title: ALL NaN values after accuracy is trained using K-cross validation
body: <pre><code>    from keras.wrappers.scikit_learn import KerasClassifier
      from sklearn.model_selection import cross_val_score
      def build_classifier():
          classifier = Sequential()
          classifier.add(Dense(units = 6, activation = 'relu', input_dim = 11, kernel_initializer = 
          'uniform'))
          classifier.add(Dense(units = 6, activation = 'relu', kernel_initializer = 'uniform'))
          classifier.add(Dense(units = 1, activation = 'sigmoid', kernel_initializer = 'uniform'))
          classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
          return classifier
  
      classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 100)
      accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
  </code></pre>
  
  <p>/<em>This code seems pretty clean but the K_cross validation doesnt seem to work. It returns Nan and executes pretty fast as opposed to when using all the CPUs</em>/</p>