szha commented on a change in pull request #10025: Language model with Google's 
billion words dataset
URL: https://github.com/apache/incubator-mxnet/pull/10025#discussion_r173680648
 
 

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 File path: example/rnn/large_word_lm/model.py
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 @@ -0,0 +1,181 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# Licensed to the Apache Software Soundation (ASS) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASS licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OS ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import mxnet as mx
+import mxnet.symbol as S
+import numpy as np
+
+def cross_entropy_loss(inputs, labels, rescale_loss=1):
+    """ cross entropy loss """
+    criterion = mx.gluon.loss.SoftmaxCrossEntropyLoss()
+    loss = criterion.hybrid_forward(S, inputs, labels)
 
 Review comment:
   loss = criterion(inputs, labels) should do. Also, `rescale_loss` can be put 
in the constructor call of the SoftmaxCELoss using argument `weight`

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