Trjtdtk

My bank got bought out, am I now going to have to start filing tax returns in a different state?

Why didn't the Space Shuttle bounce back into space as many times as possible so as to lose a lot of kinetic energy up there?

Can a stored procedure reference the database in which it is stored?

Will I lose my paid in full property

Putting Ant-Man on house arrest

How to not starve gigantic beasts

Raising a bilingual kid. When should we introduce the majority language?

Check if a string is entirely made of the same substring

How exactly does Hawking radiation decrease the mass of black holes?

Double-nominative constructions and “von”

Is there really no use for MD5 anymore?

Was Dennis Ritchie being too modest in this quote about C and Pascal?

How much cash can I safely carry into the USA and avoid civil forfeiture?

All ASCII characters with a given bit count

std::unique_ptr of base class holding reference of derived class does not show warning in gcc compiler while naked pointer shows it. Why?

Is there any pythonic way to find average of specific tuple elements in array?

Unable to completely uninstall Zoom meeting app

How would this chord from "Rocket Man" be analyzed?

Has a Nobel Peace laureate ever been accused of war crimes?

How do I check if a string is entirely made of the same substring?

Negative Resistance

Are there moral objections to a life motivated purely by money? How to sway a person from this lifestyle?

Is there metaphorical meaning of "aus der Haft entlassen"?

Can I criticise the more senior developers around me for not writing clean code?

How can we add preprocessing steps, in the keras sequential model itself?

0

$begingroup$

Is there a way to add a layer which includes my preprocessing steps in this sequential model.For example

model.add(LabelEncoder.transform(X_train['gender'],X_train['grade']),scaler(X_train))

How to include this step in the create model definition?

def create_model(optimizer='adagrad',
 kernel_initializer='glorot_uniform', 
 dropout=0.2):
model = Sequential()
model.add(Dense(64,activation='relu',kernel_initializer=kernel_initializer))
model.add(Dropout(dropout))
model.add(Dense(1,activation='sigmoid',kernel_initializer=kernel_initializer))

model.compile(loss='binary_crossentropy',optimizer=optimizer, metrics=['accuracy'])

return model

Help in this regard is very much appreciated.

deep-learning keras scikit-learn tensorflow preprocessing

share|improve this question

asked Apr 6 at 14:25

MalathiMalathi

61

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  Welcome to SE.DataScience! Even if there is such solution, generally it is not a good practice, since a single instance may be fed to the model multiple times, thus it will be pre-processed multiple times instead of only once. Therefore, it is better to keep all the pre-processing out of the model.
  $endgroup$
  – Esmailian
  Apr 6 at 14:39
  

  
  
  
  

add a comment | 

0

$begingroup$

Is there a way to add a layer which includes my preprocessing steps in this sequential model.For example

model.add(LabelEncoder.transform(X_train['gender'],X_train['grade']),scaler(X_train))

How to include this step in the create model definition?

def create_model(optimizer='adagrad',
 kernel_initializer='glorot_uniform', 
 dropout=0.2):
model = Sequential()
model.add(Dense(64,activation='relu',kernel_initializer=kernel_initializer))
model.add(Dropout(dropout))
model.add(Dense(1,activation='sigmoid',kernel_initializer=kernel_initializer))

model.compile(loss='binary_crossentropy',optimizer=optimizer, metrics=['accuracy'])

return model

Help in this regard is very much appreciated.

deep-learning keras scikit-learn tensorflow preprocessing

share|improve this question

asked Apr 6 at 14:25

MalathiMalathi

61

$endgroup$

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  $begingroup$
  Welcome to SE.DataScience! Even if there is such solution, generally it is not a good practice, since a single instance may be fed to the model multiple times, thus it will be pre-processed multiple times instead of only once. Therefore, it is better to keep all the pre-processing out of the model.
  $endgroup$
  – Esmailian
  Apr 6 at 14:39
  

  
  
  
  

add a comment | 

$begingroup$

Is there a way to add a layer which includes my preprocessing steps in this sequential model.For example

model.add(LabelEncoder.transform(X_train['gender'],X_train['grade']),scaler(X_train))

How to include this step in the create model definition?

def create_model(optimizer='adagrad',
 kernel_initializer='glorot_uniform', 
 dropout=0.2):
model = Sequential()
model.add(Dense(64,activation='relu',kernel_initializer=kernel_initializer))
model.add(Dropout(dropout))
model.add(Dense(1,activation='sigmoid',kernel_initializer=kernel_initializer))

model.compile(loss='binary_crossentropy',optimizer=optimizer, metrics=['accuracy'])

return model

Help in this regard is very much appreciated.

deep-learning keras scikit-learn tensorflow preprocessing

share|improve this question

asked Apr 6 at 14:25

MalathiMalathi

61

$endgroup$

model.add(LabelEncoder.transform(X_train['gender'],X_train['grade']),scaler(X_train))

def create_model(optimizer='adagrad',
 kernel_initializer='glorot_uniform', 
 dropout=0.2):
model = Sequential()
model.add(Dense(64,activation='relu',kernel_initializer=kernel_initializer))
model.add(Dropout(dropout))
model.add(Dense(1,activation='sigmoid',kernel_initializer=kernel_initializer))

model.compile(loss='binary_crossentropy',optimizer=optimizer, metrics=['accuracy'])

return model

share|improve this question

asked Apr 6 at 14:25

MalathiMalathi

61

share|improve this question

asked Apr 6 at 14:25

MalathiMalathi

61

asked Apr 6 at 14:25

MalathiMalathi

61

asked Apr 6 at 14:25

MalathiMalathi

61