def testNumbersWithAxis1(self):
tf_crelu = nn_ops.crelu(np.array([[-9, 7, -5, 3, -1],
[1, -3, 5, -7, 9]]),
axis=1)
np_crelu = np.array([[0, 7, 0, 3, 0, 9, 0, 5, 0, 1],
[1, 0, 5, 0, 9, 0, 3, 0, 7, 0]])
self.assertAllEqual(np_crelu, tf_crelu)
def test(self):
np.random.seed(1) # Make it reproducible.
x = np.random.randn(3, 4).astype(np.float32)
y = np.concatenate([x * (x > 0), -x * (x < 0)], axis=1)
with self.test_session():
z = nn_ops.crelu(constant_op.constant(x)).eval()
self.assertAllClose(y, z, 1e-4)
def test(self):
np.random.seed(1) # Make it reproducible.
x = np.random.randn(3, 4).astype(np.float32)
y = np.concatenate([x * (x > 0), -x * (x < 0)], axis=1)
with self.test_session():
z = nn_ops.crelu(constant_op.constant(x)).eval()
self.assertAllClose(y, z, 1e-4)
def testNumbersWithAxis1(self):
with self.cached_session():
crelu = nn_ops.crelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]), axis=1)
tf_relu = crelu.eval()
np_crelu = np.array([[0, 7, 0, 3, 0, 9, 0, 5, 0, 1],
[1, 0, 5, 0, 9, 0, 3, 0, 7, 0]])
self.assertAllEqual(np_crelu, tf_relu)
def testNumbersWithAxis1(self):
with self.test_session():
crelu = nn_ops.crelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]), axis=1)
tf_relu = crelu.eval()
np_crelu = np.array([[0, 7, 0, 3, 0, 9, 0, 5, 0, 1],
[1, 0, 5, 0, 9, 0, 3, 0, 7, 0]])
self.assertAllEqual(np_crelu, tf_relu)
def _testCrelu(self, np_features):
np_relu = np.maximum(np_features, np.zeros_like(np_features))
np_neg_relu = np.maximum(-np_features, np.zeros_like(np_features))
np_crelu = np.concatenate((np_relu, np_neg_relu),
len(np_features.shape) - 1)
tf_crelu = nn_ops.crelu(np_features)
self.assertAllClose(np_crelu, tf_crelu)
self.assertShapeEqual(np_crelu, tf_crelu)
def _testCrelu(self, np_features):
np_relu = np.maximum(np_features, np.zeros_like(np_features))
np_neg_relu = np.maximum(-np_features, np.zeros_like(np_features))
np_crelu = np.concatenate((np_relu, np_neg_relu),
len(np_features.shape) - 1)
tf_crelu = nn_ops.crelu(np_features)
self.assertAllClose(np_crelu, tf_crelu)
self.assertShapeEqual(np_crelu, tf_crelu)
def _testCrelu(self, np_features, use_gpu=False):
np_relu = np.maximum(np_features, np.zeros_like(np_features))
np_neg_relu = np.maximum(-np_features, np.zeros_like(np_features))
np_crelu = np.concatenate((np_relu, np_neg_relu),
len(np_features.shape) - 1)
with self.test_session(use_gpu=use_gpu):
crelu = nn_ops.crelu(np_features)
tf_relu = crelu.eval()
self.assertAllClose(np_crelu, tf_relu)
self.assertShapeEqual(np_crelu, crelu)
def _testCrelu(self, np_features, use_gpu=False):
np_relu = np.maximum(np_features, np.zeros_like(np_features))
np_neg_relu = np.maximum(-np_features, np.zeros_like(np_features))
np_crelu = np.concatenate((np_relu, np_neg_relu),
len(np_features.shape) - 1)
with self.test_session(use_gpu=use_gpu):
crelu = nn_ops.crelu(np_features)
tf_relu = crelu.eval()
self.assertAllClose(np_crelu, tf_relu)
self.assertShapeEqual(np_crelu, crelu)
def testCreluShape(self):
f = random_ops.random_normal([50, 5, 7, 10])
t = nn_ops.crelu(f)
self.assertEqual([50, 5, 7, 20], t.get_shape())
def testCreluShape(self): f = random_ops.random_normal([50, 5, 7, 10]) t = nn_ops.crelu(f) self.assertEqual([50, 5, 7, 20], t.get_shape())
def testNumbersWithAxis1(self):
tf_crelu = nn_ops.crelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]), axis=1)
np_crelu = np.array([[0, 7, 0, 3, 0, 9, 0, 5, 0, 1],
[1, 0, 5, 0, 9, 0, 3, 0, 7, 0]])
self.assertAllEqual(np_crelu, tf_crelu)