[GitHub] anirudhacharya commented on a change in pull request #12263: [MXNET-628] Fix example on text classification using LSTMs on IMDB dataset

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anirudhacharya commented on a change in pull request #12263: [MXNET-628] Fix example on text classification using LSTMs on IMDB dataset
URL: https://github.com/apache/incubator-mxnet/pull/12263#discussion_r211430732
 
 

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 File path: example/rnn/bucket_R/aclImdb_lstm_classification.R
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 @@ -40,20 +40,31 @@ optimizer <- mx.opt.create("adadelta", rho = 0.92, epsilon = 1e-06, wd = 2e-04,
 bucket_list <- unique(c(train.data$bucket.names, eval.data$bucket.names))
 
 symbol_buckets <- sapply(bucket_list, function(seq) {
-  rnn.graph(config = "seq-to-one", cell_type = "lstm", 
-            num_rnn_layer = 1, num_embed = 2, num_hidden = 6, 
-            num_decode = 2, input_size = vocab, dropout = 0.5, 
-            ignore_label = -1, loss_output = "softmax",
-            output_last_state = F, masking = T)
+  rnn.graph(config = "seq-to-one",
+            cell_type = "lstm",
+            num_rnn_layer = 1,
+            num_embed = 2,
+            num_hidden = 6,
+            num_decode = 2,
+            input_size = vocab,
+            dropout = 0.2,
+            ignore_label = -1,
+            loss_output = "softmax",
+            output_last_state = F,
+            masking = T)
 })
 
 model_sentiment_lstm <- mx.model.buckets(symbol = symbol_buckets,
-                          train.data = train.data, eval.data = eval.data,
-                          num.round = num.round, ctx = devices, verbose = FALSE,
-                          metric = mx.metric.accuracy, optimizer = optimizer,  
-                          initializer = initializer,
-                          batch.end.callback = NULL, 
-                          epoch.end.callback = epoch.end.callback)
+                                         train.data = train.data,
+                                         eval.data = eval.data,
+                                         num.round = num.round,
+                                         ctx = devices,
+                                         verbose = FALSE,
+                                         metric = mx.metric.accuracy,
+                                         optimizer = optimizer,
+                                         initializer = mx.init.Xavier(rnd_type = "gaussian", factor_type = "in", magnitude = 2),
 
 Review comment:
   it did not break any lint check, but i will split it up into different lines.

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