# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for tensorflow.ops.clip_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class ClipTest(tf.test.TestCase):
# ClipByValue test
def testClipByValue(self):
with self.test_session():
x = tf.constant([-5.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3])
np_ans = [[-4.4, 2.0, 3.0],
[4.0, 4.4, 4.4]]
clip_value = 4.4
ans = tf.clip_by_value(x, -clip_value, clip_value)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByValueNonFinite(self):
with self.test_session():
x = tf.constant([float('NaN'), float('Inf'), -float('Inf')])
np_ans = [float('NaN'), 4.0, -4.0]
clip_value = 4.0
ans = tf.clip_by_value(x, -clip_value, clip_value)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
# ClipByNorm tests
def testClipByNormClipped(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Norm of x = sqrt(3^2 + 4^2) = 5
np_ans = [[-2.4, 0.0, 0.0],
[3.2, 0.0, 0.0]]
clip_norm = 4.0
ans = tf.clip_by_norm(x, clip_norm)
tf_ans = ans.eval()
clip_tensor = tf.constant(4.0)
ans = tf.clip_by_norm(x, clip_norm)
tf_ans_tensor = ans.eval()
self.assertAllClose(np_ans, tf_ans)
self.assertAllClose(np_ans, tf_ans_tensor)
def testClipByNormNotClipped(self):
# No norm clipping when clip_norm >= 5
with self.test_session():
x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Norm of x = sqrt(3^2 + 4^2) = 5
np_ans = [[-3.0, 0.0, 0.0],
[4.0, 0.0, 0.0]]
clip_norm = 6.0
ans = tf.clip_by_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByNormZero(self):
# No norm clipping when norm = 0
with self.test_session():
x = tf.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
# Norm = 0, no changes
np_ans = [[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]]
clip_norm = 6.0
ans = tf.clip_by_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByNormClippedWithDim0(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 3.0], shape=[2, 3])
# Norm of x[:, 0] = sqrt(3^2 + 4^2) = 5, x[:, 2] = 3
np_ans = [[-2.4, 0.0, 0.0],
[3.2, 0.0, 3.0]]
clip_norm = 4.0
ans = tf.clip_by_norm(x, clip_norm, [0])
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByNormClippedWithDim1(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 3.0], shape=[2, 3])
# Norm of x[0, :] = 3, x[1, :] = sqrt(3^2 + 4^2) = 5
np_ans = [[-3.0, 0.0, 0.0],
[3.2, 0.0, 2.4]]
clip_norm = 4.0
ans = tf.clip_by_norm(x, clip_norm, [1])
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByNormNotClippedWithAxes(self):
# No norm clipping when clip_norm >= 5
with self.test_session():
x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 3.0], shape=[2, 3])
# Norm of x[0, :] = 3, x[1, :] = sqrt(3^2 + 4^2) = 5
np_ans = [[-3.0, 0.0, 0.0],
[4.0, 0.0, 3.0]]
clip_norm = 6.0
ans = tf.clip_by_norm(x, clip_norm, [1])
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
# ClipByGlobalNorm tests
def testClipByGlobalNormClipped(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.constant([1.0, -2.0])
# Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
clip_norm = 4.0
# Answers are the original tensors scaled by 4.0/5.0
np_ans_0 = [[-1.6, 0.0, 0.0],
[3.2, 0.0, 0.0]]
np_ans_1 = [0.8, -1.6]
ans, norm = tf.clip_by_global_norm((x0, x1), clip_norm)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[1].eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 5.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByGlobalNormClippedTensor(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.constant([1.0, -2.0])
# Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
clip_norm = tf.constant(4.0)
# Answers are the original tensors scaled by 4.0/5.0
np_ans_0 = [[-1.6, 0.0, 0.0],
[3.2, 0.0, 0.0]]
np_ans_1 = [0.8, -1.6]
ans, norm = tf.clip_by_global_norm((x0, x1), clip_norm)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[1].eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 5.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByGlobalNormSupportsNone(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.constant([1.0, -2.0])
# Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
clip_norm = 4.0
# Answers are the original tensors scaled by 4.0/5.0
np_ans_0 = [[-1.6, 0.0, 0.0],
[3.2, 0.0, 0.0]]
np_ans_1 = [0.8, -1.6]
ans, norm = tf.clip_by_global_norm((x0, None, x1, None), clip_norm)
self.assertTrue(ans[1] is None)
self.assertTrue(ans[3] is None)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[2].eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 5.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByGlobalNormWithIndexedSlicesClipped(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.IndexedSlices(tf.constant([1.0, -2.0]),
tf.constant([3, 4]))
# Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
clip_norm = 4.0
# Answers are the original tensors scaled by 4.0/5.0
np_ans_0 = [[-1.6, 0.0, 0.0],
[3.2, 0.0, 0.0]]
np_ans_1 = [0.8, -1.6]
ans, norm = tf.clip_by_global_norm([x0, x1], clip_norm)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[1].values.eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 5.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByGlobalNormPreservesDenseShape(self):
dense_shape = (1,)
slices = tf.IndexedSlices(
tf.constant([1.0]),
tf.constant([0]),
dense_shape=dense_shape)
ans, _ = tf.clip_by_global_norm([slices], 1.0)
modified_slices = ans[0]
self.assertEqual(dense_shape, slices.dense_shape)
self.assertEqual(dense_shape, modified_slices.dense_shape)
def testClipByGlobalNormNotClipped(self):
# No norm clipping when clip_norm >= 5
with self.test_session():
x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.constant([1.0, -2.0])
# Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
np_ans_0 = [[-2.0, 0.0, 0.0],
[4.0, 0.0, 0.0]]
np_ans_1 = [1.0, -2.0]
clip_norm = 6.0
ans, norm = tf.clip_by_global_norm([x0, x1], clip_norm)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[1].eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 5.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByGlobalNormZero(self):
# No norm clipping when norm = 0
with self.test_session():
x0 = tf.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.constant([0.0, 0.0])
# Norm = 0, no changes
np_ans_0 = [[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]]
np_ans_1 = [0.0, 0.0]
clip_norm = 6.0
ans, norm = tf.clip_by_global_norm([x0, x1], clip_norm)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[1].eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 0.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByAverageNormClipped(self):
# Norm clipping when average clip_norm < 0.83333333
with self.test_session():
x = tf.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 = 0.8
ans = tf.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():
x = tf.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 = tf.constant(0.8)
ans = tf.clip_by_average_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByAverageNormNotClipped(self):
# No norm clipping when average clip_norm >= 0.83333333
with self.test_session():
x = tf.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 = tf.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():
x = tf.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 = tf.clip_by_average_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
if __name__ == "__main__":
tf.test.main()