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
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.cached_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]:
# _batch_norm_with_global_normalization_grad is deprecated in v9
test_util.set_producer_version(ops.get_default_graph(), 8)
grad = gen_nn_ops.batch_norm_with_global_normalization_grad(
x, m, v, gamma, backprop, epsilon,
scale_after_normalization)
dx, dm, dv, db, dg = grad
self.assertEqual(grad.dx, dx)
self.assertEqual(grad.dm, dm)
self.assertEqual(grad.dv, dv)
self.assertEqual(grad.db, db)
self.assertEqual(grad.dg, dg)
on = self._opsBatchNorm(x, m, v, beta, gamma, epsilon,
scale_after_normalization, True)
odx, odm, odv, odb, odg = gradients_impl.gradients(
[on], [x, m, v, beta, gamma], [backprop])
if scale_after_normalization:
all_grads = self.evaluate(
[dx, dm, dv, db, dg, odx, odm, odv, odb, odg])
to_check = ["dx", "dm", "dv", "db", "dg"]
else:
all_grads = self.evaluate(
[dx, dm, dv, db, odx, odm, odv, odb])
to_check = ["dx", "dm", "dv", "db"]
for i, _ in enumerate(to_check):
self.assertAllClose(all_grads[i + len(to_check)],
all_grads[i],
atol=0.000001)
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.cached_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]:
# _batch_norm_with_global_normalization_grad is deprecated in v9
test_util.set_producer_version(ops.get_default_graph(), 8)
grad = gen_nn_ops.batch_norm_with_global_normalization_grad(
x, m, v, gamma, backprop, epsilon, scale_after_normalization)
dx, dm, dv, db, dg = grad
self.assertEqual(grad.dx, dx)
self.assertEqual(grad.dm, dm)
self.assertEqual(grad.dv, dv)
self.assertEqual(grad.db, db)
self.assertEqual(grad.dg, dg)
on = self._opsBatchNorm(x, m, v, beta, gamma, epsilon,
scale_after_normalization, True)
odx, odm, odv, odb, odg = gradients_impl.gradients(
[on], [x, m, v, beta, gamma], [backprop])
if scale_after_normalization:
all_grads = self.evaluate(
[dx, dm, dv, db, dg, odx, odm, odv, odb, odg])
to_check = ["dx", "dm", "dv", "db", "dg"]
else:
all_grads = self.evaluate([dx, dm, dv, db, odx, odm, odv, odb])
to_check = ["dx", "dm", "dv", "db"]
for i, _ in enumerate(to_check):
self.assertAllClose(
all_grads[i + len(to_check)], all_grads[i], atol=0.000001)
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
