TypeError: evaluate() got an unexpected keyword argument 'x'-CodePudding

Hey I currently ran into an error while training my model. Maybe someone is familiar with this kind of error and can help me.

I start my Training with following code:

history = student_model.fit(dataset = train_set,
                        epochs = epochs,
                        verbose = verbose,
                        validation_data = val_set,
                        callbacks = [
                            tf.keras.callbacks.CSVLogger(f"({log_dir}/train.log"),
                            tf.keras.callbacks.ModelCheckpoint(best_model_weights,
                                                               save_best_only = True,
                                                               save_weights_only = True),
                            tf.keras.callbacks.TensorBoard(log_dir=log_dir)
                        ])

So if I ran it i ran into an error which says that evaluate() got an unexpected keyword 'x'. Here you see the Traceback for this error. But I dont quite get what I should do to resolve it.

Epoch 1/10
      3/Unknown - 2s 26ms/step - loss: nan - binary_accuracy: 0.2708

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [11], in <cell line: 1>()
----> 1 history = student_model.fit(dataset = train_set,
      2                             epochs = epochs,
      3                             verbose = verbose,
      4                             validation_data = val_set,
      5                             callbacks = [
      6                                 tf.keras.callbacks.CSVLogger(f"({log_dir}/train.log"),
      7                                 tf.keras.callbacks.ModelCheckpoint(best_model_weights,
      8                                                                    save_best_only = True,
      9                                                                    save_weights_only = True),
     10                                 tf.keras.callbacks.TensorBoard(log_dir=log_dir)
     11                             ])

File ~/project_Bachelor/deep_Knowledge_Tracing/deepkt.py:134, in DKTModel.fit(self, dataset, epochs, verbose, callbacks, validation_data, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)
     64 def fit (self,
     65         dataset,
     66         epochs = 1,
   (...)
     73         validation_steps = None,
     74         validation_freq = 1):
     75     """Trains the model for a fixed number of epochs(iterations on a dataset).
     76     Arguments:
     77         dataset: A tf.data.dataset. Should return a tuple of '(inputs,(skills,targets))'
   (...)
    132 
    133     """
--> 134     return super(DKTModel, self).fit(x=dataset,
    135                                     epochs = epochs,
    136                                     verbose = verbose,
    137                                     callbacks = callbacks,
    138                                     validation_data = validation_data,
    139                                     shuffle = shuffle,
    140                                     initial_epoch = initial_epoch,
    141                                     steps_per_epoch = steps_per_epoch,
    142                                     validation_steps = validation_steps,
    143                                     validation_freq = validation_freq)

File ~/anaconda3/envs/project/lib/python3.9/site-packages/keras/utils/traceback_utils.py:67, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     65 except Exception as e:  # pylint: disable=broad-except
     66   filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67   raise e.with_traceback(filtered_tb) from None
     68 finally:
     69   del filtered_tb

File ~/anaconda3/envs/project/lib/python3.9/site-packages/keras/engine/training.py:1445, in Model.fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1431 if getattr(self, '_eval_data_handler', None) is None:
   1432   self._eval_data_handler = data_adapter.get_data_handler(
   1433       x=val_x,
   1434       y=val_y,
   (...)
   1443       model=self,
   1444       steps_per_execution=self._steps_per_execution)
-> 1445 val_logs = self.evaluate(
   1446     x=val_x,
   1447     y=val_y,
   1448     sample_weight=val_sample_weight,
   1449     batch_size=validation_batch_size or batch_size,
   1450     steps=validation_steps,
   1451     callbacks=callbacks,
   1452     max_queue_size=max_queue_size,
   1453     workers=workers,
   1454     use_multiprocessing=use_multiprocessing,
   1455     return_dict=True,
   1456     _use_cached_eval_dataset=True)
   1457 val_logs = {'val_'   name: val for name, val in val_logs.items()}
   1458 epoch_logs.update(val_logs)

TypeError: evaluate() got an unexpected keyword argument 'x'

CodePudding user response：

Update:

After @Djinn's comments I've looked into your GitHub repo. From the error I've seen that you are calling somewhere this function:

-> 1445 val_logs = self.evaluate(
   1446     x=val_x,
   1447     y=val_y,
   1448     sample_weight=val_sample_weight,
   1449     batch_size=validation_batch_size or batch_size,
   1450     steps=validation_steps,
   1451     callbacks=callbacks,
   1452     max_queue_size=max_queue_size,
   1453     workers=workers,
   1454     use_multiprocessing=use_multiprocessing,
   1455     return_dict=True,
   1456     _use_cached_eval_dataset=True)

But inside the file deepkt.py where you define the model, you've defined the function like this:

def evaluate(self, dataset, verbose = 1, steps = None, callbacks = None):
    ...

So when you call self.evaluate you should use dataset and not x and y like it looks like from your error, or change your custom evaluate function to have those arguments.

CodePudding user response：

you have code

history = student_model.fit(dataset = train_set,  etc

replace with

history = student_model.fit(x = train_set,  etc

also ensure verbose is one of 'auto', 0, 1, or 2