自定义回调中的访问损失和模型

Question

我读了这个 https://www.tensorflow.org/guide/keras/custom_callback，但我不知道如何获得所有其他参数。

这是我的代码

 (hits, ndcgs) = evaluate_model(model, testRatings, testNegatives, topK, evaluation_threads)
  hr, ndcg, loss = np.array(hits).mean(), np.array(ndcgs).mean(), hist.history['loss'][0]
  print('Iteration %d [%.1f s]: HR = %.4f, NDCG = %.4f, loss = %.4f [%.1f s]' 
                  % (epoch,  t2-t1, hr, ndcg, loss, time()-t2))
 if hr > best_hr:
     best_hr, best_ndcg, best_iter = hr, ndcg, epoch
 if args.out > 0:
     model.save(model_out_file, overwrite=True)

如您所见，我需要 model、hist 和 model.save。有没有办法在自定义回调中使用这三个参数？这样我就可以将所有这些写入自定义回调中。

class CustomCallback(keras.callbacks.Callback):

   def on_epoch_end(self, logs=None):
       keys = list(logs.keys())
       print("Stop training; got log keys: {}".format(keys))

Answer 1

该模型是一个 attribute of tf.keras.callbacks.Callback, so you can access it directly with self.model. For accessing the value of the loss, you can use the "logs" object that is passed to the methods of tf.keras.callbacks.Callback，它将包含一个名为“loss”的键。

如果你需要访问其他变量（在训练期间不会改变），那么你可以将它们设置为回调的实例变量，并通过定义 __init__函数。

class CustomCallback(keras.callbacks.Callback):
   def __init__(self, testRatings, testNegatives, topK, evaluation_threads):
       super().__init__()
       self.testRatings = testRatings
       self.testNegatives = testNegatives
       self.topK = topK
       self.evaluation_threads = evaluation_threads

   def on_epoch_end(self, epoch, logs=None):
       logs = logs or {}
       current_loss = logs.get("loss")
       if current_loss:
           print("my_loss: ", current_loss)
       print("my_model", self.model)
       # the attributes are accessble with self
       print("my topK atributes", self.topK)

# you can then create the callback by passing the correct attributes
my_callback = CustomCallback(testRatings, testNegatives, topK, evaluation_threads)

注意：如果你想做的是在每个时期之间评估模型，并在模型获得最佳指标时保存模型，我建议你看看:

fit function，真正可以提供测试集的地方
metrics module，提供将在训练集和测试集上计算的指标
ModelCheckpoint callback，将在每个 epoch 保存模型，如果提供选项 save_best_only

自定义回调中的访问损失和模型

Access loss and model in a custom callback

python

callback

keras

tensorflow