我是Keras的新手，我正在尝试建立一个递归神经网络来对音频文件进行分类。
在培训期间，我收到了InvalidArgumentError: indices[28,0] = -711 is not in [0, 20000)。
我找到了很多有关此错误的话题，但老实说，我不理解我要传递给网络的参数必须进行哪些更改才能帮助它管理培训中的负值数组。
下面的代码：
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])
以下错误：
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是下面的float32数组
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]])