K-fold cross validation of scikit-learn with confusion matrix of Keras The Next CEO of Stack Overflow2019 Community Moderator ElectionCross Validation in KerasK-Fold Cross validation confusion?Linear Regression and k-fold cross validationK fold cross validation algorithmHow does k fold cross validation work?k-fold cross-validation: model selection or variation in models when using k-fold cross validationQuestion about K-Fold Cross ValidationSimple prediction with KerasK-fold cross validation when using fit_generator and flow_from_directory() in KerasUsing K-fold cross-validation in Keras on the data of my model

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K-fold cross validation of scikit-learn with confusion matrix of Keras



The Next CEO of Stack Overflow
2019 Community Moderator ElectionCross Validation in KerasK-Fold Cross validation confusion?Linear Regression and k-fold cross validationK fold cross validation algorithmHow does k fold cross validation work?k-fold cross-validation: model selection or variation in models when using k-fold cross validationQuestion about K-Fold Cross ValidationSimple prediction with KerasK-fold cross validation when using fit_generator and flow_from_directory() in KerasUsing K-fold cross-validation in Keras on the data of my model










2












$begingroup$


I intend to display confusion matrix using Keras while K-fold of scikit-learn. My code using Keras is:



import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense

seed = 7
numpy.random.seed(seed)

# load dataset
dataframe = pandas.read_csv("BolMov.csv", header=None)
dataset = dataframe.values
X = dataset[:,0:24].astype(float)
Y = dataset[:,24]

model = Sequential()
model.add(Dense(16, activation='relu'))
model.add(Dense(7, activation='softmax')
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])

y_cat = to_categorical(Y)
result = model.fit(X, y_cat, verbose=0, epochs=50)

plot_loss_accuracy(result)

y_pred = model.predict_classes(X, verbose=0)

print(classification_report(y, y_pred))
plot_confusion_matrix(model, X, y)


How should I use kfold in this code? Here the author is calling a function. What I believe is that if I do so in my code, the model.fit() will be executed twice - once for my Keras code and another time (internally) for the KerasClassifier(). I want that the model.fit() executes once only. Help from anyone is appreciated.






keras scikit-learn cross-validation







share|improve this question











$endgroup$











  • $begingroup$
    don't call model.fit() as you currently do in your code but instead wrap your model in a KerasClassifier() and apply KFold() to it
    $endgroup$
    – pcko1
    Feb 16 at 23:58











  • $begingroup$
    @pcko1 Can I write like this: result = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0) and then plot_loss_accuracy(result) so that result can be used for kfold validation of scikit-learn as well as confusion matrix display of Keras?
    $endgroup$
    – PS Nayak
    Feb 18 at 14:08
















2












$begingroup$


I intend to display confusion matrix using Keras while K-fold of scikit-learn. My code using Keras is:



import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense

seed = 7
numpy.random.seed(seed)

# load dataset
dataframe = pandas.read_csv("BolMov.csv", header=None)
dataset = dataframe.values
X = dataset[:,0:24].astype(float)
Y = dataset[:,24]

model = Sequential()
model.add(Dense(16, activation='relu'))
model.add(Dense(7, activation='softmax')
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])

y_cat = to_categorical(Y)
result = model.fit(X, y_cat, verbose=0, epochs=50)

plot_loss_accuracy(result)

y_pred = model.predict_classes(X, verbose=0)

print(classification_report(y, y_pred))
plot_confusion_matrix(model, X, y)


How should I use kfold in this code? Here the author is calling a function. What I believe is that if I do so in my code, the model.fit() will be executed twice - once for my Keras code and another time (internally) for the KerasClassifier(). I want that the model.fit() executes once only. Help from anyone is appreciated.






keras scikit-learn cross-validation







share|improve this question











$endgroup$











  • $begingroup$
    don't call model.fit() as you currently do in your code but instead wrap your model in a KerasClassifier() and apply KFold() to it
    $endgroup$
    – pcko1
    Feb 16 at 23:58











  • $begingroup$
    @pcko1 Can I write like this: result = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0) and then plot_loss_accuracy(result) so that result can be used for kfold validation of scikit-learn as well as confusion matrix display of Keras?
    $endgroup$
    – PS Nayak
    Feb 18 at 14:08














2












2








2


1



$begingroup$


I intend to display confusion matrix using Keras while K-fold of scikit-learn. My code using Keras is:



import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense

seed = 7
numpy.random.seed(seed)

# load dataset
dataframe = pandas.read_csv("BolMov.csv", header=None)
dataset = dataframe.values
X = dataset[:,0:24].astype(float)
Y = dataset[:,24]

model = Sequential()
model.add(Dense(16, activation='relu'))
model.add(Dense(7, activation='softmax')
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])

y_cat = to_categorical(Y)
result = model.fit(X, y_cat, verbose=0, epochs=50)

plot_loss_accuracy(result)

y_pred = model.predict_classes(X, verbose=0)

print(classification_report(y, y_pred))
plot_confusion_matrix(model, X, y)


How should I use kfold in this code? Here the author is calling a function. What I believe is that if I do so in my code, the model.fit() will be executed twice - once for my Keras code and another time (internally) for the KerasClassifier(). I want that the model.fit() executes once only. Help from anyone is appreciated.






keras scikit-learn cross-validation







share|improve this question











$endgroup$




I intend to display confusion matrix using Keras while K-fold of scikit-learn. My code using Keras is:



import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense

seed = 7
numpy.random.seed(seed)

# load dataset
dataframe = pandas.read_csv("BolMov.csv", header=None)
dataset = dataframe.values
X = dataset[:,0:24].astype(float)
Y = dataset[:,24]

model = Sequential()
model.add(Dense(16, activation='relu'))
model.add(Dense(7, activation='softmax')
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])

y_cat = to_categorical(Y)
result = model.fit(X, y_cat, verbose=0, epochs=50)

plot_loss_accuracy(result)

y_pred = model.predict_classes(X, verbose=0)

print(classification_report(y, y_pred))
plot_confusion_matrix(model, X, y)


How should I use kfold in this code? Here the author is calling a function. What I believe is that if I do so in my code, the model.fit() will be executed twice - once for my Keras code and another time (internally) for the KerasClassifier(). I want that the model.fit() executes once only. Help from anyone is appreciated.






keras scikit-learn cross-validation




keras scikit-learn cross-validation






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Feb 16 at 17:46







PS Nayak

















asked Feb 16 at 17:38









PS NayakPS Nayak

134




134











  • $begingroup$
    don't call model.fit() as you currently do in your code but instead wrap your model in a KerasClassifier() and apply KFold() to it
    $endgroup$
    – pcko1
    Feb 16 at 23:58











  • $begingroup$
    @pcko1 Can I write like this: result = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0) and then plot_loss_accuracy(result) so that result can be used for kfold validation of scikit-learn as well as confusion matrix display of Keras?
    $endgroup$
    – PS Nayak
    Feb 18 at 14:08

















  • $begingroup$
    don't call model.fit() as you currently do in your code but instead wrap your model in a KerasClassifier() and apply KFold() to it
    $endgroup$
    – pcko1
    Feb 16 at 23:58











  • $begingroup$
    @pcko1 Can I write like this: result = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0) and then plot_loss_accuracy(result) so that result can be used for kfold validation of scikit-learn as well as confusion matrix display of Keras?
    $endgroup$
    – PS Nayak
    Feb 18 at 14:08
















$begingroup$
don't call model.fit() as you currently do in your code but instead wrap your model in a KerasClassifier() and apply KFold() to it
$endgroup$
– pcko1
Feb 16 at 23:58





$begingroup$
don't call model.fit() as you currently do in your code but instead wrap your model in a KerasClassifier() and apply KFold() to it
$endgroup$
– pcko1
Feb 16 at 23:58













$begingroup$
@pcko1 Can I write like this: result = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0) and then plot_loss_accuracy(result) so that result can be used for kfold validation of scikit-learn as well as confusion matrix display of Keras?
$endgroup$
– PS Nayak
Feb 18 at 14:08





$begingroup$
@pcko1 Can I write like this: result = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0) and then plot_loss_accuracy(result) so that result can be used for kfold validation of scikit-learn as well as confusion matrix display of Keras?
$endgroup$
– PS Nayak
Feb 18 at 14:08











1 Answer
1






active

oldest

votes


















0












$begingroup$

KFold() is meant for cross-validation purpose where multiple models are created over the subsets of the entire dataset and discarded after the validation procedure is over. So the model.fit() should be called explicitly to create the model for purpose. Both the tasks can be easily done through the wrapper named KerasClassifier() by packaging all the details of the model design.






share|improve this answer









$endgroup$













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    active

    oldest

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    oldest

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    0












    $begingroup$

    KFold() is meant for cross-validation purpose where multiple models are created over the subsets of the entire dataset and discarded after the validation procedure is over. So the model.fit() should be called explicitly to create the model for purpose. Both the tasks can be easily done through the wrapper named KerasClassifier() by packaging all the details of the model design.






    share|improve this answer









    $endgroup$

















      0












      $begingroup$

      KFold() is meant for cross-validation purpose where multiple models are created over the subsets of the entire dataset and discarded after the validation procedure is over. So the model.fit() should be called explicitly to create the model for purpose. Both the tasks can be easily done through the wrapper named KerasClassifier() by packaging all the details of the model design.






      share|improve this answer









      $endgroup$















        0












        0








        0





        $begingroup$

        KFold() is meant for cross-validation purpose where multiple models are created over the subsets of the entire dataset and discarded after the validation procedure is over. So the model.fit() should be called explicitly to create the model for purpose. Both the tasks can be easily done through the wrapper named KerasClassifier() by packaging all the details of the model design.






        share|improve this answer









        $endgroup$



        KFold() is meant for cross-validation purpose where multiple models are created over the subsets of the entire dataset and discarded after the validation procedure is over. So the model.fit() should be called explicitly to create the model for purpose. Both the tasks can be easily done through the wrapper named KerasClassifier() by packaging all the details of the model design.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Feb 22 at 6:24









        PS NayakPS Nayak

        134




        134



























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