Why am i getting AttributeError: 'KerasClassifier' object has no attribute 'model'?

Question

This is the code and I\'m getting the error in the last line only which is y_pred = classifier.predict(X_test). The error I\'m getting is AttributeError:

Answer 1

You get the error because you didn´t actually train the returned model from KerasClassifier which is a Scikit-learn Wrapper to make use of Scikit-learn functions.

You could for example do a GridSearch (as you might know since the code seems to be from the Udemy ML/DL course):

def build_classifier(optimizer):
    classifier = Sequential()
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', 
        activation = 'relu', input_dim = 11))
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', 
        activation = 'relu'))
    classifier.add(Dense(units = 1, kernel_initializer = 'uniform', 
        activation = 'sigmoid'))
    classifier.compile(optimizer = optimizer, loss = 
        'binary_crossentropy', metrics = ['accuracy'])
    return classifier

classifier = KerasClassifier(build_fn = build_classifier)
parameters = {'batch_size': [25, 32],
          'epochs': [100, 500],
          'optimizer': ['adam', 'rmsprop']}
grid_search = GridSearchCV(estimator = classifier,
                       param_grid = parameters,
                       scoring = 'accuracy',
                       cv = 10)
grid_search = grid_search.fit(X_train, y_train)

If you don´t need Scikit-learn functionality I suggest to avoid the wrapper and simply build your model with:

model = Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))
…

and then train with:

model.fit( … )

Answer 2

Because you haven't fitted the classifier yet. For classifier to have the model variable available, you need to call

classifier.fit(X_train, y_train)

Although you have used cross_val_score() over the classifier, and found out accuracies, but the main point to note here is that the cross_val_score will clone the supplied model and use them for cross-validation folds. So your original estimator classifier is untouched and untrained.

You can see the working of cross_val_score in my other answer here

So put the above mentioned line just above y_pred = classifier.predict(X_test) line and you are all set. Hope this makes it clear.