KeyError: job exception: 'val_acc'

[DagonArises]

The error occurred before model evaluation:

Epoch 1/2                                            
422/422 - 3s - loss: 1.7380 - accuracy: 0.4183 - val_loss: 0.8082 - val_accuracy: 0.8570

Epoch 2/2                                            
422/422 - 2s - loss: 0.9614 - accuracy: 0.6891 - val_loss: 0.4583 - val_accuracy: 0.8960

  0%|          | 0/5 [00:05<?, ?trial/s, best loss=?]
job exception: 'val_acc'
  0%|          | 0/5 [00:05<?, ?trial/s, best loss=?]
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-2-cf1d0fa8fe34> in <module>()
     82                                           max_evals=5,
     83                                           trials=Trials(),
---> 84                                           notebook_name='Deep learning GridSearch')
     85     X_train, Y_train, X_test, Y_test = data()
     86     print("Evalutation of best performing model:")

~/anaconda3/lib/python3.6/site-packages/hyperas/optim.py in minimize(model, data, algo, max_evals, trials, functions, rseed, notebook_name, verbose, eval_space, return_space, keep_temp)
     67                                      notebook_name=notebook_name,
     68                                      verbose=verbose,
---> 69                                      keep_temp=keep_temp)
     70 
     71     best_model = None

~/anaconda3/lib/python3.6/site-packages/hyperas/optim.py in base_minimizer(model, data, functions, algo, max_evals, trials, rseed, full_model_string, notebook_name, verbose, stack, keep_temp)
    137              trials=trials,
    138              rstate=np.random.RandomState(rseed),
--> 139              return_argmin=True),
    140         get_space()
    141     )

~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)
    520             show_progressbar=show_progressbar,
    521             early_stop_fn=early_stop_fn,
--> 522             trials_save_file=trials_save_file,
    523         )
    524 

~/anaconda3/lib/python3.6/site-packages/hyperopt/base.py in fmin(self, fn, space, algo, max_evals, timeout, loss_threshold, max_queue_len, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin, show_progressbar, early_stop_fn, trials_save_file)
    697             show_progressbar=show_progressbar,
    698             early_stop_fn=early_stop_fn,
--> 699             trials_save_file=trials_save_file,
    700         )
    701 

~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)
    551 
    552     # next line is where the fmin is actually executed
--> 553     rval.exhaust()
    554 
    555     if return_argmin:

~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in exhaust(self)
    354     def exhaust(self):
    355         n_done = len(self.trials)
--> 356         self.run(self.max_evals - n_done, block_until_done=self.asynchronous)
    357         self.trials.refresh()
    358         return self

~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in run(self, N, block_until_done)
    290                 else:
    291                     # -- loop over trials and do the jobs directly
--> 292                     self.serial_evaluate()
    293 
    294                 self.trials.refresh()

~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in serial_evaluate(self, N)
    168                 ctrl = base.Ctrl(self.trials, current_trial=trial)
    169                 try:
--> 170                     result = self.domain.evaluate(spec, ctrl)
    171                 except Exception as e:
    172                     logger.error("job exception: %s" % str(e))

~/anaconda3/lib/python3.6/site-packages/hyperopt/base.py in evaluate(self, config, ctrl, attach_attachments)
    905                 print_node_on_error=self.rec_eval_print_node_on_error,
    906             )
--> 907             rval = self.fn(pyll_rval)
    908 
    909         if isinstance(rval, (float, int, np.number)):

~/Desktop/honours thesis/thesis/thesis proposal/NLP tutorial/temp_model.py in keras_fmin_fnct(space)

KeyError: 'val_acc'

My code is exactly the 'complete example', except added 'notebook_name' in optim.minimize. Thanks in advance.

[DagonArises]

please use 'val_accuracy' replace this 'val_acc' such as this code: history['val_acc'] ---> history['val_accuracy'] @xxs980 I moved the issue to this page. It has worked for the author's code. But when I implemented on NLP, 'val_accuracy' raised an error:

124/124 [==============================]             
- 129s 1s/step - loss: 0.1728 - val_loss: 0.1612    

0%| | 0/5 [04:24<?, ?trial/s, best loss=?] job exception: 'val_accuracy' 0%| | 0/5 [04:24<?, ?trial/s, best loss=?]

KeyError Traceback (most recent call last)

in () 20 best_run, best_model= optim.minimize(model= create_model_gru, data= data, 21 algo= tpe.suggest, max_evals= 5, trials= Trials(), ---> 22 eval_space= True, notebook_name= 'Deep learning GridSearch') 23 24 print("Evalutation of best performing model:") ~/anaconda3/lib/python3.6/site-packages/hyperas/optim.py in minimize(model, data, algo, max_evals, trials, functions, rseed, notebook_name, verbose, eval_space, return_space, keep_temp) 67 notebook_name=notebook_name, 68 verbose=verbose, ---> 69 keep_temp=keep_temp) 70 71 best_model = None ~/anaconda3/lib/python3.6/site-packages/hyperas/optim.py in base_minimizer(model, data, functions, algo, max_evals, trials, rseed, full_model_string, notebook_name, verbose, stack, keep_temp) 137 trials=trials, 138 rstate=np.random.RandomState(rseed), --> 139 return_argmin=True), 140 get_space() 141 ) ~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file) 520 show_progressbar=show_progressbar, 521 early_stop_fn=early_stop_fn, --> 522 trials_save_file=trials_save_file, 523 ) 524 ~/anaconda3/lib/python3.6/site-packages/hyperopt/base.py in fmin(self, fn, space, algo, max_evals, timeout, loss_threshold, max_queue_len, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin, show_progressbar, early_stop_fn, trials_save_file) 697 show_progressbar=show_progressbar, 698 early_stop_fn=early_stop_fn, --> 699 trials_save_file=trials_save_file, 700 ) 701 ~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file) 551 552 # next line is where the fmin is actually executed --> 553 rval.exhaust() 554 555 if return_argmin: ~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in exhaust(self) 354 def exhaust(self): 355 n_done = len(self.trials) --> 356 self.run(self.max_evals - n_done, block_until_done=self.asynchronous) 357 self.trials.refresh() 358 return self ~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in run(self, N, block_until_done) 290 else: 291 # -- loop over trials and do the jobs directly --> 292 self.serial_evaluate() 293 294 self.trials.refresh() ~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in serial_evaluate(self, N) 168 ctrl = base.Ctrl(self.trials, current_trial=trial) 169 try: --> 170 result = self.domain.evaluate(spec, ctrl) 171 except Exception as e: 172 logger.error("job exception: %s" % str(e)) ~/anaconda3/lib/python3.6/site-packages/hyperopt/base.py in evaluate(self, config, ctrl, attach_attachments) 905 print_node_on_error=self.rec_eval_print_node_on_error, 906 ) --> 907 rval = self.fn(pyll_rval) 908 909 if isinstance(rval, (float, int, np.number)): ~/Desktop/honours thesis/thesis/thesis proposal/NLP tutorial/temp_model.py in keras_fmin_fnct(space) KeyError: 'val_accuracy' ``` My edited code: ``` def data(): # Load train= pd.read_csv('Toxic comment data/jigsaw-toxic-comment-train.csv') train.drop(['severe_toxic','obscene','threat','insult','identity_hate'],axis=1,inplace=True) train= train.iloc[:12000,:] # Tokenization xtr, xte, ytr, yte= train_test_split(train['comment_text'].values, train['toxic'].values, stratify= train['toxic'].values, random_state= 42, test_size= 0.2, shuffle= True) tok= text.Tokenizer(num_words= None) tok.fit_on_texts(list(xtr)+ list(xte)) input_dim= len(tok.word_index)+1 input_length= train['comment_text'].apply(lambda x: len(str(x).split())).max() xtr_seq= tok.texts_to_sequences(xtr); xte_seq= tok.texts_to_sequences(xte) xtr_pad= sequence.pad_sequences(xtr_seq, maxlen= input_length) xte_pad= sequence.pad_sequences(xte_seq, maxlen= input_length) # Load GloVe embeddings embedding_dict=dict() f= open('GloVe/glove.6B.100d.txt') output_dim= 100 for line in f: values= line.split() word= values[0]; coefs= asarray(values[1:], dtype= 'float32') embedding_dict[word]= coefs f.close() Emat= zeros((input_dim, output_dim)) for word, i in tok.word_index.items(): embedding_vector= embedding_dict.get(word) if embedding_vector is not None: Emat[i]= embedding_vector print('Shape of input:', xtr_pad.shape) print('Shape of embedding weight:', Emat.shape) return xtr_pad, ytr, xte_pad, yte, input_dim, input_length, Emat def create_model_gru(xtr_pad, ytr, xte_pad, yte, input_dim, input_length, Emat): model= Sequential() model.add(Embedding(input_dim, 100, input_length= input_length, weights= [Emat], trainable= False)) model.add(Dropout({{choice([0.2, 0.3, 0.5])}})) model.add(GRU({{choice([100, 150, 200])}})) model.add(Dense(1, activation= 'sigmoid')) model.compile(loss='binary_crossentropy', optimizer= {{choice(['rmsprop', 'adam'])}}) results= model.fit(xtr_pad, ytr, epochs= 2, verbose= 1, validation_split=0.1, batch_size= 70) val_accuracy = np.amax(results.history['val_accuracy']) print('Best validation acc of epoch:', val_accuracy) return {'loss': -val_accuracy, 'status': STATUS_OK, 'model': model} if __name__ == '__main__': xtr_pad, ytr, xte_pad, yte, input_dim, input_length, Emat= data() best_run, best_model= optim.minimize(model= create_model_gru, data= data, algo= tpe.suggest, max_evals= 5, trials= Trials(), eval_space= True, notebook_name= 'Deep learning GridSearch') print("Evalutation of best performing model:") print(best_model.evaluate(xte_pad, yte)) print("Best performing model chosen hyper-parameters:") print(best_run) ```

[DagonArises]

At the moment val_loss works but strangely val_accuracy returns the error shown above.

val_loss = np.amin(results.history['val_loss']) 
return {'loss': val_loss, 'status': STATUS_OK, 'model': model}