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0

I'm trying to use googel wide&deep net,but it has some problem.

#Wide part
wide = Sequential()
wide.add(Dense(1, input_dim=X_train.shape[1]))

#Deep part, Wide and Deep are keras.engine.sequential.Sequential
deep = Sequential()
deep.add(Dense(input_dim=X_train.shape[1], output_dim=100, activation='relu'))
deep.add(Dense(input_dim=100, output_dim=32, activation='relu'))
deep.add(Dense(input_dim=32, output_dim=8))
deep.add(Dense(1, activation='sigmoid'))
#merge the two part , and it changes to tensorflow.python.framework.ops.Tensor 
model = Sequential()
model = layers.Concatenate()([wide.output, deep.output]) 

#I want to add a dense layer to get one result,but it doesn't work anymore <br>
model.add(Dense(1, activation='sigmoid'))

the net and red circle is the problem
How can I deal with this problem, thanks very very much.

python tensorflow

share|improve this question

edited Mar 7 at 8:52

Cindy

asked Mar 7 at 5:11

CindyCindy

12

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Are you trying to merge two layers?
  
  – anand_v.singh
  Mar 7 at 5:57
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Thank you. I add a picture, maybe it will help me describe the problem. I have already merged wide and deep part, now I need a dense layer to give the result..thank you very much
  
  – Cindy
  Mar 7 at 8:58
  
  

  
  
  
  

add a comment | 

0

I'm trying to use googel wide&deep net,but it has some problem.

#Wide part
wide = Sequential()
wide.add(Dense(1, input_dim=X_train.shape[1]))

#Deep part, Wide and Deep are keras.engine.sequential.Sequential
deep = Sequential()
deep.add(Dense(input_dim=X_train.shape[1], output_dim=100, activation='relu'))
deep.add(Dense(input_dim=100, output_dim=32, activation='relu'))
deep.add(Dense(input_dim=32, output_dim=8))
deep.add(Dense(1, activation='sigmoid'))
#merge the two part , and it changes to tensorflow.python.framework.ops.Tensor 
model = Sequential()
model = layers.Concatenate()([wide.output, deep.output]) 

#I want to add a dense layer to get one result,but it doesn't work anymore <br>
model.add(Dense(1, activation='sigmoid'))

the net and red circle is the problem
How can I deal with this problem, thanks very very much.

python tensorflow

share|improve this question

edited Mar 7 at 8:52

Cindy

asked Mar 7 at 5:11

CindyCindy

12

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Are you trying to merge two layers?
  
  – anand_v.singh
  Mar 7 at 5:57
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Thank you. I add a picture, maybe it will help me describe the problem. I have already merged wide and deep part, now I need a dense layer to give the result..thank you very much
  
  – Cindy
  Mar 7 at 8:58
  
  

  
  
  
  

add a comment | 

I'm trying to use googel wide&deep net,but it has some problem.

#Wide part
wide = Sequential()
wide.add(Dense(1, input_dim=X_train.shape[1]))

#Deep part, Wide and Deep are keras.engine.sequential.Sequential
deep = Sequential()
deep.add(Dense(input_dim=X_train.shape[1], output_dim=100, activation='relu'))
deep.add(Dense(input_dim=100, output_dim=32, activation='relu'))
deep.add(Dense(input_dim=32, output_dim=8))
deep.add(Dense(1, activation='sigmoid'))
#merge the two part , and it changes to tensorflow.python.framework.ops.Tensor 
model = Sequential()
model = layers.Concatenate()([wide.output, deep.output]) 

#I want to add a dense layer to get one result,but it doesn't work anymore <br>
model.add(Dense(1, activation='sigmoid'))

the net and red circle is the problem
How can I deal with this problem, thanks very very much.

python tensorflow

share|improve this question

edited Mar 7 at 8:52

Cindy

asked Mar 7 at 5:11

CindyCindy

12

#Wide part
wide = Sequential()
wide.add(Dense(1, input_dim=X_train.shape[1]))

#Deep part, Wide and Deep are keras.engine.sequential.Sequential
deep = Sequential()
deep.add(Dense(input_dim=X_train.shape[1], output_dim=100, activation='relu'))
deep.add(Dense(input_dim=100, output_dim=32, activation='relu'))
deep.add(Dense(input_dim=32, output_dim=8))
deep.add(Dense(1, activation='sigmoid'))
#merge the two part , and it changes to tensorflow.python.framework.ops.Tensor 
model = Sequential()
model = layers.Concatenate()([wide.output, deep.output]) 

#I want to add a dense layer to get one result,but it doesn't work anymore <br>
model.add(Dense(1, activation='sigmoid'))

share|improve this question

edited Mar 7 at 8:52

Cindy

asked Mar 7 at 5:11

CindyCindy

12

share|improve this question

edited Mar 7 at 8:52

Cindy

asked Mar 7 at 5:11

CindyCindy

12

asked Mar 7 at 5:11

CindyCindy

12

asked Mar 7 at 5:11

CindyCindy

12