old_contents: from __future__ import print_function
  import numpy as np
  
  from keras_contrib.utils import test_utils
  from keras import optimizers
  from keras.models import Sequential
  from keras.layers import Dense, Activation
  from keras.utils import to_categorical
  
  
  def get_test_data():
      np.random.seed(1337)
      (x_train, y_train), _ = test_utils.get_test_data(num_train=1000,
                                                       num_test=200,
                                                       input_shape=(10,),
                                                       classification=True,
                                                       num_classes=2)
      y_train = to_categorical(y_train)
      return x_train, y_train
  
  
  def get_model(input_dim, num_hidden, output_dim):
      model = Sequential()
      model.add(Dense(num_hidden, input_shape=(input_dim,)))
      model.add(Activation('relu'))
      model.add(Dense(output_dim))
      model.add(Activation('softmax'))
      return model
  
  
  def _test_optimizer(optimizer, target=0.75):
      x_train, y_train = get_test_data()
      model = get_model(x_train.shape[1], 10, y_train.shape[1])
      model.compile(loss='categorical_crossentropy',
                    optimizer=optimizer,
                    metrics=['accuracy'])
      history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
      assert history.history['acc'][-1] >= target
      config = optimizers.serialize(optimizer)
      optim = optimizers.deserialize(config)
      new_config = optimizers.serialize(optim)
      assert config == new_config
  
new_contents: from __future__ import print_function
  import numpy as np
  
  from keras_contrib.utils import test_utils
  from keras import optimizers
  from keras.models import Sequential
  from keras.layers import Dense, Activation
  from keras.utils import to_categorical
  
  
  def get_test_data():
      np.random.seed(1337)
      (x_train, y_train), _ = test_utils.get_test_data(num_train=1000,
                                                       num_test=200,
                                                       input_shape=(10,),
                                                       classification=True,
                                                       num_classes=2)
      y_train = to_categorical(y_train)
      return x_train, y_train
  
  
  def get_model(input_dim, num_hidden, output_dim):
      model = Sequential()
      model.add(Dense(num_hidden, input_shape=(input_dim,)))
      model.add(Activation('relu'))
      model.add(Dense(output_dim))
      model.add(Activation('softmax'))
      return model
  
  
  def _test_optimizer(optimizer, target=0.75):
      x_train, y_train = get_test_data()
      model = get_model(x_train.shape[1], 10, y_train.shape[1])
      model.compile(loss='categorical_crossentropy',
                    optimizer=optimizer,
                    metrics=['accuracy'])
      history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
      assert history.history['acc'][-1] >= target
      config = optimizers.serialize(optimizer)
      custom_objects = {optimizer.__class__.__name__: optimizer.__class__}
      optim = optimizers.deserialize(config, custom_objects)
      new_config = optimizers.serialize(optim)
      assert config == new_config
  
subject: Fix optim test to use the custom_objects keyword argument. (#420)