Update language model (#19)

* udpate lmexp

* udpate word_language_model.py

* udpate word_language_model.py

* update with lm_decay

* update word_language_model.py with new updated hiddensize, standardrnn exchange tied and dropout; update base.py with rnn_relu config; update lm.py with awd_lstm_lm_1150 pretrained setting, and with new sentiment analysis and lm example

* remove lm test file

example/gluon/word_language_model.py

@@ -22,7 +22,7 @@
 import mxnet as mx
 from mxnet import gluon, autograd
 from mxnet.gluon import data, text
-from mxnet.gluon.model_zoo.text.lm import SimpleRNN, AWDRNN
+from mxnet.gluon.model_zoo.text.lm import StandardRNN, AWDRNN
 
 parser = argparse.ArgumentParser(description='MXNet Autograd RNN/LSTM Language Model on Wikitext-2.')
 parser.add_argument('--model', type=str, default='lstm',
@@ -68,6 +68,8 @@
                     help='list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu. (the result of multi-gpu training might be slightly different compared to single-gpu training, still need to be finalized)')
 args = parser.parse_args()
 
+print(args)
+
 
 ###############################################################################
 # Load data
@@ -82,7 +84,7 @@
 def get_frequencies(dataset):
     return collections.Counter(x for tup in dataset for x in tup[0] if x)
 
-vocab = text.vocab.Vocabulary(get_frequencies(train_dataset))
+vocab = text.vocab.Vocabulary(get_frequencies(train_dataset), reserved_tokens=['<eos>', '<pad>'])
 def index_tokens(data, label):
     return vocab[data], vocab[label]
 
@@ -124,8 +126,8 @@ def index_tokens(data, label):
     model = AWDRNN(args.model, len(vocab), args.emsize, args.nhid, args.nlayers,
                    args.tied, args.dropout, args.weight_dropout, args.dropout_h, args.dropout_i)
 else:
-    model = SimpleRNN(args.model, len(vocab), args.emsize, args.nhid, args.nlayers,
-                      args.tied, args.dropout)
+    model = StandardRNN(args.model, len(vocab), args.emsize, args.nhid, args.nlayers, args.dropout,
+                      args.tied)
 
 model.initialize(mx.init.Xavier(), ctx=context)
 
@@ -169,7 +171,7 @@ def train():
     for epoch in range(args.epochs):
         total_L = 0.0
         start_epoch_time = time.time()
-        hiddens = [model.begin_state(args.batch_size, func=mx.nd.zeros, ctx=ctx) for ctx in context]
+        hiddens = [model.begin_state(args.batch_size//len(context), func=mx.nd.zeros, ctx=ctx) for ctx in context]
         for i, (data, target) in enumerate(train_data):
             start_batch_time = time.time()
             data = data.T

python/mxnet/gluon/model_zoo/text/base.py

@@ -118,7 +118,7 @@ def get_rnn_cell(mode, num_layers, input_size, hidden_size,
 def get_rnn_layer(mode, num_layers, input_size, hidden_size, dropout, weight_dropout):
     """create rnn layer given specs"""
     if mode == 'rnn_relu':
-        block = rnn.RNN(hidden_size, 'relu', num_layers, dropout=dropout,
+        block = rnn.RNN(hidden_size, num_layers, 'relu', dropout=dropout,
                         input_size=input_size)
     elif mode == 'rnn_tanh':
         block = rnn.RNN(hidden_size, num_layers, dropout=dropout,

python/mxnet/gluon/model_zoo/text/lm.py

@@ -209,7 +209,7 @@ def awd_lstm_lm_1150(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
                        'tie_weights': True,
                        'dropout': 0.4,
                        'weight_drop': 0.5,
-                       'drop_h': 0.3,
+                       'drop_h': 0.2,
                        'drop_i': 0.65}
     assert all(k not in kwargs for k in predefined_args), \
            "Cannot override predefined model settings."