diff options
Diffstat (limited to 'tensorflow/python/kernel_tests/lrn_op_test.py')
| -rw-r--r-- | tensorflow/python/kernel_tests/lrn_op_test.py | 55 |
1 files changed, 26 insertions, 29 deletions
diff --git a/tensorflow/python/kernel_tests/lrn_op_test.py b/tensorflow/python/kernel_tests/lrn_op_test.py index 3d72e8ae35..b759c627d4 100644 --- a/tensorflow/python/kernel_tests/lrn_op_test.py +++ b/tensorflow/python/kernel_tests/lrn_op_test.py @@ -45,8 +45,8 @@ class LRNOpTest(tf.test.TestCase): np.power(bias + alpha * np.sum(patch * patch), beta)) return output - def _RunAndVerify(self, dtype, use_gpu): - with self.test_session(use_gpu=use_gpu): + def _RunAndVerify(self, dtype): + with self.test_session(): # random shape shape = np.random.randint(1, 16, size=4) # Make depth at least 2 to make it meaningful @@ -77,30 +77,28 @@ class LRNOpTest(tf.test.TestCase): self.assertShapeEqual(expected, lrn_t) def testCompute(self): - for use_gpu in (True, False): - for _ in range(2): - self._RunAndVerify(tf.float32, use_gpu) - # Enable when LRN supports tf.float16 on GPU. - if not use_gpu: - self._RunAndVerify(tf.float16, use_gpu) + for _ in range(2): + self._RunAndVerify(tf.float32) + # Enable when LRN supports tf.float16 on GPU. + if not tf.test.is_gpu_available(): + self._RunAndVerify(tf.float16) def testGradientsZeroInput(self): - for use_gpu in (True, False): - with self.test_session(use_gpu=use_gpu): - shape = [4, 4, 4, 4] - p = tf.placeholder(tf.float32, shape=shape) - inp_array = np.zeros(shape).astype("f") - lrn_op = tf.nn.local_response_normalization(p, 2, 1.0, 0.0, - 1.0, name="lrn") - grad = tf.gradients([lrn_op], [p])[0] - params = {p: inp_array} - r = grad.eval(feed_dict=params) - expected = np.ones(shape).astype("f") - self.assertAllClose(r, expected) - self.assertShapeEqual(expected, grad) + with self.test_session(): + shape = [4, 4, 4, 4] + p = tf.placeholder(tf.float32, shape=shape) + inp_array = np.zeros(shape).astype("f") + lrn_op = tf.nn.local_response_normalization(p, 2, 1.0, 0.0, + 1.0, name="lrn") + grad = tf.gradients([lrn_op], [p])[0] + params = {p: inp_array} + r = grad.eval(feed_dict=params) + expected = np.ones(shape).astype("f") + self.assertAllClose(r, expected) + self.assertShapeEqual(expected, grad) - def _RunAndVerifyGradients(self, dtype, use_gpu): - with self.test_session(use_gpu=use_gpu): + def _RunAndVerifyGradients(self, dtype): + with self.test_session(): # random shape shape = np.random.randint(1, 5, size=4) # Make depth at least 2 to make it meaningful @@ -133,12 +131,11 @@ class LRNOpTest(tf.test.TestCase): self.assertLess(err, 1.0) def testGradients(self): - for use_gpu in (True, False): - for _ in range(2): - self._RunAndVerifyGradients(tf.float32, use_gpu) - # Enable when LRN supports tf.float16 on GPU. - if not use_gpu: - self._RunAndVerifyGradients(tf.float16, use_gpu) + for _ in range(2): + self._RunAndVerifyGradients(tf.float32) + # Enable when LRN supports tf.float16 on GPU. + if not tf.test.is_gpu_available(): + self._RunAndVerifyGradients(tf.float16) if __name__ == "__main__": |