diff options
Diffstat (limited to 'tensorflow/python/kernel_tests/reduction_ops_test.py')
| -rw-r--r-- | tensorflow/python/kernel_tests/reduction_ops_test.py | 45 |
1 files changed, 25 insertions, 20 deletions
diff --git a/tensorflow/python/kernel_tests/reduction_ops_test.py b/tensorflow/python/kernel_tests/reduction_ops_test.py index dd96446662..cb013dee00 100644 --- a/tensorflow/python/kernel_tests/reduction_ops_test.py +++ b/tensorflow/python/kernel_tests/reduction_ops_test.py @@ -432,16 +432,13 @@ class ProdReductionTest(tf.test.TestCase): self._compareAll(np_arr, [0, 2]) self._compareAll(np_arr, [0, 1, 2]) - def testGradient(self): - s = [2, 3, 4, 2] - # NOTE(kearnes): divide by 20 so product is a reasonable size - x = np.arange(1.0, 49.0).reshape(s).astype(np.float32) / 20. + def _compareGradient(self, x): with self.test_session(): t = tf.convert_to_tensor(x) su = tf.reduce_prod(t, []) jacob_t, jacob_n = tf.test.compute_gradient(t, - s, + x.shape, su, [2, 3, 4, 2], x_init_value=x, @@ -450,7 +447,7 @@ class ProdReductionTest(tf.test.TestCase): su = tf.reduce_prod(t, [1, 2]) jacob_t, jacob_n = tf.test.compute_gradient(t, - s, + x.shape, su, [2, 2], x_init_value=x, @@ -459,26 +456,34 @@ class ProdReductionTest(tf.test.TestCase): su = tf.reduce_prod(t, [0, 1, 2, 3]) jacob_t, jacob_n = tf.test.compute_gradient(t, - s, + x.shape, su, [1], x_init_value=x, delta=1) self.assertAllClose(jacob_t, jacob_n, rtol=1e-3, atol=1e-3) - # NOTE(kearnes): the current gradient calculation gives NaNs for 0 inputs - x = np.arange(0.0, 48.0).reshape(s).astype(np.float32) / 20. - with self.test_session(): - t = tf.convert_to_tensor(x) - su = tf.reduce_prod(t, []) - jacob_t, _ = tf.test.compute_gradient(t, - s, - su, - [2, 3, 4, 2], - x_init_value=x, - delta=1) - with self.assertRaisesOpError("Tensor had NaN values"): - tf.check_numerics(jacob_t, message="_ProdGrad NaN test").op.run() + def testGradientWithZeros(self): + s = [2, 3, 4, 2] + x = np.arange(1.0, 49.0).reshape(s).astype(np.float32) / 20. + # No zeros in input + self._compareGradient(x) + # Zero at beginning + x1 = x.copy() + x1[:,:,0,:] = 0 + self._compareGradient(x1) + # Zero at end + x2 = x.copy() + x2[:,:,-1,:] = 0 + self._compareGradient(x2) + # Zero in middle + x3 = x.copy() + x3[:,:,2,:] = 0 + self._compareGradient(x3) + # All zeros + x4 = x.copy() + x4[:,:,:,:] = 0 + self._compareGradient(x4) def testEmptyGradients(self): with self.test_session(): |