[incubator-mxnet] branch master updated: lower learning rate (#11467)

[ha...@apache.org]

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haibin pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git


The following commit(s) were added to refs/heads/master by this push:
     new a7d858d  lower learning rate (#11467)
a7d858d is described below

commit a7d858db95dbb6f595d22a2cab87644bb81f5f12
Author: Alexander Zai <az...@gmail.com>
AuthorDate: Thu Jun 28 13:46:34 2018 -0700

    lower learning rate (#11467)
---
 tests/python/unittest/test_loss.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/tests/python/unittest/test_loss.py b/tests/python/unittest/test_loss.py
index 14c4f6b..8d5b863 100644
--- a/tests/python/unittest/test_loss.py
+++ b/tests/python/unittest/test_loss.py
@@ -180,8 +180,7 @@ def test_l1_loss():
     assert mod.score(data_iter, eval_metric=mx.metric.Loss())[0][1] < 0.1
 
 
-@unittest.skip("flaky test. https://github.com/apache/incubator-mxnet/issues/8892")
-@with_seed()
+@with_seed(1234)
 def test_ctc_loss():
     loss = gluon.loss.CTCLoss()
     l = loss(mx.nd.ones((2,20,4)), mx.nd.array([[1,0,-1,-1],[2,1,1,-1]]))
@@ -207,7 +206,7 @@ def test_ctc_loss():
     l = loss(mx.nd.ones((2,25,4)), mx.nd.array([[2,1,3,3],[3,2,2,3]]), mx.nd.array([20,20]), mx.nd.array([2,3]))
     mx.test_utils.assert_almost_equal(l.asnumpy(), np.array([18.82820702, 16.50581741]))
 
-@unittest.skip("flaky test. https://github.com/apache/incubator-mxnet/issues/8892")
+
 @with_seed(1234)
 def test_ctc_loss_train():
     N = 20
@@ -220,7 +219,7 @@ def test_ctc_loss_train():
     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=200, optimizer_params={'learning_rate': 1.},
+    mod.fit(data_iter, num_epoch=200, optimizer_params={'learning_rate': 0.01},
             initializer=mx.init.Xavier(magnitude=2), eval_metric=mx.metric.Loss(),
             optimizer='adam')
     assert mod.score(data_iter, eval_metric=mx.metric.Loss())[0][1] < 10