无法为机器学习创建 model

Question

I am creating a python application for Detecting Brain Tumor.

About the data: The dataset contains 2 folders: yes and no which contains 253 Brain MRI Images. The folder yes contains 155 Brain MRI Images that are tumorous and the folder no contains 98 Brain MRI Images that are non-tumorous.

# tensorboard
log_file_name = f'brain_tumor_detection_cnn_{int(time.time())}'
tensorboard = TensorBoard(log_dir=f'logs/{log_file_name}')

# checkpoint
# unique file name that will include the epoch and the validation (development) accuracy
filepath="cnn-parameters-improvement-{epoch:02d}-{val_acc:.2f}"
# save the model with the best validation (development) accuracy till now
checkpoint = ModelCheckpoint("models/{}.model".format(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max'))


# ## Train the model

model.fit(x=X_train, y=y_train, batch_size=32, epochs=10, validation_data=(X_val, y_val), callbacks=[tensorboard, checkpoint])

While Training the Model, I get the following error:

Epoch 1/10
91/91 [==============================] - ETA: 0s - loss: 0.7457 - accuracy: 0.6735
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
c:\python38\lib\site-packages\tensorflow\python\keras\callbacks.py in _get_file_path(self, epoch, logs)
   1243         # placeholders can cause formatting to fail.
-> 1244         return self.filepath.format(epoch=epoch + 1, **logs)
   1245       except KeyError as e:

KeyError: 'val_acc'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
<ipython-input-20-b50661a1419b> in <module>
      1 start_time = time.time()
      2 
----> 3 model.fit(x=X_train, y=y_train, batch_size=32, epochs=10, validation_data=(X_val, y_val), callbacks=[tensorboard, checkpoint])
      4 
      5 end_time = time.time()

c:\python38\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
     64   def _method_wrapper(self, *args, **kwargs):
     65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
---> 66       return method(self, *args, **kwargs)
     67 
     68     # Running inside `run_distribute_coordinator` already.

c:\python38\lib\site-packages\tensorflow\python\keras\engine\training.py in 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, **kwargs)
    811           epoch_logs.update(val_logs)
    812 
--> 813         callbacks.on_epoch_end(epoch, epoch_logs)
    814         if self.stop_training:
    815           break

c:\python38\lib\site-packages\tensorflow\python\keras\callbacks.py in on_epoch_end(self, epoch, logs)
    363     logs = self._process_logs(logs)
    364     for callback in self.callbacks:
--> 365       callback.on_epoch_end(epoch, logs)
    366 
    367   def on_train_batch_begin(self, batch, logs=None):

c:\python38\lib\site-packages\tensorflow\python\keras\callbacks.py in on_epoch_end(self, epoch, logs)
   1175           self._save_model(epoch=epoch, logs=logs)
   1176       else:
-> 1177         self._save_model(epoch=epoch, logs=logs)
   1178     if self.model._in_multi_worker_mode():
   1179       # For multi-worker training, back up the weights and current training

c:\python38\lib\site-packages\tensorflow\python\keras\callbacks.py in _save_model(self, epoch, logs)
   1194                   int) or self.epochs_since_last_save >= self.period:
   1195       self.epochs_since_last_save = 0
-> 1196       filepath = self._get_file_path(epoch, logs)
   1197 
   1198       try:

c:\python38\lib\site-packages\tensorflow\python\keras\callbacks.py in _get_file_path(self, epoch, logs)
   1244         return self.filepath.format(epoch=epoch + 1, **logs)
   1245       except KeyError as e:
-> 1246         raise KeyError('Failed to format this callback filepath: "{}". '
   1247                        'Reason: {}'.format(self.filepath, e))
   1248     else:

KeyError: 'Failed to format this callback filepath: "models/cnn-parameters-improvement-{epoch:02d}-{val_acc:.2f}.model". Reason: \'val_acc\''

Answer 1

If you are using tensorflow with keras, then try using val_accuracy instead of val_acc