"""Tests for tensorflow.ops.clip_ops.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow.python.platform 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() self.assertAllClose(np_ans, tf_ans) 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 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 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) # ClipByGlobalNorm tests 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 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 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()