title: Why am i getting AttributeError: 'KerasClassifier' object has no attribute 'model'?
body: <p>This is the code and I'm getting the error in the last line only which is <code>y_pred = classifier.predict(X_test)</code>. The error I'm getting is <code>AttributeError: 'KerasClassifier' object has no attribute 'model'</code></p>
  
  <pre><code># Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  from sklearn import datasets
  from sklearn import preprocessing
  from keras.utils import np_utils
  
  # Importing the dataset
  dataset = pd.read_csv('Data1.csv',encoding = "cp1252")
  X = dataset.iloc[:, 1:-1].values
  y = dataset.iloc[:, -1].values
  
  # Encoding categorical data
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  labelencoder_X_0 = LabelEncoder()
  X[:, 0] = labelencoder_X_0.fit_transform(X[:, 0])
  labelencoder_X_1 = LabelEncoder()
  X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
  labelencoder_X_2 = LabelEncoder()
  X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
  labelencoder_X_3 = LabelEncoder()
  X[:, 3] = labelencoder_X_3.fit_transform(X[:, 3])
  onehotencoder = OneHotEncoder(categorical_features = [1])
  X = onehotencoder.fit_transform(X).toarray()
  X = X[:, 1:]
  
  # Splitting the dataset into the Training set and Test set
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
  
  # Feature Scaling
  from sklearn.preprocessing import StandardScaler
  sc = StandardScaler()
  X_train = sc.fit_transform(X_train)
  X_test = sc.transform(X_test)
  
  # Creating the ANN!
  # Importing the Keras libraries and packages
  import keras
  from keras.models import Sequential
  from keras.layers import Dense
  from keras.layers import Dropout
  from keras.wrappers.scikit_learn import KerasClassifier
  from sklearn.model_selection import cross_val_score
  def build_classifier():
      # Initialising the ANN
      classifier = Sequential()
      # Adding the input layer and the first hidden layer
      classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 10))
  
      classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
  
      classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
      classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
      return classifier
  
  classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 2)
  accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 1, n_jobs=1)
  mean = accuracies.mean()
  variance = accuracies.std()
  
  # Predicting the Test set results
  import sklearn
  y_pred = classifier.predict(X_test)
  y_pred = (y_pred &gt; 0.5)
  
  # Making the Confusion Matrix
  from sklearn.metrics import confusion_matrix
  cm = confusion_matrix(y_test, y_pred)
  
  # Predicting new observations
  test = pd.read_csv('test.csv',encoding = "cp1252")
  test = test.iloc[:, 1:].values
  test[:, 0] = labelencoder_X_0.transform(test[:, 0])
  test[:, 1] = labelencoder_X_1.transform(test[:, 1])
  test[:, 2] = labelencoder_X_2.transform(test[:, 2])
  test[:, 3] = labelencoder_X_3.transform(test[:, 3])
  test = onehotencoder.transform(test).toarray()
  test = test[:, 1:]
  new_prediction = classifier.predict_classes(sc.transform(test))
  new_prediction1 = (new_prediction &gt; 0.5)
  </code></pre>