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我正在尝试使用 keras hyperas 优化 IMBD 数据集的超参数，但出现错误。我用这个（https://www.kaggle.com/kt66nf/hyperparameter-optimization-using-keras-hyperas）代码作为参考

代码

def vectorize_sequences(sequences, dimension=10000):
    # Create an all-zero matrix of shape (len(sequences), dimension)
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
      results[i, sequence] = 1.  # set specific indices of results[i] to 1s
      return results


 
def data():
 (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
 x_train = vectorize_sequences(train_data)  #vectorized training data
 x_test = vectorize_sequences(test_data)

 y_train = np.asarray(train_labels).astype('float32')  # vectorized labels
 y_test = np.asarray(test_labels).astype('float32')
 return x_train, y_train, x_test, y_test

def create_model(x_train, y_train, x_test, y_test):

  model = models.Sequential()
  model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))
  model.add(layers.Dense(16, activation='relu'))
  model.add(Dropout({{uniform(0.5, 1)}}))
  model.add(layers.Dense(64, activation='relu'))
  model.add(layers.Dense(64, activation='relu'))
  model.add(Dropout({{uniform(0.5, 1)}}))
  model.add(layers.Dense(1, activation='sigmoid'))

  model.compile(loss='binary_crossentropy', metrics=['accuracy'],
                optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})
               
                
  model.fit(x_train, y_train,
                     batch_size={{choice([16, 32, 64])}},
                     epochs={{choice([25, 50, 75, 100])}},
                     validation_data=(x_test, y_test))
                                   
  score, acc = model.evaluate(x_test, y_test, verbose=0)
  print('Test accuracy:', acc)
  return {'loss': -acc, 'status': STATUS_OK, 'model': model}

***then I loaded google drive and set the path to the notebook

best_run, best_model = optim.minimize(model= create_model, 
                                      data=data, 
                                      max_evals=15, 
                                      algo=tpe.suggest, 
                                      notebook_name='Copy of imbd',  #name of the notebook 
                                      trials= Trials())

这最后一个代码是我收到错误“文件”，第 182 行] ^ SyntaxError：无效语法的地方

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