本文整理汇总了Python中tensorflow.python.ops.gen_nn_ops._batch_norm_with_global_normalization_grad函数的典型用法代码示例。如果您正苦于以下问题：Python _batch_norm_with_global_normalization_grad函数的具体用法？Python _batch_norm_with_global_normalization_grad怎么用？Python _batch_norm_with_global_normalization_grad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_batch_norm_with_global_normalization_grad函数的2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: _BatchNormWithGlobalNormalizationGrad
def _BatchNormWithGlobalNormalizationGrad(op, grad):
"""Return the gradients for the 5 inputs of BatchNormWithGlobalNormalization.
We do not backprop anything for the mean and var intentionally as they are
not being trained with backprop in the operation.
Args:
op: The BatchNormOp for which we need to generate gradients.
grad: Tensor. The gradients passed to the BatchNormOp.
Returns:
dx: Backprop for input, which is (grad * (g * rsqrt(v + epsilon)))
dm: Backprop for mean, which is
sum_over_rest(grad * g) * (-1 / rsqrt(v + epsilon))
dv: Backprop for variance, which is
sum_over_rest(grad * g * (x - m)) * (-1/2) * (v + epsilon) ^ (-3/2)
db: Backprop for beta, which is grad reduced in all except the
last dimension.
dg: Backprop for gamma, which is (grad * ((x - m) * rsqrt(v + epsilon)))
"""
dx, dm, dv, db, dg = gen_nn_ops._batch_norm_with_global_normalization_grad(
op.inputs[0],
op.inputs[1],
op.inputs[2],
op.inputs[4],
grad,
op.get_attr("variance_epsilon"),
op.get_attr("scale_after_normalization"),
)
return dx, dm, dv, db, dg示例2: testBatchNormGradImpl
def testBatchNormGradImpl(self):
x_shape = [7, 5, 4, 6]
param_shape = [6]
np.random.seed(1) # Make it reproducible.
x_val = np.random.random_sample(x_shape).astype(np.float32)
m_val = np.random.random_sample(param_shape).astype(np.float32)
v_val = np.random.random_sample(param_shape).astype(np.float32)
beta_val = np.random.random_sample(param_shape).astype(np.float32)
gamma_val = np.random.random_sample(param_shape).astype(np.float32)
backprop_val = np.random.random_sample(x_shape).astype(np.float32)
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu) as sess:
x = constant_op.constant(x_val, name="x")
m = constant_op.constant(m_val, name="m")
v = constant_op.constant(v_val, name="v")
beta = constant_op.constant(beta_val, name="beta")
gamma = constant_op.constant(gamma_val, name="gamma")
backprop = constant_op.constant(backprop_val, name="backprop")
epsilon = 0.001
for scale_after_normalization in [True, False]:
dx, dm, dv, db, dg = gen_nn_ops._batch_norm_with_global_normalization_grad(
x, m, v, gamma, backprop, epsilon, scale_after_normalization
)
on = self._opsBatchNorm(x, m, v, beta, gamma, epsilon, scale_after_normalization)
odx, odm, odv, odb, odg = gradients.gradients([on], [x, m, v, beta, gamma], [backprop])
if scale_after_normalization:
all_grads = sess.run([dx, dm, dv, db, dg, odx, odm, odv, odb, odg])
to_check = ["dx", "dm", "dv", "db", "dg"]
else:
all_grads = sess.run([dx, dm, dv, db, odx, odm, odv, odb])
to_check = ["dx", "dm", "dv", "db"]
for i, n in enumerate(to_check):
print(n)
self.assertAllClose(all_grads[i + len(to_check)], all_grads[i], atol=0.000001)