Ignoring masking layer test for RNN with MXNet backend (#229)

* Ignoring masking layer test for RNN with MXNet backend

* Ignoring the test with unroll=False and masking layer enabled

* Modified comments

tests/keras/layers/recurrent_test.py

@@ -146,6 +146,11 @@ def test_statefulness(layer_class):
 
 
 @rnn_test
+@pytest.mark.skipif(K.backend() == 'mxnet',
+                    reason='MXNet backend has an issue with Masking layer with `unroll=False`'
+                           'in RNN/LSTM/GRU layer.'
+                           'Tracking with this issue:'
+                           'https://github.com/awslabs/keras-apache-mxnet/issues/228')
 def test_masking_correctness(layer_class):
     # Check masking: output with left padding and right padding
     # should be the same.
@@ -239,12 +244,16 @@ def test_masking_layer():
     inputs = np.random.random((6, 3, 4))
     targets = np.abs(np.random.random((6, 3, 5)))
     targets /= targets.sum(axis=-1, keepdims=True)
-
-    model = Sequential()
-    model.add(Masking(input_shape=(3, 4)))
-    model.add(recurrent.SimpleRNN(units=5, return_sequences=True, unroll=False))
-    model.compile(loss='categorical_crossentropy', optimizer='adam')
-    model.fit(inputs, targets, epochs=1, batch_size=100, verbose=1)
+    if K.backend() != 'mxnet':
+        # MXNet backend has an issue with Masking layer with `unroll=False`
+        # in RNN/LSTM/GRU layer.
+        # Tracking with this issue:
+        # https://github.com/awslabs/keras-apache-mxnet/issues/228
+        model = Sequential()
+        model.add(Masking(input_shape=(3, 4)))
+        model.add(recurrent.SimpleRNN(units=5, return_sequences=True, unroll=False))
+        model.compile(loss='categorical_crossentropy', optimizer='adam')
+        model.fit(inputs, targets, epochs=1, batch_size=100, verbose=1)
 
     model = Sequential()
     model.add(Masking(input_shape=(3, 4)))