Я новичок в Keras и пытаюсь построить рекуррентную нейронную сеть для классификации аудиофайлов.
Во время обучения я получаю InvalidArgumentError: indices[28,0] = -711 is not in [0, 20000).
Я обнаружилВ разных темах говорилось об этой ошибке, но, честно говоря, я не понимал, что мне нужно изменить в параметрах, которые я передаю в сеть, чтобы помочь ей управлять отрицательными значениями, которые у меня есть в массиве обучения.
Ниже кода:
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional
max_features = 20000
maxlen = 40
batch_size = 32
model = Sequential()
model.add(Embedding(max_features, 128, input_length=maxlen))
model.add(Bidirectional(LSTM(64)))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
# try using different optimizers and different optimizer configs
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
print('Train...')
model.fit(X_train, y_train,
batch_size=batch_size,
epochs=4,
validation_data=[X_test, y_test])
Ошибка ниже:
Train...
Train on 964 samples, validate on 476 samples
Epoch 1/4
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-22-3452d23cb8b5> in <module>()
12 batch_size=batch_size,
13 epochs=4,
---> 14 validation_data=[X_test, y_test])
/usr/local/lib/python3.6/dist-packages/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, **kwargs)
1037 initial_epoch=initial_epoch,
1038 steps_per_epoch=steps_per_epoch,
-> 1039 validation_steps=validation_steps)
1040
1041 def evaluate(self, x=None, y=None,
/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
197 ins_batch[i] = ins_batch[i].toarray()
198
--> 199 outs = f(ins_batch)
200 outs = to_list(outs)
201 for l, o in zip(out_labels, outs):
/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
2713 return self._legacy_call(inputs)
2714
-> 2715 return self._call(inputs)
2716 else:
2717 if py_any(is_tensor(x) for x in inputs):
/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in _call(self, inputs)
2673 fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
2674 else:
-> 2675 fetched = self._callable_fn(*array_vals)
2676 return fetched[:len(self.outputs)]
2677
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
1437 ret = tf_session.TF_SessionRunCallable(
1438 self._session._session, self._handle, args, status,
-> 1439 run_metadata_ptr)
1440 if run_metadata:
1441 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
526 None, None,
527 compat.as_text(c_api.TF_Message(self.status.status)),
--> 528 c_api.TF_GetCode(self.status.status))
529 # Delete the underlying status object from memory otherwise it stays alive
530 # as there is a reference to status from this from the traceback due to
InvalidArgumentError: indices[28,0] = -711 is not in [0, 20000)
[[{{node embedding_3/embedding_lookup}} = GatherV2[Taxis=DT_INT32, Tindices=DT_INT32, Tparams=DT_FLOAT, _class=["loc:@training_1/Adam/Assign_2"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](embedding_3/embeddings/read, embedding_3/Cast, training_1/Adam/gradients/embedding_3/embedding_lookup_grad/concat/axis)]]
EDIT1 : X_train - это массив float32 ниже
array([[-5.79938449e+02, 6.63875936e+01, -6.75054944e+00, ...,
-2.89458464e+00, -2.30009868e+00, -2.34216322e+00],
[-3.38924973e+02, 1.60668197e+01, -5.39871140e+01, ...,
1.27180395e+00, 4.28090614e+00, 2.01538667e+00],
[-5.53199739e+02, 3.45314936e+01, -1.68711443e+01, ...,
-9.47345310e-02, -1.04780706e-02, 1.69060756e-01],
...,
[-5.91902354e+02, 6.14329122e+01, 1.43761675e+00, ...,
-4.38644438e+00, -3.67977820e+00, -1.89899207e+00],
[-7.04889969e+02, 6.24931510e+01, 1.90338300e+01, ...,
-1.47540089e+00, -1.75498741e+00, -4.55713837e-01],
[-8.24296641e+02, 7.43124586e+01, 1.43319513e+01, ...,
-7.60749297e-01, -1.05324700e+00, -8.54044186e-01]])