soeque1 edited a comment on issue #12796: Add embedding to print_summary URL: https://github.com/apache/incubator-mxnet/pull/12796#issuecomment-429038277 The Post (#12778) does not have enough information to run the code. So I added something to the sample code. ``` from mxnet import gluon from mxnet.gluon import HybridBlock, nn import mxnet as mx vocab_size ``` ``` from mxnet import gluon from mxnet.gluon import HybridBlock, nn import mxnet as mx 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=20, 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 ) ``` The output is the below ``` ________________________________________________________________________________________________________________________ Layer (type) Output Shape Param # Previous Layer ======================================================================================================================== data(null) 30 0 ________________________________________________________________________________________________________________________ sentclassificationmodel2_embedding0_fwd(Embedding) 30x50 1000 data ________________________________________________________________________________________________________________________ sentclassificationmodel2_dropout0_fwd(Dropout) 30x50 0 sentclassificationmodel2_embeddi ________________________________________________________________________________________________________________________ sentclassificationmodel2_dense0_fwd(FullyConnected) 100 3100 sentclassificationmodel2_dropout ________________________________________________________________________________________________________________________ sentclassificationmodel2_dense0_relu_fwd(Activation)100 0 sentclassificationmodel2_dense0_ ________________________________________________________________________________________________________________________ sentclassificationmodel2_dense1_fwd(FullyConnected) 2 202 sentclassificationmodel2_dense0_ ======================================================================================================================== Total params: 4302 ________________________________________________________________________________________________________________________ ```
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