title: Exception: Error when checking model target: expected dense_3 to have shape (None, 1000) but got array with shape (32, 2)
body: <p>How do I create a VGG-16 sequence for my data?</p>
  
  <p>The data has the following :</p>
  
  <pre><code>model = Sequential() 
  model.add(ZeroPadding2D((1, 1), input_shape=(3, img_width, img_height))) model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1')) model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
  model.add(MaxPooling2D((2, 2), strides=(2, 2)))
  
  model.add(ZeroPadding2D((1, 1))) 
  model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1')) model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
  model.add(MaxPooling2D((2, 2), strides=(2, 2)))
  
  model.add(ZeroPadding2D((1, 1))) 
  model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1')) model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
  model.add(ZeroPadding2D((1, 1))) 
  model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))
  
  model.add(ZeroPadding2D((1, 1))) 
  model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1')) model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
  model.add(ZeroPadding2D((1, 1))) 
  model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))
  
  model.add(ZeroPadding2D((1, 1))) 
  model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1')) model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
  model.add(ZeroPadding2D((1, 1))) 
  model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3')) model.add(MaxPooling2D((2, 2), strides=(2, 2)))
  
  model.add(Flatten()) 
  model.add(Dense(4096, activation='relu'))
  model.add(Dropout(0.5)) 
  model.add(Dense(4096, activation='relu'))
  model.add(Dropout(0.5)) 
  model.add(Dense(1000, activation='softmax'))
  
  sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
  model.compile(optimizer=sgd, loss='categorical_crossentropy')
  
  train_datagen = ImageDataGenerator(
          rescale=1./255,
          shear_range=0.2,
          zoom_range=0.2,
          horizontal_flip=True)
  
  test_datagen = ImageDataGenerator(rescale=1./255)
  
  train_generator = train_datagen.flow_from_directory(
          train_data_dir,
          target_size=(img_width, img_height),
          batch_size=32)
  
  validation_generator = test_datagen.flow_from_directory(
          validation_data_dir,
          target_size=(img_width, img_height),
          batch_size=32)
  
  model.fit_generator(
          train_generator,
          samples_per_epoch=2000,
          nb_epoch=1,
          verbose=1,
          validation_data=validation_generator,
          nb_val_samples=800)
  
  json_string = model.to_json()  
  open('my_model_architecture.json','w').write(json_string) 
  model.save_weights('Second_try.h5')
  </code></pre>
  
  <p>I got an error:</p>
  
  <blockquote>
    <p>Exception: Error when checking model target: expected dense_3 to have
    shape (None, 32) but got array with shape (32, 2)</p>
  </blockquote>
  
  <p>How do I change <code>Dense</code> to make it work?</p>