Я получил
RuntimeError: Тензор Dst не инициализируется
во время обучения моей нейронной сети. В частности, кажется, что ошибка появляется, когда в моем пользовательском Callback я получаю прогнозы, используя self.model.predict(self.dataset), потому что трассировка стека говорит
Файл "mlp_keras.py", строка 20, в предсказаниях on_epoch_end = self.model.predict (self.dataset)
Это полная трассировка стека:
Traceback (most recent call last):
File "mlp_keras.py", line 150, in <module>
callbacks=[KendallTauHistory(training_dataset, training_dataset_labels, groups_id_count)])
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training.py", line 819, in fit
use_multiprocessing=use_multiprocessing)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 397, in fit
prefix='val_')
File "/usr/lib64/python2.7/contextlib.py", line 24, in __exit__
self.gen.next()
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 771, in on_epoch
self.callbacks.on_epoch_end(epoch, epoch_logs)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/callbacks.py", line 302, in on_epoch_end
callback.on_epoch_end(epoch, logs)
File "mlp_keras.py", line 20, in on_epoch_end
predictions = self.model.predict(self.dataset)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training.py", line 1013, in predict
use_multiprocessing=use_multiprocessing)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 498, in predict
workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 426, in _model_iteration
use_multiprocessing=use_multiprocessing)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 706, in _process_inputs
use_multiprocessing=use_multiprocessing)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/data_adapter.py", line 357, in __init__
dataset = self.slice_inputs(indices_dataset, inputs)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/data_adapter.py", line 383, in slice_inputs
dataset_ops.DatasetV2.from_tensors(inputs).repeat()
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 566, in from_tensors
return TensorDataset(tensors)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 2765, in __init__
element = structure.normalize_element(element)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/data/util/structure.py", line 113, in normalize_element
ops.convert_to_tensor(t, name="component_%d" % i))
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/framework/ops.py", line 1314, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py", line 52, in _default_conversion_function
return constant_op.constant(value, dtype, name=name)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/framework/constant_op.py", line 258, in constant
allow_broadcast=True)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/framework/constant_op.py", line 266, in _constant_impl
t = convert_to_eager_tensor(value, ctx, dtype)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/framework/constant_op.py", line 96, in convert_to_eager_tensor
return ops.EagerTensor(value, ctx.device_name, dtype)
RuntimeError: Dst tensor is not initialized.
Это мой код:
class KendallTauHistory(Callback):
def __init__(self, dataset, y_true, groups):
self.y_true = y_true
self.dataset = dataset
self.groups = groups
def on_epoch_end(self, epoch, logs=None):
predictions = self.model.predict(self.dataset)
predictions = predictions.flatten()
predictions = list(map(lambda element: element + np.random.uniform(0.0, 1.0) * 0.02 - 0.01, predictions))
# For batch training
ranked_predictions = np.array([])
kendalls = np.array([])
start_range = 0
for group in self.groups:
end_range = (start_range + group[1]) # Batch is a group of words with same group id
batch_predictions = predictions[start_range:end_range]
batch_labels = self.y_true[start_range:end_range]
batch_predictions = list(map(lambda element: element + np.random.uniform(0.0, 1.0) * 0.02 - 0.01, batch_predictions))
ranked_predictions = np.append(ranked_predictions, np.floor(rankdata(batch_predictions)))
kendalls = np.append(kendalls, kendalltau(batch_labels, batch_predictions))
start_range = end_range
#self.y_true = self.y_true[0:len(ranked_predictions)]
print('\nORIGINAL LABELS: {0}\n'.format(self.y_true))
print('PREDICTED LABELS: {0}'.format(ranked_predictions))
print("\nEpoch Kendall's tau: {0}".format(np.mean(kendalls)))
model = tf.keras.Sequential()
model.add(LSTM(units=10, batch_input_shape=(None, 2, 839)))
model.add(Dense(15, activation='sigmoid'))
model.summary()
model.compile(loss=listnet_loss, optimizer=keras.optimizers.Nadam(learning_rate=0.000005, beta_1=0.9, beta_2=0.999))
real_labels = np.array([])
losses = np.array([])
with tf.device('/GPU:0'):
model.fit(training_dataset, training_dataset_labels, epochs=10, workers=10,
verbose=1, callbacks=[KendallTauHistory(training_dataset, training_dataset_labels, groups_id_count)])