code: def model(datagen, X_train, Y_train, X_test, Y_test):
      batch_size = 32
      nb_epoch = 200
  
      # input image dimensions
      img_rows, img_cols = 32, 32
      # the CIFAR10 images are RGB
      img_channels = 3
  
      model = Sequential()
  
      model.add(
          Convolution2D(
              32,
              3,
              3,
              border_mode="same",
              input_shape=X_train.shape[1:],
          )
      )
      model.add(Activation("relu"))
      model.add(Convolution2D(32, 3, 3))
      model.add(Activation("relu"))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout({{uniform(0, 1)}}))
  
      model.add(Convolution2D(64, 3, 3, border_mode="same"))
      model.add(Activation("relu"))
      model.add(Convolution2D(64, 3, 3))
      model.add(Activation("relu"))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout({{uniform(0, 1)}}))
  
      model.add(Flatten())
      model.add(Dense(512))
      model.add(Activation("relu"))
      model.add(Dropout(0.5))
      model.add(Dense(nb_classes))
      model.add(Activation("softmax"))
  
      # let's train the model using SGD + momentum (how original).
      sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
      model.compile(
          loss="categorical_crossentropy",
          optimizer=sgd,
          metrics=["accuracy"],
      )
  
      # fit the model on the batches generated by datagen.flow()
      model.fit_generator(
          datagen.flow(X_train, Y_train, batch_size=batch_size),
          samples_per_epoch=X_train.shape[0],
          nb_epoch=nb_epoch,
          validation_data=(X_test, Y_test),
      )
  
      score, acc = model.evaluate(X_test, Y_test, verbose=0)
  
      return {"loss": -acc, "status": STATUS_OK, "model": model}
  
file_path: hyperas/examples/cifar_generator_cnn.py
imports: from __future__ import print_function
  from hyperopt import Trials, STATUS_OK, tpe
  from hyperas import optim
  from hyperas.distributions import uniform
  from keras.models import Sequential
  from keras.layers.core import Dense, Dropout, Activation, Flatten
  from keras.layers.convolutional import Convolution2D, MaxPooling2D
  from keras.optimizers import SGD
  from keras.preprocessing.image import ImageDataGenerator
  from keras.datasets import cifar10
  from keras.utils import np_utils
label_desc: Use image node_id 2 for calling a global function with example usage: model(datagen, X_train, Y_train, X_test, Y_test) and returns: dict