def testL2NormalizeDimArray(self): x_shape = [20, 7, 3] np.random.seed(1) x_np = np.random.random_sample(x_shape).astype(np.float32) dim = [1, 2] y_np = self._l2Normalize(x_np, dim) x_tf = constant_op.constant(x_np, name="x") y_tf = nn_impl.l2_normalize_v2(x_tf, dim) self.assertAllClose(y_np, self.evaluate(y_tf))
def testL2NormalizeDimArray(self): x_shape = [20, 7, 3] np.random.seed(1) x_np = np.random.random_sample(x_shape).astype(np.float32) dim = [1, 2] y_np = self._l2Normalize(x_np, dim) x_tf = constant_op.constant(x_np, name="x") y_tf = nn_impl.l2_normalize_v2(x_tf, dim) self.assertAllClose(y_np, self.evaluate(y_tf))
def testL2NormalizeGradient(self):
x_shape = [20, 7, 3]
np.random.seed(1)
x_np = np.random.random_sample(x_shape).astype(np.float64)
for dim in range(len(x_shape)):
with self.cached_session():
x_tf = constant_op.constant(x_np, name="x")
y_tf = nn_impl.l2_normalize_v2(x_tf, dim)
err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf,
x_shape)
print("L2Normalize gradient err = %g " % err)
self.assertLess(err, 1e-4)
def testL2NormalizeGradient(self):
x_shape = [20, 7, 3]
np.random.seed(1)
x_np = np.random.random_sample(x_shape).astype(np.float64)
for dim in range(len(x_shape)):
with self.cached_session():
x_tf = constant_op.constant(x_np, name="x")
y_tf = nn_impl.l2_normalize_v2(x_tf, dim)
err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf,
x_shape)
print("L2Normalize gradient err = %g " % err)
self.assertLess(err, 1e-4)