Removed fixed seed and increased learning rate and tolerance for test…

…_nadam (#12164)

tests/python/unittest/test_optimizer.py

@@ -943,7 +943,7 @@ def test_ftrl():
         compare_optimizer(opt1(lazy_update=True, **kwarg), opt2(**kwarg), shape,
                           np.float32, w_stype='row_sparse', g_stype='row_sparse')
 
-@with_seed(1234)
+@with_seed()
 def test_nadam():
 
     def get_net(num_hidden, flatten=True):
@@ -965,10 +965,10 @@ def get_net(num_hidden, flatten=True):
     loss = Loss(output, l)
     loss = mx.sym.make_loss(loss)
     mod = mx.mod.Module(loss, data_names=('data',), label_names=('label',))
-    mod.fit(data_iter, num_epoch=60, optimizer_params={'learning_rate': 0.0005, 'wd': 0.0005},
+    mod.fit(data_iter, num_epoch=60, optimizer_params={'learning_rate': 0.001, 'wd': 0.0005},
             initializer=mx.init.Xavier(magnitude=2), eval_metric=mx.metric.Loss(),
             optimizer='nadam')
-    assert mod.score(data_iter, eval_metric=mx.metric.Loss())[0][1] < 0.1
+    assert mod.score(data_iter, eval_metric=mx.metric.Loss())[0][1] < 0.11
 
 # AdaGrad
 class PyAdaGrad(mx.optimizer.Optimizer):