[GitHub] haven-jeon opened a new issue #12778: Cannot print parameter summary of embedding layer

[GitBox <gi...@apache.org>]

haven-jeon opened a new issue #12778: Cannot print parameter summary of embedding layer 
URL: https://github.com/apache/incubator-mxnet/issues/12778
 
 
   ## Description
   0  parameter of Embedding layer, when print_summary() 
   
   ## Environment info (Required)
   
   MXNet 1.3.0
   
   Package used (Python/R/Scala/Julia):
   I am using python
   
   ## Minimum reproducible example
   
   ```
   class SentClassificationModel(gluon.HybridBlock):
       def __init__(self, vocab_size, num_embed, **kwargs):
           super(SentClassificationModel, self).__init__(**kwargs)
           with self.name_scope():
               self.embed = nn.Embedding(input_dim=vocab_size, output_dim=num_embed)
               self.drop = nn.Dropout(0.3)
               self.fc = nn.Dense(100, activation='relu')
               self.out = nn.Dense(2)  
       def hybrid_forward(self, F ,inputs):
           em_out = self.drop(self.embed(inputs))
           fc_out = self.fc(em_out) 
           return(self.out(fc_out))
   
   ctx = mx.gpu()
   
   model = SentClassificationModel(vocab_size = len(vocab.idx_to_token), num_embed=50)
   
   model.initialize(mx.init.Xavier(),ctx=ctx)
   model.hybridize()
   
   mx.viz.print_summary(
       model(mx.sym.var('data')), 
       shape={'data':(1,30)}, #set your shape here
   )
   ```
   
   > ________________________________________________________________________________________________________________________
   Layer (type)                                        Output Shape            Param #     Previous Layer                  
   ========================================================================================================================
   data(null)                                          30                      0                                           
   ________________________________________________________________________________________________________________________
   sentclassificationmodel0_embedding0_fwd(Embedding)  30x50                   0           data                            
   ________________________________________________________________________________________________________________________
   sentclassificationmodel0_dropout0_fwd(Dropout)      30x50                   0           sentclassificationmodel0_embeddi
   ________________________________________________________________________________________________________________________
   sentclassificationmodel0_dense0_fwd(FullyConnected) 100                     3100        sentclassificationmodel0_dropout
   ________________________________________________________________________________________________________________________
   sentclassificationmodel0_dense0_relu_fwd(Activation)100                     0           sentclassificationmodel0_dense0_
   ________________________________________________________________________________________________________________________
   sentclassificationmodel0_dense1_fwd(FullyConnected) 2                       202         sentclassificationmodel0_dense0_
   ========================================================================================================================
   Total params: 3302
   ________________________________________________________________________________________________________________________
   
   
   
   

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