How to return history of validation loss in Keras

Question

Using Anaconda Python 2 7 Windows 10   I am training a language model using the Keras exmaple   print  Build model      model   Sequential   model add GRU 512  return sequences True  input shape  maxlen  len chars     model add Dropout 0 2   model add GRU 512  return sequences False   model add Dropout 0 2   model add Dense len chars    model add Activation  softmax     model compile loss  categorical crossentropy   optimizer  rmsprop    def sample a  temperature 1 0         helper function to sample an index from a probability array     a   np log a    temperature     a   np exp a    np sum np exp a       return np argmax np random multinomial 1  a  1       train the model  output generated text after each iteration for iteration in range 1  3       print       print  -    50      print  Iteration   iteration      model fit X  y  batch size 128  nb epoch 1      start index   random randint 0  len text  - maxlen - 1       for diversity in  0 2  0 5  1 0  1 2           print           print  ----- diversity    diversity           generated              sentence   text start index  start index   maxlen          generated    sentence         print  ----- Generating with seed       sentence                sys stdout write generated           for i in range 400               x   np zeros  1  maxlen  len chars                for t  char in enumerate sentence                   x 0  t  char indices char     1               preds   model predict x  verbose 0  0              next index   sample preds  diversity              next char   indices char next index               generated    next char             sentence   sentence 1     next char              sys stdout write next char              sys stdout flush           print     According to Keras documentation  the model fit method returns a History callback  which has a history attribute containing the lists of successive losses and other metrics   hist   model fit X  y  validation split 0 2  print hist history    After training my model  if I run print model history  I get the error    AttributeError   Sequential  object has no attribute  history    How do I return my model history after training my model with the above code   UPDATE  The issue was that   The following had to first be defined   from keras callbacks import History  history   History     The callbacks option had to be called  model fit X train  Y train  nb epoch 5  batch size 16  callbacks  history     But now if I print  print history History    it returns       even though I ran an iteration

User · Answer

I have also found that you can use verbose 2 to make keras print out the Losses   history   model fit X  Y  validation split 0 33  nb epoch 150  batch size 10  verbose 2    And that would print nice lines like this   Epoch 1 1  - 5s - loss  0 6046 - acc  0 9999 - val loss  0 4403 - val acc  0 9999   According to their documentation   verbose  0  1  or 2  Verbosity mode  0   silent  1   progress bar  2   one line per epoch

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Those who got still error like me   Convert model fit generator   to model fit

User · Answer

Thanks to Alloush  Following parameter must be included in model fit    validation data    x test  y test   If it is not defined  val acc and val loss will not be exist at output

User · Answer

Actually  you can also do it with the iteration method  Because sometimes we might need to use the iteration method instead of the built-in epochs method to visualize the training results after each iteration   history       Creating a empty list for holding the loss later for iteration in range 1  3       print       print  -    50      print  Iteration   iteration      result   model fit X  y  batch size 128  nb epoch 1   Obtaining the loss after each training     history append result history  loss     Now append the loss after the training to the list      start index   random randint 0  len text  - maxlen - 1  print history    This way allows you to get the loss you want while maintaining your iteration method

User · Answer

Another option is CSVLogger  https   keras io callbacks  csvlogger  It creates a csv file appending the result of each epoch  Even if you interrupt training  you get to see how it evolved

User · Answer

It s been solved   The losses only save to the History over the epochs  I was running iterations instead of using the Keras built in epochs option   so instead of doing 4 iterations I now have   model fit         nb epoch   4    Now it returns the loss for each epoch run   print hist history    loss    1 4358016599558268  1 399221191623641  1 381293383180471  h1 3758836857303727

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The dictionary with histories of  acc    loss   etc  is available and saved in hist history variable

User · Answer

For plotting the loss directly the following works   model    model fit X  Y  epochs       verbose 1   plt plot list model  history values    0   k-o

User · Answer

The following simple code works great for me       seqModel  model fit x train  y train            batch size        batch size            epochs            num epochs            validation data    x test  y test             shuffle           True            verbose 0  callbacks  TQDMNotebookCallback      for visualization   Make sure you assign the fit function to an output variable  Then you can access that variable very easily    visualizing losses and accuracy train loss   seqModel history  loss   val loss     seqModel history  val loss   train acc    seqModel history  acc   val acc      seqModel history  val acc   xc           range num epochs   plt figure   plt plot xc  train loss  plt plot xc  val loss    Hope this helps  source  https   keras io getting-started faq  how-can-i-record-the-training-validation-loss-accuracy-at-each-epoch

User · Answer

Just an example started from  history   model fit X  Y  validation split 0 33  nb epoch 150  batch size 10  verbose 0    You can use  print history history keys      to list all data in history   Then  you can print the history of validation loss like this    print history history  val loss

[python] How to return history of validation loss in Keras

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