如何使用hyperopt进行Keras深度学习网络的超参数优化？

2 回答

• 3

  我在Hyperas上取得了很大的成功 . 以下是我学会使其发挥作用的事情 .

  1）从终端（而不是从Ipython笔记本）运行它作为python脚本2）确保您的代码中没有任何注释（Hyperas不喜欢注释！）3）将数据和模型封装在一个功能如hyperas自述文件中所述 .

  下面是一个适用于我的Hyperas脚本示例（按照上面的说明） .

  from __future__ import print_function
  
  from hyperopt import Trials, STATUS_OK, tpe
  from keras.datasets import mnist
  from keras.layers.core import Dense, Dropout, Activation
  from keras.models import Sequential
  from keras.utils import np_utils
  import numpy as np
  from hyperas import optim
  from keras.models import model_from_json
  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 , Adam
  import tensorflow as tf
  from hyperas.distributions import choice, uniform, conditional
  __author__ = 'JOnathan Hilgart'
  
  
  
  def data():
      """
      Data providing function:
  
      This function is separated from model() so that hyperopt
      won't reload data for each evaluation run.
      """
      import numpy as np
      x = np.load('training_x.npy')
      y = np.load('training_y.npy')
      x_train = x[:15000,:]
      y_train = y[:15000,:]
      x_test = x[15000:,:]
      y_test = y[15000:,:]
      return x_train, y_train, x_test, y_test
  
  
  def model(x_train, y_train, x_test, y_test):
      """
      Model providing function:
  
      Create Keras model with double curly brackets dropped-in as needed.
      Return value has to be a valid python dictionary with two customary keys:
          - loss: Specify a numeric evaluation metric to be minimized
          - status: Just use STATUS_OK and see hyperopt documentation if not feasible
      The last one is optional, though recommended, namely:
          - model: specify the model just created so that we can later use it again.
      """
      model_mlp = Sequential()
      model_mlp.add(Dense({{choice([32, 64,126, 256, 512, 1024])}},
                          activation='relu', input_shape= (2,)))
      model_mlp.add(Dropout({{uniform(0, .5)}}))
      model_mlp.add(Dense({{choice([32, 64, 126, 256, 512, 1024])}}))
      model_mlp.add(Activation({{choice(['relu', 'sigmoid'])}}))
      model_mlp.add(Dropout({{uniform(0, .5)}}))
      model_mlp.add(Dense({{choice([32, 64, 126, 256, 512, 1024])}}))
      model_mlp.add(Activation({{choice(['relu', 'sigmoid'])}}))
      model_mlp.add(Dropout({{uniform(0, .5)}}))
      model_mlp.add(Dense({{choice([32, 64, 126, 256, 512, 1024])}}))
      model_mlp.add(Activation({{choice(['relu', 'sigmoid'])}}))
      model_mlp.add(Dropout({{uniform(0, .5)}}))
      model_mlp.add(Dense(9))
      model_mlp.add(Activation({{choice(['softmax','linear'])}}))
      model_mlp.compile(loss={{choice(['categorical_crossentropy','mse'])}}, metrics=['accuracy'],
                    optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})
  
  
  
      model_mlp.fit(x_train, y_train,
                batch_size={{choice([16, 32, 64, 128])}},
                epochs=50,
                verbose=2,
                validation_data=(x_test, y_test))
      score, acc = model_mlp.evaluate(x_test, y_test, verbose=0)
      print('Test accuracy:', acc)
      return {'loss': -acc, 'status': STATUS_OK, 'model': model_mlp}
  
      enter code here
  
  if __name__ == '__main__':
      import gc; gc.collect()
  
      with K.get_session(): ## TF session
          best_run, best_model = optim.minimize(model=model,
                                                data=data,
                                                algo=tpe.suggest,
                                                max_evals=2,
                                                trials=Trials())
          X_train, Y_train, X_test, Y_test = data()
          print("Evalutation of best performing model:")
          print(best_model.evaluate(X_test, Y_test))
          print("Best performing model chosen hyper-parameters:")
          print(best_run)
  

  它由不同的gc序列引起，如果先python收集会话，程序将退出成功，如果python首先收集swig内存（tf_session），程序退出失败 .

  你可以通过以下方式强制python到del session：

  del session
  

  或者如果您使用的是keras，则无法获取会话实例，您可以在代码末尾运行以下代码：

  import gc; gc.collect()
  

  回复于 2024-05-13T10:58:58+08:00
• 9

  这也可以是另一种方法：

  from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
  from sklearn.metrics import roc_auc_score
  import sys
  
  X = []
  y = []
  X_val = []
  y_val = []
  
  space = {'choice': hp.choice('num_layers',
                      [ {'layers':'two', },
                      {'layers':'three',
                      'units3': hp.uniform('units3', 64,1024), 
                      'dropout3': hp.uniform('dropout3', .25,.75)}
                      ]),
  
              'units1': hp.uniform('units1', 64,1024),
              'units2': hp.uniform('units2', 64,1024),
  
              'dropout1': hp.uniform('dropout1', .25,.75),
              'dropout2': hp.uniform('dropout2',  .25,.75),
  
              'batch_size' : hp.uniform('batch_size', 28,128),
  
              'nb_epochs' :  100,
              'optimizer': hp.choice('optimizer',['adadelta','adam','rmsprop']),
              'activation': 'relu'
          }
  
  def f_nn(params):   
      from keras.models import Sequential
      from keras.layers.core import Dense, Dropout, Activation
      from keras.optimizers import Adadelta, Adam, rmsprop
  
      print ('Params testing: ', params)
      model = Sequential()
      model.add(Dense(output_dim=params['units1'], input_dim = X.shape[1])) 
      model.add(Activation(params['activation']))
      model.add(Dropout(params['dropout1']))
  
      model.add(Dense(output_dim=params['units2'], init = "glorot_uniform")) 
      model.add(Activation(params['activation']))
      model.add(Dropout(params['dropout2']))
  
      if params['choice']['layers']== 'three':
          model.add(Dense(output_dim=params['choice']['units3'], init = "glorot_uniform")) 
          model.add(Activation(params['activation']))
          model.add(Dropout(params['choice']['dropout3']))    
  
      model.add(Dense(1))
      model.add(Activation('sigmoid'))
      model.compile(loss='binary_crossentropy', optimizer=params['optimizer'])
  
      model.fit(X, y, nb_epoch=params['nb_epochs'], batch_size=params['batch_size'], verbose = 0)
  
      pred_auc =model.predict_proba(X_val, batch_size = 128, verbose = 0)
      acc = roc_auc_score(y_val, pred_auc)
      print('AUC:', acc)
      sys.stdout.flush() 
      return {'loss': -acc, 'status': STATUS_OK}
  
  
  trials = Trials()
  best = fmin(f_nn, space, algo=tpe.suggest, max_evals=50, trials=trials)
  print 'best: '
  print best
  

  Source

  回复于 2024-05-13T10:58:58+08:00