Я пытаюсь обучить модель с помощью простого генератора данных, используя Tensorflow.keras.utils.Sequence, но имеет ошибку атрибута numpy.
from tensorflow.keras.utils import Sequence
class DataGenerator(Sequence):
def __init__(self, dataset, batch_size=16, dim=(1), shuffle=True):
'Initialization'
self.dim = dim
self.batch_size = batch_size
self.dataset = dataset
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
'Denotes the number of batches per epoch'
return tf.math.ceil(len(self.dataset) / self.batch_size)
def __getitem__(self, index):
'Generate one batch of data'
# Generate indexes of the batch
indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
# Find list of IDs
list_IDs_temp = [self.dataset.index[k] for k in indexes]
# Generate data
y = dataset.loc[list_IDs_temp,['rating']].to_numpy()
X = dataset.loc[list_IDs_temp,['user_id', 'item_id']].to_numpy()
return (X, y)
def on_epoch_end(self):
'Updates indexes after each epoch'
self.indexes = np.arange(len(dataset))
if self.shuffle == True:
np.random.shuffle(self.indexes)
И примерку:
history = model.fit(train_generator,use_multiprocessing=True, steps_per_epoch=1, epochs=10, verbose=0)
сообщение об ошибке:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-23-c17d5c8aa46c> in <module>()
----> 1 history = model.fit(train_generator,use_multiprocessing=True, steps_per_epoch=1, epochs=10, verbose=0)
5 frames
/tensorflow-2.1.0/python3.6/tensorflow_core/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_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
817 max_queue_size=max_queue_size,
818 workers=workers,
--> 819 use_multiprocessing=use_multiprocessing)
820
821 def evaluate(self,
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
233 max_queue_size=max_queue_size,
234 workers=workers,
--> 235 use_multiprocessing=use_multiprocessing)
236
237 total_samples = _get_total_number_of_samples(training_data_adapter)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py in _process_training_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, steps_per_epoch, validation_split, validation_data, validation_steps, shuffle, distribution_strategy, max_queue_size, workers, use_multiprocessing)
591 max_queue_size=max_queue_size,
592 workers=workers,
--> 593 use_multiprocessing=use_multiprocessing)
594 val_adapter = None
595 if validation_data:
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py in _process_inputs(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)
704 max_queue_size=max_queue_size,
705 workers=workers,
--> 706 use_multiprocessing=use_multiprocessing)
707
708 return adapter
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, standardize_function, shuffle, workers, use_multiprocessing, max_queue_size, **kwargs)
941 raise ValueError("`sample_weight` argument is not supported when using "
942 "`keras.utils.Sequence` as input.")
--> 943 self._size = len(x)
944 self._shuffle_sequence = shuffle
945 super(KerasSequenceAdapter, self).__init__(
/tensorflow-2.1.0/python3.6/tensorflow_core/python/framework/ops.py in __index__(self)
860
861 def __index__(self):
--> 862 return self._numpy().__index__()
863
864 def __bool__(self):
AttributeError: 'numpy.float32' object has no attribute '__index__'
Есть предложения? Спасибо