Nested CV with Keras Deep Learning





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Should one use Nested CV when creating a deep learning model in Keras? I have been running the following code which takes a very long time. I don't have a very large dataset (50K rows) and the task is a binary classification one if it matters at all. 



from keras.wrappers.scikit_learn import KerasClassifier

from sklearn.model_selection import GridSearchCV



def create_model():

# create model

    model = Sequential()

    model.add(Dense(12, input_dim=20, activation='relu'))

    model.add(Dense(1, activation='sigmoid'))

    # Compile model

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

    return model



estimator = KerasClassifier(build_fn=create_model, epochs=100, batch_size=5, verbose=0)



param_grid = dict(epochs=[10,20,30])

gs = GridSearchCV(estimator=estimator, param_grid=param_grid, n_jobs=-1)



results = cross_val_score(gs, X_train, y_train, cv=2)#, n_jobs = -1)

print("Results: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))





machine-learning keras deep-learning cross-validation







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    0















    Should one use Nested CV when creating a deep learning model in Keras? I have been running the following code which takes a very long time. I don't have a very large dataset (50K rows) and the task is a binary classification one if it matters at all. 



    from keras.wrappers.scikit_learn import KerasClassifier
    
from sklearn.model_selection import GridSearchCV
    

    
def create_model():
    
# create model
    
    model = Sequential()
    
    model.add(Dense(12, input_dim=20, activation='relu'))
    
    model.add(Dense(1, activation='sigmoid'))
    
    # Compile model
    
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    
    return model
    

    
estimator = KerasClassifier(build_fn=create_model, epochs=100, batch_size=5, verbose=0)
    

    
param_grid = dict(epochs=[10,20,30])
    
gs = GridSearchCV(estimator=estimator, param_grid=param_grid, n_jobs=-1)
    

    
results = cross_val_score(gs, X_train, y_train, cv=2)#, n_jobs = -1)
    
print("Results: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))





    machine-learning keras deep-learning cross-validation







    share|improve this question

























      0












      0








      0








      Should one use Nested CV when creating a deep learning model in Keras? I have been running the following code which takes a very long time. I don't have a very large dataset (50K rows) and the task is a binary classification one if it matters at all. 



      from keras.wrappers.scikit_learn import KerasClassifier
      
from sklearn.model_selection import GridSearchCV
      

      
def create_model():
      
# create model
      
    model = Sequential()
      
    model.add(Dense(12, input_dim=20, activation='relu'))
      
    model.add(Dense(1, activation='sigmoid'))
      
    # Compile model
      
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
      
    return model
      

      
estimator = KerasClassifier(build_fn=create_model, epochs=100, batch_size=5, verbose=0)
      

      
param_grid = dict(epochs=[10,20,30])
      
gs = GridSearchCV(estimator=estimator, param_grid=param_grid, n_jobs=-1)
      

      
results = cross_val_score(gs, X_train, y_train, cv=2)#, n_jobs = -1)
      
print("Results: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))





      machine-learning keras deep-learning cross-validation







      share|improve this question














      Should one use Nested CV when creating a deep learning model in Keras? I have been running the following code which takes a very long time. I don't have a very large dataset (50K rows) and the task is a binary classification one if it matters at all. 



      from keras.wrappers.scikit_learn import KerasClassifier
      
from sklearn.model_selection import GridSearchCV
      

      
def create_model():
      
# create model
      
    model = Sequential()
      
    model.add(Dense(12, input_dim=20, activation='relu'))
      
    model.add(Dense(1, activation='sigmoid'))
      
    # Compile model
      
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
      
    return model
      

      
estimator = KerasClassifier(build_fn=create_model, epochs=100, batch_size=5, verbose=0)
      

      
param_grid = dict(epochs=[10,20,30])
      
gs = GridSearchCV(estimator=estimator, param_grid=param_grid, n_jobs=-1)
      

      
results = cross_val_score(gs, X_train, y_train, cv=2)#, n_jobs = -1)
      
print("Results: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))





      machine-learning keras deep-learning cross-validation




      machine-learning keras deep-learning cross-validation






      share|improve this question













      share|improve this question











      share|improve this question




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      asked Jan 3 at 3:53









      OdisseoOdisseo

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