[GitHub] janelu9 commented on issue #8065: Help? when I go on training a language model, errors occured

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janelu9 commented on issue #8065: Help? when I go on training a language model, 
errors occured
URL: 
https://github.com/apache/incubator-mxnet/issues/8065#issuecomment-332734162
 
 
   this is a  n-gram model
   import mxnet as mx
   import numpy as np
   from n_gamma import N_gamma
   from word2vec_io import BucketSentenceIter
   import pickle as pk
   
   def Perplexity(label, pred):
       label = label.T.reshape((-1,))
       loss = 0.
       for i in range(pred.shape[0]):
           loss += -np.log(max(1e-10, pred[i][int(label[i])]))
       return np.exp(loss / label.size)
   
   if __name__ == '__main__':
       batch_size=64
       hidden_num = 128
       embed_dim = 128
       N=5
       vocab=pk.load(open('.//data//dict_news_lstm20160804.pkl','r'))
       vocab_size=len(vocab)
       data=pk.load(open('.//data//train.pkl','r'))
       data=[i[0] for i in data]
       data_val=pk.load(open('.//data//test.pkl','r'))
       data_val=[i[0] for i in data_val]
       def sym_gen(seq_len):
           return N_gamma(seq_len,batch_size,vocab_size,embed_dim,hidden_num,N)
       train=BucketSentenceIter(data,batch_size,N)
       test=BucketSentenceIter(data_val,batch_size,N)
       symbol=sym_gen
       model = mx.model.FeedForward(ctx=[mx.context.gpu(i) for i in range(1)],
                                    symbol=symbol,
                                    num_epoch=10,
                                    learning_rate=0.01,
                                    momentum=0.0,
                                    wd=0.00001,
                                    
initializer=mx.init.Xavier(factor_type="in", magnitude=2.34))
   
       import logging
       #head = '%(asctime)-15s %(message)s'
       #logging.basicConfig(level=logging.DEBUG, format=head)
       logging.basicConfig(level=logging.DEBUG,
                       filename='./train.log',
                       filemode='w')
       console = logging.StreamHandler()
       console.setLevel(logging.DEBUG)
       logging.getLogger().addHandler(console)
   
       model.fit(X=train, eval_data=test,
                 eval_metric = mx.metric.np(Perplexity),
                          
epoch_end_callback=mx.callback.do_checkpoint("./output/5_gram"),
                 batch_end_callback=mx.callback.Speedometer(batch_size, 100),)``
 
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