title: How to fix 'Expected to see 2 array(s), but instead got the following list of 1 arrays'
body: <p>I'm training a multi class cnn model. <code>model.fit</code> method works fine but when I use <code>fit_generator</code> method the error in the title occurs.</p>
  
  <pre><code>y_train_age = utils.to_categorical(y_train_age, 117)
  y_test_age = utils.to_categorical(y_test_age, 117)
  
  y_train_gender = utils.to_categorical(y_train_gender, 2)
  y_test_gender = utils.to_categorical(y_test_gender, 2)
  
  y_train = np.concatenate((y_train_age, y_train_gender), axis=1)
  y_test = np.concatenate((y_test_age, y_test_gender), axis=1)
  
  print(x_train.shape)
  print(x_test.shape)
  print(y_train.shape)
  print(y_test.shape)
  </code></pre>
  
  <blockquote>
    <p>(15000, 100, 100, 3),(8708, 100, 100, 3),(15000, 119),(8708, 119)</p>
  </blockquote>
  
  <p>Model:</p>
  
  <pre><code>from keras import layers
  from keras.models import Model
  from keras.layers import Input, Dense, Activation
  from keras.layers import AveragePooling2D, MaxPooling2D, Flatten, Conv2D, ZeroPadding2D
  
  x_input = Input((100,100,3))
  
  x = Conv2D(64, (3,3))(x_input)
  x = Activation('relu')(x)
  x = MaxPooling2D((3, 3), strides=(2, 2))(x)
  
  x = Conv2D(64, (3,3))(x)
  x = Activation('relu')(x)
  x = MaxPooling2D((3, 3), strides=(2, 2))(x)
  
  x = Conv2D(128, (3,3))(x)
  x = Activation('relu')(x)
  x = MaxPooling2D((3, 3), strides=(2, 2))(x)
  
  x = Conv2D(256, (3,3))(x)
  x = Activation('relu')(x)
  x = MaxPooling2D((3, 3), strides=(2, 2))(x)
  
  x = Flatten()(x)
  x = Dense(64, activation='relu')(x)
  x = Dense(128, activation='relu')(x)
  x = Dense(128, activation='relu')(x)
  
  y1 = Dense(117, activation='softmax', name="Age")(x)
  y2 = Dense(2, activation='softmax', name="Gender")(x)
  
  model = Model(inputs=x_input, outputs=[y1, y2])
  model.compile(loss=['categorical_crossentropy', 'categorical_crossentropy'], optimizer='adam', metrics=['accuracy'])
  model.summary()
  </code></pre>
  
  <p>And the problem:</p>
  
  <pre><code>from keras.preprocessing.image import ImageDataGenerator
  
  model.fit_generator(ImageDataGenerator(shear_range=0.3, zoom_range=0.1, 
                      horizontal_flip=True).flow(x_train, y_train, 32),
                      steps_per_epoch=len(x_train) / 32,
                      epochs=5, verbose=1,
                      validation_data=(x_test, y_test))
  </code></pre>
  
  <p>The error:</p>
  
  <pre><code>ValueError: Error when checking model target: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays: [array([[0., 0., 0., ..., 0., 1., 0.],
         [0., 0., 0., ..., 0., 0., 1.],
         [0., 0., 0., ..., 0., 0., 1.],
         ...,
         [0., 0., 0., ..., 0., 0., 1.],
         [0., 0., 0., ..., 0., 0., 1....
  </code></pre>
  
  <p>Please help me, thanks.</p>
  
  <p><strong>THE ANSWER</strong></p>
  
  <pre><code>generator = ImageDataGenerator(...)
  
  def generate_data_generator(generator, X, Y1, Y2):
      genX1 = generator.flow(X, Y1, seed=7)
      genX2 = generator.flow(X, Y2, seed=7)
      while True:
          X1i = genX1.next()
          X2i = genX2.next()
          yield X1i[0], [X1i[1], X2i[1]]
  
  history = model.fit_generator(generate_data_generator(generator, x_train, y_train_age, y_train_gender),
                      steps_per_epoch=len(x_train) / 32,
                      epochs=5, 
                      verbose=1, 
                      callbacks = callbacks,
                      validation_data=(x_test, [y_test_age, y_test_gender]))
  </code></pre>