_op.constant(1.0))示例2: testExceptionSafety
def testExceptionSafety(self):
def f(unused_x):
raise ValueError()
try:
backprop.gradients_function(f)(1.0)
except ValueError:
pass
def real_f(x):
return x * x
self.assertAllEqual(backprop.gradients_function(real_f)(1.0)[0], 2.0)示例3: testDifferentiableFunctionNoneOutputs
def testDifferentiableFunctionNoneOutputs(self):
@function.defun
def my_function(x):
return x, None
def wrapper(x):
return my_function(x)[0]
g = backprop.gradients_function(wrapper, [0])(constant_op.constant(0.0))
self.assertAllEqual(g[0], 1.)
@function.defun
def foo(a):
return None, a * a
x = constant_op.constant(5.0)
with backprop.GradientTape() as tp:
tp.watch(x)
none, r = foo(x)
g = tp.gradient(r, x)
self.assertIs(none, None)
self.assertAllEqual(r, 25.0)
self.assertAllEqual(g, 2 * 5.0)示例4: testAggregateGradients
def testAggregateGradients(self):
def fn(x):
ind1 = constant_op.constant(np.array([0, 1]))
ind2 = constant_op.constant(np.array([2, 3]))
ind3 = constant_op.constant(np.array([1, 3]))
# A mixture of IndexedSlices and dense tensor to aggregate.
g1 = embedding_ops.embedding_lookup(x, ind1)
g2 = embedding_ops.embedding_lookup(x, ind2)
g3 = embedding_ops.embedding_lookup(x, ind3)
g4 = math_ops.reduce_sum(x * constant_op.constant(2.0))
return g1 * g2 * g3 * g4
var_np = np.random.rand(4, 2).astype(np.float32)
var = constant_op.constant(var_np)
grad = backprop.gradients_function(fn, [0])(var)[0]
grad = self.evaluate(ops.convert_to_tensor(grad))
if not context.executing_eagerly():
tf_var = array_ops.constant(var_np, dtypes.float32)
tf_ind1 = array_ops.constant([0, 1])
tf_ind2 = array_ops.constant([2, 3])
tf_ind3 = array_ops.constant([1, 3])
tf_g1 = embedding_ops.embedding_lookup(tf_var, tf_ind1)
tf_g2 = embedding_ops.embedding_lookup(tf_var, tf_ind2)
tf_g3 = embedding_ops.embedding_lookup(tf_var, tf_ind3)
tf_g4 = math_ops.reduce_sum(tf_var * 2.0, axis=(0, 1))
tf_y = tf_g1 * tf_g2 * tf_g3 * tf_g4
tf_grad = gradients.gradients(tf_y, [tf_var])[0]
tf_dense_grad = math_ops.unsorted_segment_sum(
tf_grad.values, tf_grad.indices, tf_grad.dense_shape[0])
self.assertAllClose(grad, self.evaluate(tf_dense_grad))示例5: testDy
def testDy(self):
def f(x):
return x
grad_fn = backprop.gradients_function(f)
self.assertAllEqual(2., grad_fn(1., dy=2.)[0])示例6: testArgsGradientFunction
def testArgsGradientFunction(self):
def f(*args):
return args[0] * args[0]
grad = backprop.gradients_function(f)
self.assertAllEqual(grad(1.0)[0], 2.0)示例7: testArgmax
def testArgmax(self):
def argmax(x):
i = math_ops.argmax(x)
return array_ops.stop_gradient(i)
grad = backprop.gradients_function(argmax)
self.assertAllEqual(grad([0.0])[0], None)示例8: testGradient
def testGradient(self):
def f(x):
return x
with self.test_scope():
grad_fn = backprop.gradients_function(f)
self.assertAllEqual(2., grad_fn(1., dy=2.)[0])示例9: testAggregateGradients
def testAggregateGradients(self):
def fn(x):
ind1 = tensor.Tensor(np.array([0, 1]))
ind2 = tensor.Tensor(np.array([2, 3]))
ind3 = tensor.Tensor(np.array([1, 3]))
# A mixture of IndexedSlices and dense tensor to aggregate.
g1 = embedding_ops.embedding_lookup(x, ind1)
g2 = embedding_ops.embedding_lookup(x, ind2)
g3 = embedding_ops.embedding_lookup(x, ind3)
g4 = math_ops.reduce_sum(x * tensor.Tensor(2.0))
return g1 * g2 * g3 * g4
var_np = np.random.rand(4, 2).astype(np.float32)
var = tensor.Tensor(var_np)
grad = backprop.gradients_function(fn, [0])(var)[0]
with context.graph_mode(), self.test_session():
tf_var = array_ops.constant(var_np, dtypes.float32)
tf_ind1 = array_ops.constant([0, 1])
tf_ind2 = array_ops.constant([2, 3])
tf_ind3 = array_ops.constant([1, 3])
tf_g1 = embedding_ops.embedding_lookup(tf_var, tf_ind1)
tf_g2 = embedding_ops.embedding_lookup(tf_var, tf_ind2)
tf_g3 = embedding_ops.embedding_lookup(tf_var, tf_ind3)
tf_g4 = math_ops.reduce_sum(tf_var * 2.0, reduction_indices=(0, 1))
tf_y = tf_g1 * tf_g2 * tf_g3 * tf_g4
tf_grad = gradients.gradients(tf_y, [tf_var])[0]
tf_dense_grad = math_ops.unsorted_segment_sum(
tf_grad.values, tf_grad.indices, tf_grad.dense_shape[0])
self.assertAllClose(grad.numpy(), tf_dense_grad.eval())示例10: testGradientInteger
def testGradientInteger(self):
def f(x):
return x + x
int_tensor = constant_op.constant(1)
self.assertEqual(backprop.gradients_function(f)(int_tensor)[0], None)示例11: f
def f(x):
(y,) = backprop.gradients_function(lambda x: x * x)(x)
def grad(dy):
return [2 * dy]
return y, grad示例12: testMulType
def testMulType(self):
def mul(x):
return math_ops._mul_dispatch(x, x) # pylint: disable=protected-access
self.assertAllEqual(
backprop.gradients_function(mul)(3.0)[0].numpy(),
6.0)示例13: testTfTensor
def testTfTensor(self):
def fn(x):
return x
t = constant_op.constant(1.0)
g, = backprop.gradients_function(fn, [0])(t)
self.assertAllEqual(g, 1.0)示例14: testOutput
def testOutput(self):
def multiout(x):
return x + 2, x * x
x = constant_op.constant([0.0, 1.0, 2.0])
grad = backprop.gradients_function(multiout)(x)[0]
self.assertAllEqual([1.0, 3.0, 5.0], grad)示例15: testCPU
def testCPU(self):
def fn(x):
b = tensor.Tensor(2.0)
c = math_ops.add(x, b)
return math_ops.add(c, tensor.Tensor(3.0))
grad = backprop.gradients_function(fn, [0])(tensor.Tensor(1.0))[0]
self.assertEqual(grad.numpy(), 1.0)