add end index marker on loss

main.py

@@ -147,7 +147,7 @@ def model_init(n_hidden,vocab_size, embed_dim, max_seq_length, embed_weights, ct
     model.embedding.weight.set_data(embed_weights)
 
     trainer = gluon.Trainer(model.collect_params(), 'rmsprop')
-    loss = SoftmaxCrossEntropyLossMask(axis = 2)
+    loss = SoftmaxCrossEntropyLossMask(end_idx, axis = 2)
     return(model, loss, trainer)
 
 
@@ -290,7 +290,7 @@ def train(epochs, tr_data_iterator, model, loss, trainer, ctx, start_epoch=1, md
     else:
         print("train start from '{}'".format(opt.init_model))
         model.load_params(opt.init_model, ctx=ctx)
-        trainer_sgd = gluon.Trainer(model.collect_params(), 'sgd', optimizer_params={'learning_rate':0.01,},  kvstore='device')
+        trainer_sgd = gluon.Trainer(model.collect_params(), 'sgd', optimizer_params={'learning_rate':0.1,}, kvstore='local')
         tr_loss, te_loss = train(5, tr_data_iterator, model, loss, trainer_sgd, ctx=ctx, mdl_desc=opt.model_prefix, decay=True)
 
 if opt.test:

mask_loss.py

@@ -2,16 +2,18 @@
 
 
 class SoftmaxCrossEntropyLossMask(Loss):
-    def __init__(self, axis=-1, sparse_label=True, from_logits=False, weight=None,
+    def __init__(self, end_idx, axis=-1, sparse_label=True, from_logits=False, weight=None,
                  batch_axis=0, **kwargs):
         super(SoftmaxCrossEntropyLossMask, self).__init__(weight, batch_axis, **kwargs)
         self._axis = axis
         self._sparse_label = sparse_label
         self._from_logits = from_logits
+        self.end_idx = end_idx
 
     def hybrid_forward(self, F, pred, label, sample_weight=None):
+        #각 label 문장의 마지막 문자('END') 인덱스 정보 추출 
         label = F.cast(label, dtype='float32')
-        label_sent_length = F.argmax(F.where(label == 2.0, F.ones_like(label), F.zeros_like(label)), axis=1)
+        label_sent_length = F.argmax(F.where(label == self.end_idx, F.ones_like(label), F.zeros_like(label)), axis=1)
 
 
         if not self._from_logits:
@@ -28,3 +30,6 @@ def hybrid_forward(self, F, pred, label, sample_weight=None):
         loss = F.SequenceMask(loss, sequence_length=label_sent_length + 1, use_sequence_length=True)
         loss = F.transpose(loss, (1,0, 2))
         return F.sum(loss, axis=self._batch_axis, exclude=True)/(label_sent_length + 1)
+
+
+