for i in range(epochs):
generator = data_generator(train_descriptions,encoding_train,word_to_idx,max_len,batch_size)
model.fit_generator(generator,epochs=1,initial_epoch=1,steps_per_epoch=steps,verbose=1)
model.save(’./model_weight/model_’+str(i)+’.h5’)
KeyError Traceback (most recent call last)
in
1 for i in range(epochs):
2 generator = data_generator(train_descriptions,encoding_train,word_to_idx,max_len,batch_size)
----> 3 model.fit_generator(generator,epochs=1,initial_epoch=1,steps_per_epoch=steps,verbose=1)
4 model.save(’./model_weight/model_’+str(i)+’.h5’)
~\Anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py in new_func(*args, **kwargs)
322 ‘in a future version’ if date is None else (‘after %s’ % date),
323 instructions)
–> 324 return func(*args, **kwargs)
325 return tf_decorator.make_decorator(
326 func, new_func, ‘deprecated’,
~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1477 use_multiprocessing=use_multiprocessing,
1478 shuffle=shuffle,
-> 1479 initial_epoch=initial_epoch)
1480
1481 @deprecation.deprecated(
~\Anaconda3\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.
~\Anaconda3\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)
813 workers=workers,
814 use_multiprocessing=use_multiprocessing,
–> 815 model=self)
816
817 # Container that configures and calls tf.keras.Callbacks.
~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in init(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model)
1110 use_multiprocessing=use_multiprocessing,
1111 distribution_strategy=ds_context.get_strategy(),
-> 1112 model=model)
1113
1114 strategy = ds_context.get_strategy()
~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in init(self, x, y, sample_weights, workers, use_multiprocessing, max_queue_size, model, **kwargs)
770 # Since we have to know the dtype of the python generator when we build the
771 # dataset, we have to look at a batch to infer the structure.
–> 772 peek, x = self._peek_and_restore(x)
773 assert_not_namedtuple(peek)
774 peek = self._standardize_batch(peek)
~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in _peek_and_restore(x)
828 @staticmethod
829 def _peek_and_restore(x):
–> 830 peek = next(x)
831 return peek, itertools.chain([peek], x)
832
in data_generator(train_descriptions, encoding_train, word_to_idx, max_len, batch_size)
8 n +=1
9
—> 10 photo = encoding_train[key+".jpg"]
11 for desc in desc_list:
12
KeyError: ‘2513260012_03d33305cf.jpg’
.