def testClipByAverageNormNotClipped(self):
# No norm clipping when average clip_norm >= 0.83333333
with self.test_session(use_gpu=True):
x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
np_ans = [[-3.0, 0.0, 0.0], [4.0, 0.0, 0.0]]
clip_norm = 0.9
ans = clip_ops.clip_by_average_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByAverageNormZero(self):
# No norm clipping when average clip_norm = 0
with self.test_session(use_gpu=True):
x = constant_op.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
# Average norm = 0, no changes
np_ans = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
clip_norm = 0.9
ans = clip_ops.clip_by_average_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByAverageNormClippedTensor(self):
# Norm clipping when average clip_norm < 0.83333333
with self.test_session(use_gpu=True):
x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
np_ans = [[-2.88, 0.0, 0.0], [3.84, 0.0, 0.0]]
clip_norm = constant_op.constant(0.8)
ans = clip_ops.clip_by_average_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByAverageNormClippedTensor(self):
# Norm clipping when average clip_norm < 0.83333333
with self.session(use_gpu=True):
x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
np_ans = [[-2.88, 0.0, 0.0], [3.84, 0.0, 0.0]]
clip_norm = constant_op.constant(0.8)
ans = clip_ops.clip_by_average_norm(x, clip_norm)
tf_ans = self.evaluate(ans)
self.assertAllClose(np_ans, tf_ans)
def testClipByAverageNormZero(self):
# No norm clipping when average clip_norm = 0
with self.session():
x = constant_op.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
# Average norm = 0, no changes
np_ans = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
clip_norm = 0.9
ans = clip_ops.clip_by_average_norm(x, clip_norm)
tf_ans = self.evaluate(ans)
self.assertAllClose(np_ans, tf_ans)
def testClipByAverageNormNotClipped(self):
# No norm clipping when average clip_norm >= 0.83333333
with self.session():
x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
np_ans = [[-3.0, 0.0, 0.0], [4.0, 0.0, 0.0]]
clip_norm = 0.9
ans = clip_ops.clip_by_average_norm(x, clip_norm)
tf_ans = self.evaluate(ans)
self.assertAllClose(np_ans, tf_ans)
def testClipByAverageNormReplacedWithClipByNorm(self):
# Check clip_by_average_norm(t) is the same as
# clip_by_norm(t, clip_norm * tf.compat.v1.to_float(tf.size(t)))
with self.session(use_gpu=True):
x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
# expected answer [[-2.88, 0.0, 0.0], [3.84, 0.0, 0.0]]
clip_norm = constant_op.constant(0.8)
with_norm = clip_ops.clip_by_average_norm(x, clip_norm)
without_norm = clip_ops.clip_by_norm(
x, clip_norm * math_ops.cast(array_ops.size(x), dtypes.float32))
clip_by_average_norm_ans = self.evaluate(with_norm)
clip_by_norm_ans = self.evaluate(without_norm)
self.assertAllClose(clip_by_average_norm_ans, clip_by_norm_ans)
def testClipByAverageNormReplacedWithClipByNorm(self):
# Check clip_by_average_norm(t) is the same as
# clip_by_norm(t, clip_norm * tf.to_float(tf.size(t)))
with self.session(use_gpu=True):
x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
# expected answer [[-2.88, 0.0, 0.0], [3.84, 0.0, 0.0]]
clip_norm = constant_op.constant(0.8)
with_norm = clip_ops.clip_by_average_norm(x, clip_norm)
without_norm = clip_ops.clip_by_norm(
x, clip_norm * math_ops.to_float(array_ops.size(x)))
clip_by_average_norm_ans = self.evaluate(with_norm)
clip_by_norm_ans = self.evaluate(without_norm)
self.assertAllClose(clip_by_average_norm_ans, clip_by_norm_ans)
